FINTECH

FINTECH, short for Financial Technology, describes the emerging technology applied to financial services including trading, investments, banking, and marketplaces including current exchanges and new alternative marketplaces. FINTECH covers innovations that are rapidly changing all aspects of the front office, middle office, and back office. CloudQuant is considered a FINTECH company as we are bringing crowdsourcing to algorithmic trading. Other FINTECH innovations include cryptocurrencies, machine learning, and deep learning applications.

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AI & Machine Learning News. 22, October 2018

 “As long as we work together—with both urgency and determination—there are no limits to what we can achieve.” – Paul Allen

10 Times Tech Legend Paul Allen Spoke About Artificial Intelligence

The co-founder of Microsoft, Paul Allen’s tryst with personal computers is not unknown. Allen, in his early years played a pivotal role at Microsoft, steering the company’s fortunes and making it a dominant player as an enterprise software. In the second half of his career, Allen put all his might behind advancing AI. Quoting on how computers are easier to understand than human brain, he once famously said that computers are basically computing elements and are easy to understand as compared to human brain. After parting ways with Microsoft, he kick-started many ventures of which Allen Institute of Artificial Intelligence (AI2) is one of the most ambitious. He was quite driven into his dual efforts to firstly, reverse-engineer the human brain and secondly, build one from scratch through artificial intelligence. “When I founded AI2, I wanted to expand the capabilities of artificial intelligence through high-impact research,” he had said. Envisioning a world of perfect AI, aligned with human values, he invested considerable resources in advancing the developments in artificial intelligence through AI2. While his soul might have departed, he continues to inspire us and has given a lot of hope to AI visionaries to keep up with the innovations in this field. Paying a tribute, here we list 10 times Paul Allen quoted artificial intelligence and technology. 2018-10-18 11:03:09+00:00 Read the full story.

Paul G. Allen, 1953-2018: Microsoft co-founder leaves legacy of innovation, philanthropy, bold bets

Paul G. Allen, who founded Microsoft with Bill Gates before making his mark in technology investing, sports ownership, commercial space, global philanthropy, the environment, museums and the arts, has died at the age of 65, two weeks after announcing that he was diagnosed with a recurrence of non-Hodgkin’s lymphoma, according to a statement Monday from his company Vulcan. “Personal computing would not have existed without him,” Gates said in a statement, calling Allen a “true partner and dear friend.” ‘His legacy will live on forever’: Death of Paul Allen brings reaction from the many worlds he touched Allen’s life and work were remarkable for their depth and breadth, spanning from the formative years of the personal computer in the 1970s to the modern world of 21st century wireless technologies, artificial intelligence, cutting-edge brain science and the promise of space exploration. Allen remained enthralled with the potential of technology throughout his life. “It really is a golden age of what’s possible,” Allen said in an interview with GeekWire last year, after he made a $40 million gift to the University of Washington’s computer science and engineering program, joining with Microsoft to create a $50 million endowment for what was rechristened the Paul G. Allen School of Computer Science & Engineering. 2018-10-15 22:09:19-07:00 Read the full story. Insights from Paul Allen CloudQuant Thoughts… Surprisingly little in our feeds on the passing of a man so influential in the history of computing. A huge proponent and supporter of AI (he founded the AI2 research institute in Seattle), he will be sorely missed.  

Future of sports viewing? Steve Ballmer and L.A. Clippers debut new augmented reality NBA experience

Steve Ballmer has launched plenty of products in high-stakes situations, but this takes it to a new level. His NBA team, the L.A. Clippers, is rolling out a new augmented reality viewing experience for basketball fans on opening night for the 2018-19 NBA season at Staples Center. The new experience, Clippers CourtVision, uses computer vision, artificial intelligence and augmented reality to analyze the action on the court and translate it into on-screen annotations and animations, displayed on screen as the game unfolds. Viewers can see the probability that a player will make a shot, for example, or watch as the play is diagrammed in real time on the basketball court. Ballmer and the company that developed the technology, Second Spectrum, believe it could be the first step toward a radically different viewing experience for professional sports in the future. Ballmer is an investor in Second Spectrum, and has championed the technology among his fellow NBA owners. 2018-10-18 00:00:13-07:00 Read the full story.

‘CourtVision’ Review: We tested Steve Ballmer’s attempt to transform the NBA viewing experience

Augmented reality on sports broadcasts isn’t new. In fact, there’s a gold standard: the first-down line for football games, invented two decades ago. Imagine the chaos and confusion in homes and sports bars across the country if they took that away. More recent additions to the genre include the virtual baseball strike zone, and the digital arc tracing the flight of a golf ball. So what about basketball? Can augmented reality provide anything so indispensable that NBA fans would howl in protest if it were taken away? Not yet. But the technology is impressive, and the potential is there. That’s my takeaway after trying out Clippers CourtVision for two games. The experience was unveiled this week by former Microsoft CEO Steve Ballmer and his NBA team, the L.A. Clippers. Developed by technology company Second Spectrum with Ballmer’s backing and encouragement, CourtVision uses computer vision to see what’s happening on court, artificial intelligence to understand the game, and augmented reality to display animations and data on screen. The stream is currently delayed by two minutes from the live action, to allow time for the required processing, but that’s down significantly from earlier testing, and Ballmer and Second Spectrum say the time gap will get even shorter over time. 2018-10-21 17:21:28-07:00 Read the full story. CloudQuant Thoughts… First video games started to look more like real life basketball, now real life basketball starts to look more like a video game. I am not sure where we are headed here!  

FAANG Report

Apple throws privacy ‘shade’ at rivals; Amazon hit for anti-union video at Whole Foods; Facebook shows off election ‘war room’ to fight election manipulation; Google introduces privacy tweak on Chrome after privacy gripes and is hit by a major data breach and did not report it over regulation fears ; and Netflix subscriber growth expanding at incredible pace. 2018-10-18 00:00:00 Read the full story. CloudQuant Thoughts… In case you are not aware FAANG refers to the Five major stocks in the US Equities Market (FB, AAPL, AMZN, NFLX, GOOG/GOOGL). Last year many analysts stated that, if you removed these Five companies from the market then it was basically stagnant, did not move at all. The suggestion was that the entire upwards trend of recent years was down to these Five companies. As a Data Scientist, can you use this observation as a jumping off point to create a model at CloudQuant?  

US driverless cars unsafe as they can’t spot iconic British vehicles like the Routemaster bus and Hackney cab, experts warn

US driverless cars pose a safety risk on the UK’s streets as they can’t spot iconic British vehicles, such as London’s red buses and black cabs, as their artificial intelligence has not been taught to notice them on the roads, experts claim. Engineers have noticed that the autonomous cars made in Silicon Valley are currently using cameras and software that have only been trained on pictures and videos of US vehicles, meaning they won’t detect unique vehicles with more district styles, such as the Routemaster bus and Hackney taxi. This has led UK scientists and politicians to raise questions over companies like Google and Uber, who have been developing their own autonomous technology, being able to test driverless cars in Britain. 2018-10-20 00:00:00 Read the full story. CloudQuant Thoughts… Whoops! Slight oversight there!  

CrowdStrike Hires Goldman Sachs To Lead IPO

NEW YORK/SAN FRANCISCO – Cybersecurity software maker CrowdStrike Inc has hired investment bank Goldman Sachs Group to prepare for an initial public offering that could come in the first half of next year, people familiar with the matter said on Friday. CrowdStrike uses artificial intelligence for its Falcon platform to prevent attacks on computers on or off the network. CrowdStrike is trying to stand out from the hundreds of security startups that have sprouted in recent years, promising next-generation technologies to fight cyber criminals, government spies and hacker activists, who have plagued some of the world’s biggest corporations. 2018-10-21 01:59:20-04:00 Read the full story. CloudQuant Thoughts… IPOs (Initial Public Offerings) are a well know source of Volatility and where there is volatility there is profit. The interesting thing from a Data Scientist’s point of view is that there is a distinct lack of data. This is one of those situations where you are literally learning and making decisions live, starting today, IPO day. Can you write a model on CloudQuant to find the best IPOs algorithmically? Work out the best number of days to hold?  

Must Read Books on Machine Learning Artificial Intelligence

In this article, we’ve listed some of the must-read books on Machine Learning and Artificial Intelligence. These books are in no particular rank or order. The motive of this article is not to promote any particular book, but to make you aware of a world which exists beyond video tutorials, blogs and podcasts. Machine Learning Yearning – Andrew Ng Programming Collective Intelligence Machine Learning for Hackers Drew Conway and John Myles White Machine Learning by Tom M Mitchell The Elements of Statistical Learning – Trevor Hastie, Robert Tibshirani and Jerome Friedman Learning from Data – Yaser Abu Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin Pattern Recognition and Machine Learning – Christopher M Bishop Natural Language Processing with Python – Steven Bird, Ewan Klein, and Edward Loper Artificial Intelligence: A Modern Approach – Stuart Russell and Peter Norvig Artificial Intelligence for Humans – Jeff Heaton Paradigm of Artificial Intelligence Programming – Peter Norvig Artificial Intelligence: A New Synthesis – Nils J Nilsson Superintelligence – Nick Bostrom The Singularity is Near – Ray Kurzweil Life 30 – Being Human in the Age of Artificial Intelligenc – Max Tegmark The Master Algorithm – Pedro Domingos 2018-10-17 19:00:29+05:30 Read the full story.  

Doctor who? UK patients signal support for AI surgeons

A third of patients in the UK are willing to have major invasive surgery performed by AI, according to research conducted by YouGov for PwC. The research found that young people and men are most supportive, with nearly half of 18 to 24-year-olds and 39% of men willing to go under a knife wielded by AI. Perhaps unexpectedly it is old people who are the most sceptical. Only 24% of over 55s are open to the idea. As the benefits of AI-powered healthcare become more and more evident, it is only a matter of time before AI infuses into the healthcare system. AI promises to help to alleviate waiting times and surgery delays that currently plague operations, free up staff and resources, and cut costs. But it is no exaggeration to say that patients value the relationship they have with medical professionals more than in any other context, preferring to have sensitive consultations and life-changing decisions made by real-life humans. Even if AI interfaces don’t develop a believable ‘human touch’ – and there is no reason to suspect that this won’t happen sooner or later – having treatment decisions made by a cold chunk of metal and code might be a pill worth swallowing if it means your healthcare is smarter and fast-tracked. 2018-10-18 00:00:00 Read the full story.  

Rolls-Royce Partners With Intel For The Next Generation Of Self-Piloting Ships

With all the developments that are occurring involving autonomous vehicles, it’s easy to imagine a future in which commuters sit back, relax, and are shuttled to their destinations by self-driving cars. Continuing developments in artificial intelligence (AI) and advances in computer vision are coming together to make this futuristic dream a reality. Yet the technology won’t be limited to the open road. Rolls-Royce (NASDAQOTH:RYCEY) develops power and propulsion systems for a wide range of transportation assets including aircraft and ocean vessels. As a key player in the international shipping industry, the company is joining forces with Intel (NASDAQ:INTC) to advance its plans to make self-piloting ships a reality. The company already uses AI to create intelligent systems to make commercial shipping safer, but now Rolls-Royce is collaborating with Intel to process the massive amount of data necessary by turning these ships into floating data centers. Rolls-Royce’s Intelligent Awareness System (IAS) uses sensor fusion and enhanced decision making that combines data from LIDAR, radar, thermal cameras, HD cameras, satellite data, and weather forecasts. Each vessel can capture up to 1 terabyte (or approximately 1 trillion bytes) of data per day — even when the data is compressed. 2018-10-16 01:41:02-04:00 Read the full story.  

Speech Recognition Challenge with Deep Learning Studio

We might be on the verge of too many screens. It seems like everyday, new versions of common objects are “re-invented” with built-in wifi and bright touch screens. A promising antidote to our screen addiction are voice interfaces. Talking more voice interface Speech recognition technology has become an increasingly popular concept in recent years. From organizations to individuals, the technology is widely used for various advantages it provides. One of the most notable advantages of speech recognition technology includes the dictation ability it provides. With the help of the technology users can easily control devices and create documents by speaking. Speech recognition can allow documents to be created faster because the software generally produces words as fast as they are spoken, which is generally much faster than a person can type. No longer will spelling or writing hold you back. Voice recognition software, as well as being faster to complete tasks, is increasingly accurate when it comes to vocabulary and spelling. For most of us, the ultimate luxury would be an assistant who always listens for your call, anticipates your every need, and takes action when necessary. That luxury is now available thanks to artificial intelligence assistants, aka voice assistants. Voice assistants come in somewhat small packages and can perform a variety of actions after hearing a wake word or command. They can turn on lights, answer questions, play music, place online orders, etc. But, for independent makers and entrepreneurs, it’s hard to build a simple speech detector using free, open data and code. Many voice recognition datasets require preprocessing before a neural network model can be built on them. To help with this, TensorFlow had released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. 2018-10-21 22:16:13.912000+00:00 Read the full story.  

IBM Snags $240 Million AI Deal

International Business Machines (NYSE:IBM) exited the PC business in 2005 by selling its PC division to Lenovo (NASDAQOTH:LNVGY). That deal began a relationship between the two companies, and Lenovo has since become the No. 1 seller of PCs worldwide. Lenovo is now looking to make its commercial PC business more efficient, and it’s turning to IBM’s artificial intelligence technology for help. IBM announced a multiyear deal with Lenovo on Thursday that aims to use AI to reduce customer service and field service costs. The $240 million pact, covering North America, Europe, the Middle East, Africa, and Latin America, is a win for IBM’s technology support services business. 2018-10-19 02:13:14-04:00 Read the full story.  

IBM opening up a series of AI, cloud and security initiatives

Positioning itself as an open vendor, IBM on Oct. 15 announced its new AI OpenScale, MultiCloud Manager and Security Connect platforms. AI OpenScale enables organizations to use multiple artificial intelligence frameworks including TensorFlow, AWS SageMaker, AzureML and others on the IBM cloud. It also has a new system that will use AI to build new AI models. The new IBM MultiCloud Manager is a service designed to help organizations automate and manage workloads across different cloud providers. Finally, the IBM Security Connect offering provides an open cloud-based platform for integration of multiple security tools. 2018-10-17 00:00:00 Read the full story.  

Real-time Data Processing with Serverless Computing

In the Big Data landscape, data engineers are always striving to come up with efficient and accurate methods to compute volume, variety and velocity of data so that it can prove to be a strong skeleton for Data Scientists who are conducting their analysis. This is necessary since new age technologies like Machine Learning and Artificial Intelligence heavily depends on Big Data as mention in the recent Forbes post. However, Big Data deployment in the Cloud came up with a lot of difficulties such as under, improper and over utilization of compute resources at various periods. To abstract away these problems, Serverless Architecture came to the rescue. Netflix, Mapbox and New York Times are some of the prominent organizations using Serverless Architecture and unleashing their potential in Real-time Data Processing. 2018-10-19 00:35:03-07:00 Read the full story.  

AI to reshape rather than replace finance workforce

The job market within the financial service sector is set for a significant shift as the revolution in artificial intelligence (AI) reshapes processes and roles, a panel told its audience at this year’s Money20/20. For Gregory Simpson, senior vice president and chief technology officer at Synchrony, while the workforce will change there may not be a widescale loss of opportunities in the marketplace. “We’ve taken a very conscious approach about how to reskill people, because there will be different types of jobs,” he said. “We talk about AI as augmented intelligence versus artificial intelligence – and how augmentation can change jobs, and how prepared people,are to fill those roles. There will be some jobs that need replaced, and in some cases there will be a displacement of other types of automation.” “In some cases it’s creating new jobs with people needed to build that automation. Our audit team is talking about hiring data scientists rather than auditors, for instance,” he added. 2018-10-21 00:00:00 Read the full story.  

This CEO is paying 600,000 strangers to help him build human-powered AI that’s ‘whole orders of magnitude better than Google’

One of the worst-kept secrets in Silicon Valley is that it takes a whole lot of human labor to make artificial intelligence…intelligent. The best example: When Google’s reCAPTCHA pages ask you to identify street signs or storefronts in photos before you can log in, you’re proving you’re not a robot, sure. You’re also providing valuable, human insight into what a street sign looks like, which is extremely useful data when you’re trying to train a self-driving car, or a smart security camera. The whole concept was memorably lampooned in an episode last year of HBO’s “Silicon Valley.” Hive pays 600,000 workers and counting to label photos, getting paid pennies in return. You won’t get rich, but as Hive CEO Guo says, it’s a simple “game” that makes you money — what other app on your phone can do that? The data gets put to use in training AI systems, at a scale that Guo says is unmatched. Hive is a Silicon Valley startup that’s best known for its AI-powered image recognition system, with customers including NASCAR. 2018-10-21 00:00:00 Read the full story.  

The ultimate guide to starting AI – Part 2

Many teams try to start an applied AI project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that’s like raising a puppy in a New York City apartment for a few years, then being surprised that it can’t herd sheep for you. You can’t expect to get anything useful by asking wizards to sprinkle machine learning magic on your business without some effort from you first. Instead, the first step is for the owner — that’s you! — to form a clear vision of what you want from your dog (or ML/AI system) and how you’ll know you’ve trained it successfully. My previous article discussed the why, now it’s time to dive into how to do this first step for ML/AI, with all its gory little sub-steps. This reference guide is densely-packed and long, so feel free to stick to large fonts and headings for a two-minute crash course. Here’s the table of contents:
  • Figure out who’s in charge
  • Identify the use case
  • Do some reality checks
  • Craft a performance metric wisely
  • Set testing criteria to overcome human biases
2018-10-19 15:42:21.123000+00:00 Read the full story.  

Concise Cheat Sheets for Machine Learning with Python (and Maths)

Machine learning is difficult for beginners. As well as libraries for Machine Learning in python are difficult to understand. Over the past few weeks, I have been collecting Machine Learning cheat sheets from different sources and to make things more interesting and give context, I added excerpts for each major topic. If you are just getting started with Machine Learning or Data Science, you’ll richly benefit from resources compiled from our recent publications; An Overview of Best Machine Learning Cheat Sheets…
  1. Scikit-Learn Cheat Sheet: Python Machine Learning
  2. Python Cheat Sheet for Scikit-learn
  3. Keras Cheat Sheet: Neural Networks in Python
  4. Python SciPy Cheat Sheet
  5. Theano Cheat Sheet
2018-10-22 00:00:08+00:00 Read the full story.  

Stop Making These Five Simple Mistakes in Big Data Analytics

Data Scientists, using their formidable skills in math, statistics, and programming, dig out an enormous mass of messy data then clean, manage, and organize it. They use their analytic powers that include industry knowledge, contextual understanding, and skepticism of existing assumptions to help businesses unearth hidden solutions to complex challenges. But nobody is perfect, mistakes happen, though that can in fact be a good thing. It is rightly quoted by the famous Irish novelist, James Joyce that, “Mistakes are the portals of discovery.” For Data Scientists, mistakes help them become sharper and discover new data trends, but that doesn’t mean mistakes in Big Data Analytics are not sometimes quite problematic. Although Data Scientists rarely commit egregious mistakes, since they are hired with precision, some newbies in this field can make them, which we are going to discuss here. So let’s see what common mistakes Data Analysts/Scientists make and how to avoid them. Mistake 1: Going Overboard on Tools Mistake 2: Not Being an Explorer and Visualizer Mistake 3: Ignoring Possibilities Mistake 4: Solving Problems Randomly Mistake 5: Fearing to Communicate and Compete 2018-10-18 00:30:56-07:00 Read the full story.  

What is a Decision Tree in Machine Learning? – Hacker Noon

Decision trees, one of the simplest and yet most useful Machine Learning structures. Decision trees, as the name implies, are trees of decisions. You have a question, usually a yes or no (binary; 2 options) question with two branches (yes and no) leading out of the tree. You can get more options than 2, but for this article, we’re only using 2 options. Trees are weird in computer science. Instead of growing from a root upwards, they grow downwards. Think of it as an upside down tree. The top-most item, in this example, “Am I hungry?” is called the root. It’s where everything starts from. Branches are what we call each line. A leaf is everything that isn’t the root or a branch. Trees are important in machine learning as not only do they let us visualise an algorithm, but they are a type of machine learning. 2018-10-22 Read the full story.  

National Australia Bank trials AI-powered facial recognition ATMs

The move is part of a push across the bank to use the latest cloud-based systems and will use Microsoft’s Azure platform, along with its cognitive services AI software as it evaluates whether customers like the idea of cardless banking. The proof-of-concept ATMs are being demonstrated at this week’s Sibos conference in Sydney, and will only be introduced to the wider public if the bank is satisfied it can address any privacy and security concerns about the use of customer biometrics. The ATMs will recognise a customer’s face and then require them to add their PIN number to complete transactions. 2018-10-18 00:00:00 Read the full story.  

Predicting Hospital Readmission for Patients with Diabetes Using Scikit-Learn

As the healthcare system moves toward value-based care, CMS has created many programs to improve the quality of care of patients. One of these programs is called the Hospital Readmission Reduction Program (HRRP), which reduces reimbursement to hospitals with above average readmissions. For those hospitals which are currently penalized under this program, one solution is to create interventions to provide additional assistance to patients with increased risk of readmission. But how do we identify these patients? We can use predictive modeling from data science to help prioritize patients. One patient population that is at increased risk of hospitalization and readmission is that of diabetes. Diabetes is a medical condition that affects approximately 1 in 10 patients in the United States. According to Ostling et al, patients with diabetes have almost double the chance of being hospitalized than the general population (Ostling et al 2017). Therefore, in this article, I will focus on predicting hospital readmission for patients with diabetes. In this project I will demonstrate how to build a model predicting readmission in Python using the following steps
  • data exploration
  • feature engineering
  • building training/validation/test samples
  • model selection
  • model evaluation
2018-10-21 21:04:34.462000+00:00 Read the full story.  

Deloitte: 42% of executives believe AI will be of ‘critical importance’ within 2 years

“Companies are excited about the potential of AI to improve performance and competitiveness — and for good reason,” said Dr. Jeff Loucks, executive director at Deloitte’s Center for Technology, Media, and Telecommunications. “But to reach this potential, companies must engage risk, address talent shortfalls, and execute well. While AI’s upside is significant, haste can leave companies with bridges to nowhere — pilots that don’t scale or projects with no business benefit.” The report breaks out AI adoption into four categories: machine learning, or the ability of statistical models to develop capabilities and autonomously improve their performance over time; deep learning, a form of machine learning involving neural networks; natural language processing, the ability to parse meaning from text; and computer vision, techniques that extract intent out of visual elements. Natural language processing outstripped all other categories in terms of growth, according to the survey, with 62 percent of companies reporting having adopted it (up from 53 percent a year ago). Machine learning came in second with 58 percent (up 5 percent year-over-year), and computer vision and deep learning followed close behind, with 57 percent and 50 percent adoption, respectively (a 16 percent increase from 2017). 2018-10-21 00:00:00 Read the full story.  

A Pioneering Scientist Explains Deep Learning

Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies. Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence. The Verge spoke to Sejnkowski about how “deep learning” suddenly became everywhere, what it can and cannot do, and the problem of hype. 2018-10-18 13:07:14+00:00 Read the full story.  

Crux Raises $20 mln in Series B Funding Round • Integrity Research

Yesterday, alternative data engineering firm, Crux Informatics announced that it had closed a $20 mln Series B funding round. Investors in the latest financing round include quantitative hedge fund Two Sigma, plus previous investors Goldman Sachs and Citigroup. Founded in 2017, Crux Informatics provides outsourced data engineering to clean, normalize and transform raw data into formats specific to each client, as well as offering investors an online portal of third-party data sources. Crux Informatics CEO, Philip Brittan explained the strategic rationale for the latest investment round, “We have built a solution for what has become a significant pain point for financial services firms – ingesting and managing the tremendous amount of data that is now available to them. We are transforming the data industry making it possible for our clients to reap the benefits from improved data flow, which translates into more actionable insights and alpha. This funding will help us continue to drive innovation, which will scale our business and the data set solutions we offer to our clients.” 2018-10-18 15:44:17+00:00 Read the full story.  

Cloudera Announces Strategic Alliance with NEC to Support the Utilization of Big Data

A recent press release reports, “Cloudera, Inc., the modern platform for machine learning and analytics optimized for the cloud, announced a strategic partnership with NEC Corporation, a leading global systems integrator, to accelerate the use of big data. Through this strategic partnership, customers will be able to obtain NEC’s advanced technical, consulting and support services, and access to Cloudera’s data management software, Cloudera Enterprise. Businesses today are turning data into a competitive advantage. They are finding that the key to success is to take control of their data and the infrastructure needed to unlock the data value and derive insights to make better, smarter and fact-based decisions. To do so, having a well-structured and centralized data repository is critical. This alliance will provide organizations the ability to integrate and centralize data across different business units through Cloudera’s modern, scalable and secure platform, enabling them to solve many complex and important problems through the application of machine learning and artificial intelligence.” 2018-10-22 00:15:43-07:00 Read the full story.  

The 5 Basic Statistics Concepts Data Scientists Need to Know

Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
  • Statistical Features
  • Probability Distributions
  • Dimensionality Reduction
  • Over and Under Sampling
  • Bayesian Statistics
2018-10-22 02:17:34.099000+00:00 Read the full story.  

Deutsche Bank steps up Big Data efforts with new analytics capability for Securities Services

Deutsche Bank is launching its enterprise analytics capability, which collates and analyses millions of lines of data daily to identify opportunities for efficiencies in the bank’s and its clients’ securities settlements. Deutsche Bank’s Securities Services will be bringing live its new enterprise analytics capability in November 2018, enabling daily data analysis on securities transactions. Starting with the German market and with a wider roll-out planned, the platform represents a key step towards unlocking the value of Deutsche Bank’s huge repository of transaction data, which details the holdings and movements of cash, securities and other instruments in and out of client accounts. 2018-10-22 12:19:00 Read the full story.  

Alternative Data Researcher M Science Enhances Flagship Video Game Data • Integrity Research

M Science, the alternative data research provider originally known as Majestic Research, added video game viewership data from Twitch to its recently released analytic platform, M Data Viz, further strengthening the firm’s video game capabilities.. M Science has expanded its analytic coverage by over 20% over the last year, primarily in its coverage of technology and European consumer stocks. As of June 2018, the firm had over 30 research staff registered on LinkedIn, the majority of which (21) were based in Portland Oregon. The firm is currently recruiting for senior analysts in the healthcare, industrials and fintech sectors. In July 2018, M Science launched an improved version of its transaction data, called SWIPE, which uses machine learning to calculate five metrics derived from transaction data – aggregate spending at a company level, unique customers, number of transactions per shopper, average ticket, and a proprietary estimate of a company’s revenue (labelled the ‘M Science Index’). The firm currently provides revenue estimates for fifty stocks. 2018-10-19 13:55:46+00:00 Read the full story.  

Link data to outcomes to avoid a data deluge, say Hitachi Vantara Australia CTO

Digital transformation projects are failing in Australia because organisations haven’t linked their data strategy to business outcomes, according to Chris Drieberg, director of pre-sales and CTO for Hitachi Vantara ANZ. Worse still, Drieberg says, the digital infrastructure being built today may prove fundamentally inadequate very soon. “Most of [our customers] that we deal with today are building a data warehouse strategy and they’ll only look at a data lake strategy when the business is impacted,” 018-10-18 13:46:23+11:00 Read the full story.  

It’s all about the data

Just continuing on the theme of how different industries can learn from each other, I used to work for NCR. NCR had several major industries they served: retailers, airlines, telcos and banks. The common thread across all of these industries was high frequency customer contact, and the challenge for all of these industries was how to leverage that contact. The highest frequency contact was in banking and telecommunications. Sure, retailers and airlines see customers often, but banks and telecommunications firms touch customers every day. Back in the 1990s, when I was at NCR, I made a prediction as a result. The prediction was that one day banks would become telecoms firms and telecoms firms would become banks. Today, that prediction is part-true. In some countries, banks are running mobile networks and, in other countries, mobile networks are running banks. But what happens longer term, if that prediction plays out? 2018-10-16 06:52:10+00:00 Read the full story.  

NICE Actimize Launches X-Sight, the Industry’s First Financial Crime Risk Management PaaS

According to a recent press release, “NICE Actimize, a NICE business and leader in Autonomous Financial Crime Management, today announced the launch of X-Sight, an advanced machine-learning based Platform-as-a-Service designed to power the industry’s first financial crime risk management marketplace. The NICE Actimize X-Sight Platform-as-a-Service offers a single, unified, cost-effective way for financial service organizations to rapidly innovate and to introduce new services while supporting best-in-class financial crime, risk and compliance management capabilities.” 2018-10-22 00:10:19-07:00 Read the full story.  

SEC Launches FinHub Portal

Thursday marked the launch of the Securities and Exchange Commission’s FinHub, which will serve as the agency’s “strategic hub for innovation and technology.” It is accessible to the public, and also will serve as an exchange of ideas for those in the industry and other regulators. The hub covers four topical areas: 1) Blockchain/distributed ledger, 2) Digital marketplace financing, 3) Automated investment advice, and 4) Artificial intelligence/machine learning. Under each topic there are links to current regulation, investor information and various speeches and presentations. For example, under the Digital Marketplace Financing category is a regulation bar that when clicked on will provide information on the SEC’s crowdfunding final rule as well as a compliance details. 2018-10-18 Read the full story (@ ThinkAdvisor). 2018-10-22  Read the full story (@ TheIndustrySpread).  

Will Alphabet Earnings Crush Expectations Again? — The Motley Fool

Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) is slated to report its third-quarter 2018 results after the market close on Thursday, Oct. 25. Google’s parent company is going into its report on a solid note. In the second quarter, revenue increased 26% year over year and earnings per share adjusted for one-time factors jumped 32% to $11.75, trouncing the $9.59 Wall Street was looking for. On a GAAP basis, however, EPS declined 9.4%, driven by a 4.34 bil… 2018-10-22 00:00:00 Read the full story.  

Database Trends and Applications Magazine: October/November 2018

The Journal of Information Integration and Management – Volume 32, Number 5 • October/November 2018
  • Content Automation Emerges in Cluttered Enterprises
  • Hybrid Clouds Fulfill a Wide Range of Data Enterprise Needs
  • How to Transform Your Back-End Infrastructure into a Platform of the Future
  • Controlling Costs in the Cloud for High-Availability Applications
  • Using Data Lakes to Fuel Self-Service Analytics
  • Revelation Software OfferS Primer on OpenInsight 10
  • Pick Cloud and 3phi Partner to Sell Reporting Tools Based on PICK/MultiValue Databases
  • Kore Technologies’ Newest Kourier Update Enhances Cloud Capabilities
  • NEXT-GEN DATA MANAGEMENT Dangers of Statistical Modeling
  • EMERGING TECHNOLOGIES Blockchain Enables Unique Digital Proofs
  • IOUG OBSERVATIONS Considerations Before Converting from an RDBMS to Cloud-Native Services
  • DBA Corner The Mainframe Does Big Data
  • SQL Server Drill Down Fly to the Cloud Using the Azure Database Migration Service (DMS)
  • Database Elaborations Should PII and GDPR Drive Database Design
2018-10-19 00:00:00 Read the full story.  

Data Centre World 2018: Water scarcity could halt data centre growth

At last week’s Data Centre World in Singapore, the largest industry event in Asia, water technology provider Ecolab discussed sustainable solutions to address the freshwater crisis and examples of how data centres have successfully reduced water consumption. Demand for natural resources is exceeding supply at an alarming rate, not least when it comes to our most fundamental resource – water. By 2030, global demand for fresh water is expected to exceed available supplies by 40 per cent. Running parallel to our increasing water consumption is the mushrooming of data centres all over the world, that rely on large volumes of water to cool their racks and servers. 2018-10-16 00:00:00 Read the full story.  

Is Hadoop Officially Dead?

The merger of Cloudera and Hortonworks was applauded by many people in the big data community, and even Wall Street liked the news initially. But as the confetti from the party clears, some are asking tough questions, like whether the merger signals the death of Hadoop as a viable computer platform moving forward. The answer is probably not. Here’s why. The October 3 announcement that Hortonworks will join forces with its arch-rival Cloudera to create a single company with about $730 million in annual revenue, 2,500 customers, and a $5.2 billion market valuation took a lot of people by surprise. 2018-10-18 00:00:00 Read the full story.  

Top 10 real-life examples of Machine Learning

Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living. But what is machine learning? It is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. Machine learning applications provide results on the basis of past experience. In this article, we will discuss 10 real-life examples of how machine learning is helping in creating better technology to power today’s ideas.
  • Image Recognition
  • Speech Recognition
  • Medical diagnosis
  • Statistical Arbitrage
  • Learning associations
  • Classification
  • Prediction
  • Extraction
  • Regression
  • Financial Services
2018-10-22 Read the full story.  

The business case for machine learning

Machine Learning (ML) is one of the most researched topics in computer science. It’s been around for decades. However, many people consider it just another buzzword, or even worse, confuse it with Artificial Intelligence (AI). However, the two are not the same. Machine Learning is the science of a machine learning and improving without being specifically programed to do so. Artificial Intelligence is the base technology that makes this possible. Think of ML as a subset of AI. It’s important to keep in mind that all Machine Learning is Artificial Intelligence but not all Artificial Intelligence is Machine Learning. More than clarifying the difference between the two, it’s important to understand why Machine Learning has gained so much attention over the past few years. 2018-10-19 00:00:00 Read the full story.  

Here’s the one key advantage Seattle’s tech ecosystem has over Silicon Valley

Silicon Valley may be the startup capital of the world, but Seattle has a leg up when it comes what is arguably the most impactful technology of the future. The age-old Bay Area vs. Seattle tech ecosystem debate came up again during a venture capital panel discussion at the Seattle Interactive Conference on Thursday that included Heather Redman, managing director at Flying Fish Partners; Hope Cochran, venture partner at Madrona Venture Group; and Shauna Causey, partner at Madrona Venture Labs. They gave the usual responses — Seattle is too humble; there isn’t enough capital flowing; it’s cheaper in Seattle and employees are more loyal here. But Redman, a veteran of the Seattle tech scene who launched Flying Fish last year, offered a forward-looking take. She said Seattle not only has more artificial intelligence and machine learning talent than the Bay Area, but its innovation culture is one that lends itself to setting the ethical bar for the industry at large. “This is the place where we can thread the needle on how to get the benefits from those technologies without tripping into some of the really nasty potential side effects of having a lot of machines making ethical choices for us,” she said. 2018-10-19 16:35:01-07:00 Read the full story.  

AI Weekly: For evidence of academic investment in AI, look no further than Pittsburgh

The Massachusetts Institute of Technology announced this week that it would invest $1 billion in a new college of computer engineering: the Stephen A. Schwarzman College of Computing. It’s the single largest investment in artificial intelligence (AI) by a U.S. academic institution to date. And when the new building hosts its first classes in 2022, it’ll be the largest structural addition to MIT’s campus since the 1950s. But MIT isn’t the only university channeling funds toward AI education. Yet another AI-forward institution of note is Carnegie Mellon University (CMU), which partnered with Bosch’s Center for Artificial Intelligence on an $8 million research project that goes through 2023. CMU has the additional distinction of being the first university to offer an undergraduate degree in AI, and it neighbors the ARM Institute, a $250 million initiative focused on accelerating the advancement of transformative robotics technologies and education in the U.S. manufacturing industry. 2018-10-19 00:00:00 Read the full story.  

The Pixel 3’s dual cameras are a tacit admission that AI can’t do everything — yet

Google’s latest flagship smartphones — the Pixel 3 and Pixel 3 XL — are finally shipping to customers, and the reviews are unanimous: The rear camera and dual selfie cams are best in class. But as good as those cameras might be, they’re a bit puzzling — and sort of paradoxical. Allow me to explain. The original Pixel and Pixel XL have two cameras: one front and one rear. The Pixel 2 and Pixel 2 XL have two cameras: one front and one rear. And the Pixel 3 and Pixel 3 XL have three cameras: two front and one rear. “The notion of a software-defined camera or computational photography camera is a very promising direction and I think we’re just beginning to scratch the surface,” Google AI researcher Marc Levoy told The Verge in October 2016, shortly after the Pixel and Pixel XL’s debut. “I think the excitement is actually just starting in this area, as we move away from single-shot hardware-dominated photography to this new area of software-defined computational photography.” In the weeks preceding the launch of the Pixel 2 and Pixel 2 XL, Mario Queiroz — Google’s GM and VP of phones — insisted that the phones’ single front and rear cameras were just as capable as dual-sensor setups from the likes of Apple and Huawei. The Mountain View company even considered bragging about needing one camera in its marketing materials, according to 9to5Google. I’ve been trying to reconcile the inconsistency since the Pixel 3 was announced last week. Is the addition of a second front camera a tacit admission that AI can’t do everything? That a physical camera is superior? That a hybrid approach might be best? 2018-10-19 00:00:00 Read the full story.  

Anki Vector review: Big on heart, not on smarts

It was late Monday afternoon, and Vector was giving me a headache. The darn thing seemed to be ignoring me. Earlier in the month, I received a package containing Anki’s new Vector, which the company calls a “home robot.” It has generated plenty of enthusiasm — in fact, it blew through its $500,000 Kickstarter goal in just a few days, hitting $1.9 million by the end of the campaign — and began shipping to early backers this month. “It’s truly the first mass-market consumer robot that’s fundamentally powered by the cloud, [and it’s running] the first truly production-grade robotic operating system that third-party developers can use,” Anki cofounder Mark Palatucci told me in a recent phone interview. “We’re leveraging the enormous economies of scale of the modern cloud while maintaining this mass market price. But if my testing is any indication, Vector could use a tad more R&D. 2018-10-21 00:00:00 Read the full story.  

Inside Teradata’s Audacious Plan to Consolidate Analytics

To hear Teradata COO Oliver Ratzesberger explain it, top executives at Fortune 500 firms — and the boards that hold their purse strings — are simply fed up with big analytic investments that haven’t panned out, and they’re turning to Teradata for answers. “The last five to 10 years have been a curse and a blessing,” Ratzesberger tells Datanami in an interview here at Teradata Analytics Universe in Las Vegas, Nevada, where approximately 3,000 Teradata customers, partners, and employees gathered for four-and-a-half days of training, education, and commiserating about failed analytic projects. “There are few executives left who don’t say ‘I’ve spent billions of dollars. I have 1,500 clusters. I have Vertica there, Hana there, Greenplum there. We bought a couple instances of Netezza. But IBM just de-released Netezza, Vertica just got sold a second time, Greenplum is now this open source thing. And Hadoop – well, that is going away.’ – “They’re literally coming to us and saying ‘Give us a proposal to clean up the dozens of instances and consolidate them into one,’” Ratzesberger continues. 2018-10-17 00:00:00 Read the full story.  

Meta Tagging Shoes with Pytorch CNNs – Towards Data Science

Something that I have been wanting to play around with is generating text to describe images. When posed in this way two pathways come to mind. First would be to use a combination of CNNs for feature extraction and feed those extracted features to an LSTM and let it generate descriptions by iterating repeatedly. The second way would be to build a multi-label classification model and have the output nodes represent specific tags. The first model is good in the case you wanted to generate captions for images that have a grammatical structure to them. The multi-label classifier works in situations where there is a finite number of tags that are of interest to generate. This number could be large and as long there is enough data you can train a model in this way. For this particular post I wanted to try and generate meta data tags for pairs of shoes using only raw images as inputs. As for methodology I decided to use a multi-label classification model. My first reason for not using the CNN + LSTM route is that I didn’t explicitly need that English like structure that would be the goal of the LSTM. The second reason is that I felt I would need more data than I was willing to generate by hand to get the model to train nicely for my tailored use case. I hoped that I would be able to leverage pretrained networks to quickly build a model to generate these meta data tags. This turned out to be partially true. 2018-10-21 21:02:26.423000+00:00 Read the full story.  

Analysts Get A Guiding Hand

It can be tough knowing what you can and cannot do in analytics. While it may be possible to build a query that considers a person’s race during a credit check, it is also illegal (not to mention unethical). Now data catalog provider Alation is stepping up by providing more automation to help users conduct analyses in a responsible manner. Earlier this year Alation rolled out a new feature in its Compose query building environment called TrustCheck that gives users instant feedback on whether the query they’re writing is kosher or not. 2018-10-17 00:00:00 Read the full story.  

Contact Centers Have Moved To Cloud — What’s Next?

As of 2018, the market for contact center technology has fully shifted to cloud. The remaining outliers of demand are large, highly customized implementations that preserve legacy on-premises software or organizations that wish to maintain the technology inside their firewall. Agility, flexibility, and the ability to rapidly shift capacity are all key elements that AD&D pros gain when migrating to cloud contact centers. Our recently published report, “The Forrester Wave™: Cloud Contact Centers, Q3 2018,” identified the 11 most significant players in the cloud contact center market: 8×8, Aspect, Avaya, Cisco, Enghouse Interactive, Five9, Genesys, NewVoiceMedia, NICE inContact, Serenova, and Talkdesk. 2018-10-15 11:46:36-04:00 Read the full story.  

Why Microsoft, IBM, Google and Boeing are taking a giant leap into quantum computing

The small world of quantum physics is a big deal on the frontier of computer science. Microsoft CEO Satya Nadella rates quantum computing as one of three key technologies that will shape his company’s future, along with artificial intelligence and mixed reality. Google and NASA are working with D-Wave Systems to blaze a quantum trail. IBM has its Q initiative, and Boeing’s newly formed Disruptive Computing & Networks unit is targeting quantum as well. There’s been a White House summit on quantum information science, and Congress is considering legislation that’d give quantum computing a $1.3 billion boost over the next 10 years. What’s going on? Actually, not a whole lot as of yet. Sure, D-Wave’s computer can take on some specific quantum tasks, but that doesn’t mean a general-purpose, universal computer that takes advantage of all the weird properties of quantum physics is just around the corner. 2018-10-19 01:14:36-07:00 Read the full story.  

Facebook ‘war room’: Teams gather to fight election manipulation

It’s a windowless room, packed with about two dozen desks, a half-dozen screens showing TV news and Twitter feeds and even more monitors lining the walls tracking trends in Facebook user behavior. This is Facebook’s first ever “war room,” designed to prevent election manipulation by improving data-sharing across the company and enabling quick decision-making. This roughly 900-square-foot room, which Facebook recently showed to journalists, is a visual representation of the company’s commitment to dramatically improving communication and security ahead of the Nov. 6 U.S. midterms. Facebook has assembled teams from across the company in a 900-square-foot “war room,” to help identify and stop attempts to manipulate elections, including the Nov. 6 U.S. midterms. WhatsApp, Instagram, operations, software engineering, data science, research operations, legal, policy, communications — they’re all represented in the room. Ahead of this month’s votes in the Brazilian presidential election, the company identified an effort to suppress voter turnout with fake posts saying the election was delayed due to protests. Facebook was quickly able to shut it down. 2018-10-17 00:00:00 Read the full story.

How Facebook is Using War Room to Fight Election Interference

Facebook announced Oct. 18 that the company has set up a war room at its offices in Menlo Park, Calif., to monitor efforts to interfere with national elections in the United States and Brazil. According to the announcement, the war room is staffed by more than two dozen experts from throughout the company, including from their threat intelligence, legal, data science and software engineering teams. The room includes several large monitors on walls around the room and desks for each of the workers. The idea of a war room is to get everyone that’s involved in dealing with a threat or some other type of urgent problem into a single room where they can interact instantly to counteract the problem. The workers in the room can see threats as they appear on dashboards on the monitors, and then can work together to investigate and solve each problem. 2018-10-19 00:00:00 Read the full story.  

Amazon creates 1,000 new UK research roles as tech giants home in on British talent

Amazon is investing in three regional hubs across the UK, creating more than 1,000 new skilled jobs in a move UK trade secretary Liam Fox hailed as a “signal to the world that the UK is very much open for business”. The internet giant will open a new office in Manchester, to house at least 600 new employees working on software development, machine learning and research and development. 2018-10-19 00:00:00 Read the full story.  

Canada installs Chinese underwater monitoring devices next to US nuclear submarine base

While the eyes of the world have been on the strategic tussle between Beijing and Washington in the South China Sea, Chinese scientists, with the help of the Canadian authorities, have succeeded in positioning four monitoring devices in waters just 300km (186 miles) off the United States’ Pacific coast. The instruments, which use hi-tech sensors to monitor the underwater environment, are connected to the Ocean Network Canada (ONC), a grid of marine observatories stretching from the northeast Pacific to the Arctic. While the network is operated by the University of Victoria in British Columbia, its four new additions are the property of the Sanya Institute of Deep-sea Science and Engineering, a unit of the Chinese Academy of Sciences, which also developed and built them. The devices were placed on the Endeavour segment of the Juan de Fuca Ridge by a remote-controlled submersible owned by the Canadian Coast Guard on June 27. Now fully operational, they can be used to provide real-time streaming of data to the Chinese institute’s control centres in Sanya, a city on the island province of Hainan, and elsewhere. 2018-10-22 01:02:46+08:00 Read the full story.  

Driverless trucks, supply chains, AI and Big Data – How does it work?

You would’ve heard of driverless cars. The technology would soon be redefining the way people commute, not only around the world but between and in cities as well. Driverless technology is not only being applied to personal vehicles, but also to commercial transportation. Driverless trucks are the natural evolution of driverless cars. They will cut down on costs, increase efficiency, and generally shake up the shipping industry. Let’s look at how artificial intelligence technology is changing the outlook of the freight industry. However, at the moment, “driverless trucks” will be a misnomer. There will still be someone in the cab, monitoring the truck like a conductor, rather than actively driving. Much like experimental driverless taxis or the driverless Google cars, there’s someone behind the wheel in case something goes wrong. In the case of shipping, however, it’s also to make sure the truck is on schedule, making the correct deliveries, and more. It’s not something a driver would have to constantly do, but rather they must check in every so often to make sure all systems are running properly. The driver is a failsafe more than anything, and perhaps a person to provide a signature upon delivery. 2018-10-17 14:42:03+00:00 Read the full story.  

Designing the future with the help of the past with Bill Buxton – Microsoft Research Podcast 32 mins

Episode 46, October 17, 2018 The ancient Chinese philosopher Confucius famously exhorted his pupils to study the past if they would divine the future. In 2018, we get the same advice from a decidedly more modern, but equally philosophical Bill Buxton, Principal Researcher in the HCI group at Microsoft Research. In addition to his pioneering work in computer science and design, Bill Buxton has spent the past several decades amassing a collection of more than a thousand artifacts that chronicle the history of human computer interaction for the very purpose of informing the future of human computer interaction. Today, in a wide-ranging interview, Bill Buxton explains why Marcel Proust and TS Eliot can be instructive for computer scientists, why the long nose of innovation is essential to success in technology design, why problem-setting is more important than problem-solving, and why we must remember, as we design our technologies, that every technological decision we make is an ethical decision as well. 2018-10-17 08:00:57-07:00 Read the full story.  

In a tiny Seattle apartment, the bed is on the ceiling, and that’s part of what makes it a smart home

In a small apartment just a couple blocks from the waterfront in downtown Seattle, windows looking south over the Alaskan Way Viaduct usually afford a view of Mount Rainier. Clouds kept the mountain hidden during a visit this week. The home’s bed and several storage compartments were also out of sight. The one-bedroom apartment on University Street, in a building called Cyrene, looks like you’d imagine many do in Seattle these days — sleek and spare, and kind of tiny. But it’s a little extra minimalist thanks to a smart-home invention that has created space where there would normally be furniture. San Francisco-based Bumblebee Spaces is in Seattle to demo its signature Bumblebee bed and smart storage cabinets that comprise a system for making “space for what matters.” This is no ordinary furniture showroom. And this is not an old-school Murphy bed, folded up into a cabinet, but rather a modern answer that uses depth sensors, artificial intelligence, machine learning, an app-based control panel, industrial-grade straps and electric motors to raise a bed off the floor and tuck it overhead on the ceiling. 2018-10-19 13:00:12-07:00 Read the full story.  

Arm’s Neoverse will span everything from tiny devices to high-end server chips

Arm’s Neoverse, a cloud-to-edge infrastructure that the chip design company hopes will support a world with a trillion intelligent devices. The Neoverse is basically Arm’s ecosystem for supporting the chip design and manufacturing firms that will produce those devices, based on the Arm architecture. But it’s also a market-based approach to supporting customers in different segments, like automotive, machine learning, or the internet of things (IoT). Arm is also stepping up with a more aggressive roadmap for processors that make use of the most advanced manufacturing possible. That means the company is targeting everything from low-power embedded processors to high-end chips for servers. I spoke with Haas about Neoverse and other topics at the Arm TechCon event last week in San Jose, California. Here’s an edited transcript of our interview… 2018-10-21 00:00:00 Read the full story.  
Behind a PayWall or Registration Wall…

Which Data Skills Do You Actually Need? This 2×2 Matrix Will Tell You.

Data skills — the skills to turn data into insight and action — are the driver of modern economies. According to the World Economic Forum, computing and mathematically-focused jobs are showing the strongest growth, at the expense of less quantitative roles. So whether it’s to maximize the part we play in data-driven economic growth, or simply to ensure that we and our teams remain relevant and employable, we need to think about transitioning to a… 2018-10-18 16:00:45+00:00 Read the full story.  
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Jessica Titlebaum Darmoni, Founder of The Title Connection

Jessica Darmoni’s Review of the 34th Futures Industry Association Expo

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The Futures Industry Association (FIA) held their 34th Annual FIA Expo conference at the Hilton Chicago last week bringing together capital markets influencers, regulators and innovators to discuss some of the most pressing matters facing the industry.
AdobeStock_196767235

AI & Machine Learning News. 09, October 2018

The Chairman of Nokia on Ensuring Every Employee Has a Basic Understanding of Machine Learning — Including Him

I’ve long been both paranoid and optimistic about the promise and potential of artificial intelligence to disrupt — well, almost everything. Last year, I was struck by how fast machine learning was developing and I was concerned that both Nokia and I had been a little slow on the uptake. What could I do to educate myself and help the company along? As chairman of Nokia, I was fortunate to be able to worm my way onto the calendars of several of the world’s top AI researchers. But I only understood bits and pieces of what they told me, and I became frustrated when some of my discussion partners seemed more intent on showing off their own advanced understanding of the topic than truly wanting me to get a handle on “how does it really work.” I spent some time complaining. Then I realized that as a long-time CEO and Chairman, I had fallen into the trap of being defined by my role: I had grown accustomed to having things explained to me. Instead of trying to figure out the nuts and bolts of a seemingly complicated technology, I had gotten used to someone else doing the heavy lifting. Why not study machine learning myself and then explain what I learned to others who were struggling with the same questions? That might help them and raise the profile of machine learning in Nokia at the same time. 2018-10-04 13:00:05+00:00 Read the full story. CloudQuant Thoughts… Nokia is one of those companies that I miss. I don’t miss that ringtone, but they were truly a technological marvel of a company. If this documentary is still available online it is well worth an hour of your time. It is great to see the current CEO, Risto Siilasmaa, continuing the Nokia tradition of excellence towards its people and its business.   

Babysitter screening app Predictim uses AI to sniff out bullies

If you’re a parent with young kids, you probably know how arduous it can be to screen a babysitter. According to a Care.com survey, roughly 51 percent of families opt not to hire a sitter because it’s too stressful to find someone they like. And among those who have hired one, a whopping 62 percent didn’t bother to check their references. That spurred Sal Parsa and Joel Simonoff, the cofounders of Berkeley startup Predictim, to develop a no-frills solution that taps artificial intelligence (AI) to generate personality assessments from digital footprints. The eponymous Predictim platform, which launches today, uses natural language processing (NLP) and computer vision algorithms to sift through social media posts — including tweets, Facebook posts, and Instagram photos — for warning signs. 2018-10-04 00:00:00 Read the full story. CloudQuant Thoughts… This one is for my colleague Tayloe, he wants this for everyone he deals with online!  

Investors Say They Were Harmed by Manipulation in Volatility Products

OCC, the world’s largest equity derivatives clearing organization, announced today that total cleared contract volume in September reached 365,152,938 contracts, up 8.7 percent compared to September 2017 volume of 335,867,813. OCC’s year-to-date average daily cleared contract volume is up 18.6 percent with 20,209,877 contracts compared to 17,038,958 contracts in 2017. 2018-10-01 19:54:10+00:00 Read the full story. CloudQuant Thoughts… Investment News aggregators like John Lothian News are ideal jumping off points for new model ideas.  

Machine Learning Hacks: Cheatsheets, Codes, Guides And Walkthrough

As the Data Science and Machine Learning field evolve, there is a huge demand for a number of professionals who are skilled in this domain. When one starts with learning and implementing the techniques involved in building the models with the help of necessary libraries, it can be difficult to remember all the concepts. A flowchart or a cheat sheet will definitely help one to understand and remember the footsteps to build a robust model. In this article, we shall explore a couple of cheat sheets for machine learning tasks. For a given dataset, one can make use of the cheat sheets to handle various tasks with ease. 2018-10-04 11:23:49+00:00 Read the full story. CloudQuant Thoughts… This blog post is an oversimplification of what it takes to get up and running with machine learning but cheat sheets are great to have around.  

Themes and Conferences per Pacoid, Episode 2

Paco Nathan‘s column covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. Welcome back to our new monthly series! September has been the busiest part of “Conference Season” with excellent new material to review. Three themes jump out recently.
  • Data science for accountability.
  • Reinforcement learning challenges assumptions.
  • AI and Economics have surprises in-store.
2018-10-03 00:05:15-07:00 Read the full story. CloudQuant Thoughts… A very nice, well detailed (and long) blog post. Unfortunately a rare thing these days!  

How Artificial Intelligence Makes Today’s Email Marketing Smarter

When it comes to new technologies in email marketing, everyone’s attention is caught byartificial intelligence (AI). The talks about marketing teams replaced by robots in the near future make people feel a mixture of jitters and excitement. The majority of email marketers can only guess what are the potential implications of AI on their work. Very few specialists know how exactly they can apply AI in their email marketing activities. At the same time, marketing automation platforms add AI features to meet the trending demand for data-driven email campaigns and added segmentation. The enthusiasts, who were among the first to implement AI-functionality, already boast about the results they get. Simply stated, today AI helps answer the eternal questions about who to send what and when. To predict the right time and content that most likely will convert an individual recipient, AI takes into account maximum available data and does this in seconds. 2018-10-02 15:30:40+00:00 Read the full story. CloudQuant Thoughts… Reminded me of the  “how Target figured out a teenage girl was pregnant before her father did” story. It is an amazing tale of Data Science and Machine Learning.  

Fintech firm values properties based on sewer cocaine level data

An innovative proptech mortgage company backed by Savills has said it uses drug usage statistics gleaned from sewer monitoring to value homes prior to making loan offers. The extraordinary claim was made by Proportunity CEO and cofounder Vadim Toader at a Google Campus gathering in London’s Old Street tech neighbourhood this week. While explaining to a crowded room of fellow tech start-up entrepreneurs how his company values properties, he revealed that Proportunity’s data scientists use reports from official measurements of chemical compounds in sewers to determine levels of local drug use and therefore measure an area’s economic development. Attendees were shocked to hear that gentrification of a postcode can be measured by a reduction in crack cocaine residue present in local sewers. 2018-10-05 06:55:04+00:00 Read the full story. CloudQuant Thoughts… OK nothing to do with AI, ML ,BigData, Fintech etc and no idea why our algo picked it out but it is very interesting none the less!!  

An Australian start up is using AI to improve IVF treatment

The application of AI and deep learning can improve the results of IVF treatment by up to 50 per cent, according to Dr Michelle Perugini of Life Whisper. A critical part of IVF treatment includes analysing embryos to determine their suitability. Traditionally this step was completed by eye, with embryologists analysing images under a microscope to determine their viability – “A typically manual and imprecise process. It’s a huge accuracy uplift in picking the best embryos,It’s an area where there’s a lot of subjectivity, at the moment, in selecting the best embryos. It’s very difficult for clinicians to do that,” Perugini told Which-50. Images are scanned for complex patterns and features common in more healthy embryos, ultimately providing clinicians with information on which embryo has the best chance of success. “The AI essentially provides an extra set of eyes via the computer that can help them to make the best decision and pick the right embryos first time.” Perugini says clinicians can identify the embryos on either end of the quality spectrum with relative ease but “the 90 per cent in the middle” are difficult to assess. Adding AI and machine learning appears to have dramatically improved accuracy. Perugini cited two clinical studies which had shown adding the AI tool had improved success rates between 30 and 50 per cent. 2018-10-02 16:23:15+11:00 Read the full story. CloudQuant Thoughts… AI helping people have babies is just about the most positive use in the world. Imagine writing code that brings into the world little humans that otherwise would not have existed! Amazing!  

AI could “end famine” by spotting developing crises before they begin, says World Bank president.

Speaking to reporters at Stanford University, Jim Yong Kim said AI could give aid workers as much as six months’ advance warning to stabilise potential famines before they spiraled out of control. The Bank is working with Amazon, Google and Microsoft to develop a system called Artemis that would trawl through data from satellites, food prices, weather records and social media and analyse it for signs of trouble. The AI system would be linked to a funding mechanism which automatically releases relief money once certain thresholds are met, rather than waiting for a famine to be officially declared. Mr Kim said: “This could actually end famines. We are getting information well ahead of time instead of waiting until the fifth stage of famine. 2018-10-03 00:00:00 Read the full story. CloudQuant Thoughts… And if it is not helping to bring babies into the world, it is helping the poorest most desperate in the world out of horrendous situations. Oh… and Skynet!  

How AI and emotion tracking are helping brands avoid costly video campaign mistakes

Marketers have plenty of ways to measure video campaign success, but artificial intelligence is uncovering new methods for determining whether the dollars you’re spending are being applied optimally. That’s what video insights company YouFirst is offering, and a recent study of one of its clients, spanning 13 video campaigns over two years, is revealing. Even with the latest and greatest analytics tools at the brand’s disposal, AI and emotion tracking are opening up new insights. More importantly, AI is showing where to make changes to a campaign so it hits its exact target market — and when to pull the plug. YouFirst works by allowing a focus group of video viewers access to the content through its player, which — with permission — monitors the facial expressions of the consumer via a webcam. The AI and platform YouFirst developed can determine whether the video is eliciting an emotional response during playback, and it can understand the difference between six main emotions; anger, disgust, fear, happiness, sadness, and surprise. 2018-10-05 00:00:00 Read the full story. CloudQuant Thoughts… In 2013 prior to the launch of the Xbox One Microsoft told advertisers that its new “always on camera” could tell them how many people were in the room watching an advertisement, their ages, heart rates, muscle tension, whether or not they were smiling. So YouFirst seem a little behind the curve.. make that a lot..  

This week’s Triple Hitter

Cloudera, Hortonworks Merger Will Create New Data Platform

A couple of neighboring Silicon Valley data platform makers who have been competing in the Hadoop data storage and analytics market are finally joining forces–to the surprise of not many people in the enterprise IT world. In fact, some industry observers were wondering why it took so long to happen. Cloudera and Hortonworks, who both entered the business world about 10 years ago and immediately began going after the same customers, jointly announced Oct. 3 that they have agreed to become one and the same in an all-stock merger of equals worth a combined $5.2 billion. The companies, the combination of which was unanimously approved by the boards of both companies, will use their synergies to create what they describe as “the world’s leading next-generation data platform provider, spanning multi-cloud, on-premises and the edge.” 2018-10-04 00:00:00 Read the full story.

Elastic IPO Expected to Raise $250M

Elastic went public today on the New York Stock Exchange in what could be one of the big data industry’s hottest IPOs of the year. Elastic started selling shares this morning under the ticker symbol ESTC at $36 per share. Oversubscribed demand led the Mountain View, California-based company to raise its price target from $26 to $29. Shares “popped” in early trading, and were up more than 90%, an indication of continued strong demand. The compan… 2018-10-05 00:00:00 Read the full story.

This tech investor had a killer week thanks to two big open-source deals

Mike Volpi of Index Ventures started investing in open-source software companies when it wasn’t clear if they could make much money. This past week — more than any before it — has validated his conviction that they can. On Wednesday, Hortonworks, a big-data software company backed by Volpi, announced that it was merging with competitor Cloudera. Two days later, another one of Volpi’s companies, Elastic, started trading on the New York Stock Exchange and doubled in value in its debut. 2018-10-07 00:00:00 Read the full story. CloudQuant Thoughts… Not much to add here except that news regarding this merger, IPO and Investment took up a significant portion of this feed this week. Either they are very important or someone has a great publicist!  
Below the fold…   Facebook Launches PyTorch 1.0 With Integrations For Google Cloud Working on their earlier vision of making development in artificial intelligence faster and more interoperable, Facebook on Tuesday announced their first-ever PyTorch Developer Conference, where they introduced updates about the growing ecosystem of software, hardware, and education partners that are deepening their investment in PyTorch. According to a report, an unspecified number of engineers are collaborating to make the open source machine learning PyTorch framework by the social media giant work with Google’s Tensor Processing Units (TPUs). This collaboration is also reportedly one of the rare instances where these tech giants are working together on a project. Rajen Sheth, director of product management at Google Cloud said in a blog post, “Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs. The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs. 2018-10-03 06:00:51+00:00 Read the full story.   5 Amazing Machine Learning GitHub Repositories & Reddit Threads from September 2018 Welcome to the September edition of our popular GitHub repositories and Reddit discussions series! GitHub repositories continue to change the way teams code and collaborate on projects. They’re a great source of knowledge for anyone willing to tap into their infinite potential. As more and more professionals are vying to break into the machine learning field, everyone needs to keep themselves updated with the latest breakthroughs and frameworks. GitHub serves as a gateway to learn from the best in the business.
  • Object Detection using Deep Learning – a one-stop shop where you can find all the top object detection algorithms designed since 2014
  • Train Imagenet Models in 18 Minutes – You need to have Python 3.6 (or higher) to get started. Go ahead and dive right in.
  • Pypeline – Creating Concurrent Data Pipelines – This repository contains codes, benchmarks, documentation and other resources to help you become a data pipeline expert!
  • Everybody Dance Now – Pose Estimation
  • Beginner Friendly AI Papers you can Implement – Quite a few links to easy-to-read AI research papers
  • What Happens when an Already Accepted Research Paper is Found to have Flaws? – The original author of the paper took time out to respond to this mistake.
  • Having Trouble Understanding a Research Paper? This Thread has All the Answers – That’s exactly what this thread aims to do.
  • How can you Prepare for a Research Oriented Role? – this thread is an enlightening one with different takes on what the prerequisites are.
  • Researchers who Claim they will Release Code Mentioned in a Paper but Never Do
2018-10-04 10:43:30+05:30 Read the full story.   The Graph That Knows the World Somewhere in a data center in Fremont, California, exists a large computer cluster that’s hoovering up every piece of data it can find from the Web and using machine learning algorithms to find connections among them. It’s arguably the largest known graph database in existence, encompassing 10 billion entities and 10 trillion edges. No, it’s not some secret government project to catalog the world’s information. In fact, the graph was created and is run by a private company called Diffbot, and in fact you can get access to it for as little as $300 per month. 2018-10-02 00:00:00 Read the full story.   Autonomous cars present new challenges for Explainable AI (XAI) As society trusts more of its operations to autonomous systems, increasingly companies are making it a requirement that humans can understand how exactly a machine has reached a certain conclusion. The research efforts behind Explainable AI (XAI) are gaining traction as technology giants like Microsoft, Google and IBM, agree that AI should be to explain its decision making. XAI, sometimes called transparent AI, has the backing of the Defense Advanced Research Projects Agency (DARPA) an agency of the US Department of Defense, which is funding a large program develop the state of the art explainable AI techniques and modelling. Dr Brian Ruttenberg was formerly the senior scientist at Charles River Analytics (CRA) in Cambridge, where he was the principal investigator for CRA’s effort on DARPA’s XAI program. He argues XAI helps to identify bias or errors in algorithms and engenders trust in the technology. “Doctors aren’t going to tell someone they have cancer because the machine learning box told them they have cancer,” Ruttenberg told Which-50. 2018-10-08 00:07:23+11:00 Read the full story.   2018 GeekWire Summit recap: The future of innovation at our biggest tech conference ever Flying cars, virtual reality gloves, rock-picking robots, and the future of companies such as Boeing, eBay, Amazon, Redfin, and Nintendo. The 2018 GeekWire Summit was our biggest event yet, an action-packed national tech conference that explored what’s next in the innovation economy and brought together more than 800 business, tech, media and government leaders. The panel discussions and fireside chats on stage in Seattle covered a wide spectrum of industries: aerospace, e-commerce, real estate, philanthropy, education, and more. 2018-10-04 17:57:15-07:00 Read the full story.   What do you call AI without the boring bits : Kubeflow? – Cassie Kozyrkov of Google AGAIN! Kubeflow: beautiful machine learning. Specifically, the beauty of the data scientist’s experience while wrestling into submission the beast that is machine learning in multicloud hybrid environments as an entire stack. Which, if you’ve tried to DIY it in the pre-Kubeflow days, was anything but beautiful. 2018-10-05 20:19:43.883000+00:00 Read the full story.   AI Weekly: Free speech fears about California’s bot bill are overblown How many bots have you interacted with today? If you’re an avid social media user, the number might be higher than you realize. In fact, 95 million Instagram users could be bots. Experts peg the number of bots on Twitter at 48 million. And that number might be in the hundreds of millions on Facebook. (In May, the social network said it disabled 1.3 billion fake and automated profiles.) Lawmakers in California took a crack at the malicious bot problem this week, when Governor Jerry Brown signed into law the B.O.T. Act (SB 1001), which prohibits automated, anonymous accounts from “[incentivizing] a purchase or sale of goods or services in a commercial transaction or [influencing] a vote in an election.” Effective July 1, 2019, chatbots on platforms with more than 10 million unique monthly visitors from the U.S. will have to disclose in a “clear, conspicuous, and reasonably designed” way that they’re not human. The legislation, which was jointly drafted by nonprofit consumer ratings group Common Sense Media and the Center for Human Technology, is the first of its kind in the U.S. Federal regulation might follow on its heels — Senator Dianne Feinstein (D-CA) introduced a similar bill in the U.S. Senate in June. And both have prompted debates about free speech. In an interview with the New York Times earlier this summer, Ryan Calo, co-director of the Tech Policy Lab at the University of Washington, said that a broad-brush ban on political commentary could prove problematic. “[Speech] comes in different forms,” he said. “Imagine a concerned citizen sets up a bot to criticize a particular official for failing to act on climate change. Now say that official runs for re-election. Is the concerned citizen now in violation of California law?” Meanwhile, the Electronic Frontier Foundation (EFF) — a nonprofit organization dedicated to defending civil liberties across digital domains — argued that forcing bots to identify themselves as such would “restrict and chill [the] speech” of their creators. “Bots are used for all sorts of ordinary and protected speech activities,” the group asserted, “including poetry, political speech, and even satire, such as poking fun at people who cannot resist arguing — even with bots.” 2018-10-05 00:00:00 Read the full story.   Automation Is The Answer To Our Changing Demographics “Automation can be the ally of our prosperity if we will just look ahead, if we will understand what is to come, and if we will set our course wisely after proper planning for the future” – these words were spoken not by a tech CEO or even in recent history, but by U.S. President Lyndon B. Johnson in 1964. The possibility – and fear – of robots rendering human work unneeded has riveted us from almost the moment of their creation. I think that instead of being the enemy, automation in the workplace will save us from productivity declines attributable to changing demographics globally. We’re already seeing proof of this in RPA, as the use of offshore outsourcing has steadily dwindled due to a one-two punch of rising workforce costs and RPA getting better, faster and cheaper thanks to A.I. We might not need to employ as many people in the future, but that’s not such a bad thing in a future with fewer people. 2018-10-05 00:00:00 Read the full story.   A.I. May Have Met Its Ethical Match in California Senate Bill 1001 A new California law, Senate Bill 1001, aims to prevent chatbots from pretending as though they’re human – but may have given us an additional gift when it comes to artificial intelligence (A.I.). Here’s the text from the bill: “This bill would, with certain exceptions, make it unlawful for any person to use a bot to communicate or interact with another person in California online with the intent to mislead the other person about its artificial identity for the purpose of knowingly deceiving the person about the content of the communication in order to incentivize a purchase or sale of goods or services in a commercial transaction or to influence a vote in an election. The bill would define various terms for these purposes. The bill would make these provisions operative on July 1, 2019.” It goes on to define what ‘bot’ and ‘online’ mean (such as: “‘Bot’ means an automated online account where all or substantially all of the actions or posts of that account are not the result of a person.”). The language seems specific to Twitter and Facebook accounts with regard to election hacking and altering public opinions. 2018-10-04 00:00:00 Read the full story.   Leveraging DataFrames in Python (Part 1) – Towards Data Science In order to get insights from data you have to play with it a little.. It is important to be able to extract, filter, and transform data from DataFrames in order to drill into the data that really matters. The pandas library has many techniques that make this process efficient and intuitive. And in today’s article I will list those techniques with code samples and some explanations. Let’s get started. 2018-10-08 00:44:35.221000+00:00 Read the full story.   Lesser-Known Technology Jobs With Growing Salaries In today’s talent market, having cutting-edge technical skills and/or a CS degree is potentially a ticket to a lucrative career in programming, software development or engineering. But what if you’d rather do something different? Will you have to give up the opportunity to make big bucks? Not necessarily. Here are some examples of lesser-known tech jobs that pay hefty salaries (and are available today).
  • Data Detective
  • Conversational Agent Designer
  • Creative Catalyst\Growth Hacker
  • Cyber City Analyst
  • Man-Machine Teaming Manager
  • Digital Targeter
  • Technical Operations Officer
2018-10-07 00:00:00 Read the full story.   Big Data Quarterly (PDF) : Fall 2018 Issue Give Us One Minute and We Will Give You The World of Data Management Once Every Quarter! Subscribe FREE to DBTA Magazine Now! 2018-10-03 00:00:00 Read the full story.   What Is Deep Learning AI? A Simple Guide With 8 Practical Examples There’s a lot of conversation lately about all the possibilities of machines learning to do things humans currently do in our factories, warehouses, offices and homes. While the technology is evolving—quickly—along with fears and excitement, terms such as artificial intelligence, machine learning and deep learning may leave you perplexed. I hope that this simple guide will help sort out the confusion around deep learning and that the 8 practical examples will help to clarify the actual use of deep learning technology today. The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various (deep) layers that enable learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve. 2018-10-04 09:58:43+00:00 Read the full story.   Deutsche Bank’s AI stockpicker cuts through corporate greenwash Data scientists at Deutsche Bank have developed an artificial intelligence tool that cuts through the flannel padding out the Environmental Social and Governance reports of most companies to analyse non-financial news and quantify the stock impact. Deutsche Bank’s Alpha-Dig machine is one of the first products developed by the Bank’s Data Innovation Group (dbDIG), which was set up to tap into the power of alternative data and AI to provide data-driven investment solutions. The bank trained A-Dig on data from 1000 organisations, using Natural Language Processing to infer the context of a typical ESG report and generate ‘buy’ and ‘sell’ signals accordingly. The tool is being used to cut through the positive spin and upbeat language that clutters company financial reporting and dig out the hidden meanings behind the ‘greenwashing’. The Bank says that over the years since the financial crisis, the average number of words per quarterly SEC filing is now about 20,000, more than double the number two decades ago, and annual filings have grown to 50,000 words. 2018-10-05 11:51:00 Read the full story.   These Are the Skills You Need to Land a Machine Learning Job Machine learning (ML) is hot right now, and seems to be poised for a broader takeover of tech in the future. As jobs open up, we’ve identified the skills needed to land your next ML job. A recent study by Univa shows 96 percent of tech pros say the number of machine learning projects will expand over the next 24 months. Some 68 percent of respondents say their company has used machine learning increasingly in the past two years, too. Interest in a technology or skill typically aligns with job growth. We’ve seen ML jobs rise steadily over time, and there’s been a strong boost the past year or so as Google, Apple, and Microsoft have gotten serious about what machine learning is capable of. As each company rolls out their own developer tools, developer engagement and interest climbs. Tech pros can’t just apply for machine learning jobs without correlating skills, though. As we see in the chart below, the total number of ML jobs over the past two years has grown nearly 600 percent. From the beginning, one skill – ‘data science’ – has led the way. 2018-10-05 00:00:00 Read the full story.   Translating AI for Bottom Line Impact It’s no secret that organizations have been increasingly turning to advanced analytics and artificial intelligence (AI) to improve decision-making across business processes — from research and design to supply chain and risk management. Along the way, there’s been plenty of literature and executive hand-wringing over hiring and deploying ever-scarce data scientists to make this happen. Certainly, data scientists are required to build the analytics models — including machine learning and, increasingly, deep learning — capable of turning vast amounts of data into insights. More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — translators. Why are translators so important? They help ensure that organizations achieve real impact from their analytics initiatives (which has the added benefit of keeping data scientists fulfilled and more likely to stay on, easing executives’ stress over sourcing that talent). 2018-10-18 16:00:00+00:00 Read the full story.   Filling Policy Void, U.S. AI Report Outlines Stakes Who’s ahead in the race to win the global AI race is the subject of intense debate, much of it centered on China’s all-out effort to dominate AI development and whether the U.S. can get its act together in time to keep pace. Few would dispute Beijing’s commitment to AI development, most pointing to its aggressive strategy that seeks to catch up with the U.S. and the rest of the world by 2020, becoming dominant by 2030. That has prompted policy makers to worry over the ability of U.S. companies to keep pace and how “general” AI will be used in the future. Despite huge federal investments in R&D, totaling nearly $500 billion in 2015, “the United States’ leadership in AI is no longer guaranteed,” warns a report released in September by the House Oversight and Government Reform IT subcommittee. The study is based on a series of hearings held earlier this year that included AI experts from industry, academia and federal research agencies. 2018-10-02 00:00:00 Read the full story.   Microsoft Open Sources Their Infer.NET Machine Learning Framework Tech giant Microsoft made the decision to announce that one of the top-tier cross-platform frameworks for model-based machine learning is open to one and all worldwide. “We’re extremely excited today to open source Infer.NET on GitHub under the permissive MIT license for free use in commercial applications,” wrote Yordan Zaykov, Principal Research Software Engineering Lead at Microsoft in an official statement. Open sourcing Infer.NET represents the culmination of a long and ambitious journey. The Microsoft Research Team in Cambridge embarked on developing the framework back in 2004. The statement said that they had learned a lot along the way about making ML solutions that are scalable and interpretable. 2018-10-08 11:12:53+00:00 Read the full story.   SQream Adds Power9 to GPU Data Warehouse Data warehouse vendor SQream Technologies, which recently claimed a 15-fold performance increase with the latest release of its SQreamDB, has now added support for IBM’s Power9 architecture to its GPU-accelerated database. The New York-based database specialist with R&D operations in Tel Aviv, Israel, announced during an IBM OpenPower event in Europe on Wednesday (Oct. 3) that its SQream data warehouse will now combine the Power9 multicore design with its existing data warehouse accelerated with Nvidia’s GPUs. The combination would yield SQL query performance improvements of as much as 150 percent for Power9 users, the company claimed. 2018-10-03 00:00:00 Read the full story.   Data Science, AI and Hype cycles When in industry more than 50% of new roles are driven towards a specific skill set and when projections from various recruiting companies shows the world being short of certain skilled people and employers are scrambling to find certain type of resources in the market and are willing to pay a premium to get them on-board then it is a clear sign that we’re in a hype cycle. The skill here is Data Science and resources Data Scientists. New Keyword, New Hype Cycle – Those who have been in the industry long enough can recognize this. A decade back industry was going crazy for a similar skill known as Business Analysts, who are now found dime a dozen in the market (apologies if I’ve hurt someone’s sentiments, but you can’t escape the truth). I know certain organizations where the certain preference is being given to Data Modelers, Data Analysts & data Scientists instead of sitting Business Analysts. Orders are simple, upgrade or get left behind. 2018-10-05 16:46:53 Read the full story.   Why the Future is Bright for Natural Language Processing Natural language processing (NLP), one of the most exciting components of AI is all set to rule the way we communicate with the external world. NLP uses computational and mathematical methods to analyze the human language to facilitate interactions with computers using conversational language. Subtopics in this genre include natural language understanding of the inputs created by humans, and natural language generation, to focus on generating natural language narratives. The most popular approaches to NLP deploy Machine Learning. Natural language processing is improving human-computer conversations at the most advanced levels with applications or systems like Google Duplex, which can act as an agent to perform tasks like making haircut appointments over the phone by conversing with humans. IBM’s Debater Project Debater, a groundbreaking AI technology which can argue with the humans on complex topics is another stride attributed to NLP. 2018-10-05 18:31:11+05:30 Read the full story.   A.I. Still in ‘Discovery’ Phase, but Companies Seeing Its Value Artificial intelligence (A.I.) is still reaching maturity as a technology, but a new study underscores that companies are very interested in it. We’re also getting a better idea of how those firms planning to use A.I. will implement it alongside their existing services. Globant’s new study queries “senior-level decision makers” in the United States, and was conducted over a week in February 2018. As you might expect, the study notes that CEOs are the most involved in how A.I. fits into their company, with almost half directly involved in artificial intelligence decisions. Department leads were second, surprisingly outpacing CTOs and CIOs (though it’s very possible many companies just don’t have people in those roles). Only eight percent say “all” employees are offered a chance to voice their opinions on artificial intelligence. The study found 20 percent of respondents don’t see how A.I. would have an “immediate impact” on their business (one-fifth of ‘decision makers’ simply don’t understand what artificial intelligence is at this stage). Another 32 percent say they think A.I. will be useful in automating routine tasks, while 48 percent say it will be handy for surfacing insights from massive datasets. 2018-10-05 00:00:00 Read the full story.   Deep Learning Is Great, But Use Cases Remain Narrow Deep learning is all the rage these days, and is driving a surge in interest around artificial intelligence. However, despite the advantages that deep neural networks can bring for certain applications, the actual use cases for deep learning in the real world remain narrow, as traditional machine learning methods continue to lead the way. The rapid ascent of deep learning is arguably one of the least expected technological phenomena to have occurred in the past five years. While neural networks have been around for decades, it wasn’t until the University of Toronto’s Geoff Hinton paired those techniques with a new computational paradigm (GPU) and the availability of huge amounts of training data to yield what we now know as deep learning. 2018-10-03 00:00:00 Read the full story.   Intelligent Data Management Supports Better Data Access Data access is a right, not a privilege. That philosophy may not be directly stated by companies, but certainly they have moved in that direction. Join that trend with the phenomenal growth in data from multiple sources in multiple formats and hosted across multiple venues (like the Cloud), and you can see how barriers to entry can expand for the increased number of users that want or need to get to data sets. According to a Dun & Bradstreet survey, three fifths of executives at 870 companies said their data is stored in silos, and only 20% reported that their data is fully integrated and leveraged across the company. “The real organizational problem is how do I make people able to discover the right data, to make sure it’s what they really want, and make it available to them? 2018-10-04 00:35:14-07:00 Read the full story.   Make IoT Data Actionable with Splunk for Industrial IoT According to a recent press release, “Splunk Inc., delivering actions and outcomes from any data, today announced the general availability of Splunk for Industrial IoT, Splunk’s first Internet of Things (IoT) solution. Splunk Industrial IoT combines the power of Splunk® Enterprise, the Splunk Machine Learning Toolkit, and Splunk Industrial Asset Intelligence (IAI) to provide a simple view of complex industrial data, helping industrial organizations minimize downtime, shift operations from reactive to proactive and save money.” 2018-10-05 00:05:59-07:00 Read the full story.   Xilinx Unveils Versal for AI Workload Acceleration Xilinx officials earlier this year introduced a concept they called an adaptive compute acceleration platform—or ACAP—that essentially brings together multiple compute acceleration technologies, integrated networking, leading edge memory, software development tools and frameworks to address such modern workloads as artificial intelligence, big data and 5G networking. Officials with the FPGA (field-programmable gate array) vendor at the time said innovation in the industry was happening too quickly for compute processing technologies like CPUs from Intel and GPUs from Nvidia and Advanced Micro Devices to keep pace and called for a highly software programmable architecture that could quickly adapt to the changing needs. ACAP was the focus of what Xilinx engineers called its Everest project, an effort years in the making that cost more than $1 billion. 2018-10-02 00:00:00 Read the full story.   Inferring The Future Of The FPGA, And Then Making It Technologies often start out in one place and then find themselves in another. The architecture of the GPU was initially driven by the need for 3D graphics for video games, was co-opted as a massively parallel processor for HPC workloads, and finally became the engine for machine learning training, forever changing its architecture, particularly with the latest “Pascal” and “Volta” GPUs aimed at the Tesla accelerator lines. The changes in the GPU architecture to do machine learning better didn’t make GPUs any less valuable to gamers or HPC centers, but it is clear what is driving the technology. A similar thing is happening with the field programmable gate array, that wonderful malleable device that sits somewhere on the borderline between hardware and software that has had enthusiasts come and go and is now finding its way as a device of choice for machine learning inference while a t the same time holding on to its old jobs as a network function accelerator for server adapter cards or network switches, just to name two important things that FPGAs do in the real world. But make no mistake about it, the FPGA, no matter what Xilinx wants to call it as it unveils the first of its “Everest” line of products, is first and foremost designed as an inference engine and whatever goodness that comes to these other workloads will be fortunate even if it somewhat coincidental. 2018-10-02 00:00:00 Read the full story.   No, Robots Are Not Taking Away Your Jobs, They Are Making Them More Humane There’s no dearth of doomsday theories where it’s speculated that artificial intelligence would slowly take over the entire world — starting with jobs and sources of employment. While experts say that many jobs will be affected — not necessarily reduced — by AI, it all boils down to how wisely we allow technology to have an impact on us. For example, robots can help reduce the mundane and repetitive tasks, which can help explore human skills like empathy and creativity in effective ways. Automation can provide in routine work and put humans to the work that requires variability and creativity. 2018-10-05 04:32:05+00:00 Read the full story.   4 Tech Jobs Most at Risk from Automation, Artificial Intelligence …automation won’t prove a good thing for every tech pro. Many companies will look at these labor-saving tools and decide they can get by with smaller teams of technologists. While high performers will benefit from automation, it stands to reason that not everyone will survive when new, more sophisticated platforms come online. With that in mind, here are four tech jobs most at risk from automation and A.I.
  • Datacenter Administrators
  • Help Desk Staff
  • Programmers
  • Data Analysts
2018-10-03 00:00:00 Read the full story.   Ask A Flowchart: What Should I Name My Marketing AI? Artificial intelligence is a big deal in marketing technology. Forrester sees strong potential in AI as an enabling technology for marketing, and it’s great to see many vendors embracing AI to enhance existing offerings and as the basis for new solutions. But for vendors, mastering AI technology alone isn’t enough to get to the top. AI can be intimidating to buyers because it is opaque and difficult to evaluate. Vendors need to take a page from the branding playbook of the very marketers they are selling to. Because what AI-driven marketing applications really need to ensure success is a great name — something to humanize the technology and make it relatable beyond the ins and outs of APIs, classifications, and deep learning. Differentiation is what this game is all about for vendors, and gaining recognition is critically important in an increasingly congested landscape of AI-driven marketing solutions. But don’t worry: We’ve put together a handy flowchart to keep your AI solution branding efforts on track. 2018-10-04 23:29:19+00:00 Read the full story.   Stanford AI detects even the smallest earthquakes from seismic data Seismic events like the magnitude-9.0 earthquake that hit off the coast of Japan in March 2011 aren’t difficult to detect, but few are quite so violent. Microearthquakes — low-intensity earthquakes that register 2.0 or less magnitude on the moment magnitude scale — rarely cause property damage. And as a result of background noise, small events, and false positives, they’re not always picked up by seismic monitoring systems. A possible solution is described in a new paper from the Department of Geophysics at Stanford University, where scientists have developed an AI system — dubbed Cnn-Rnn Earthquake Detector, or CRED — that can isolate and identify a range of seismic signals from historical and continuous data. 2018-10-05 00:00:00 Read the full story.   The Essential Landscape of Enterprise AI Companies (2018-2019) Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies. By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also known as a type of customer relationship management software, or CRM, it is the system of record for sales professionals to enter in their contacts, progress of leads, and for sales metrics to be tracked. Any company directly selling their products and services would benefit from a CRM. 2018-10-02 07:00:21+00:00 Read the full story.   The evidence is clear: data-driven decision-making drives profitability The evidence of the profitable impact of data on decision-making is undeniable — largely because it is based upon real data. As early as 2013, research by Andrew McAfee and his team at Centre for Digital Business found that the organisations most strongly focused on data-driven decision making had four per cent higher productivity overall and six per cent higher profits. In other words, a quantifiable, significant edge over the competition. By 2015 Gartner, in its Data-Driven Marketing Survey, was suggesting that marketers expected most of their decisions to be quantitatively driven by 2017 and that, as a result, most companies planned on growing their analytics teams. 2018-10-08 00:09:40+11:00 Read the full story.   3 Common Mistakes That Can Derail Your Team’s Predictive Analytics Efforts With today’s high demand for data scientists and the high salaries that they command, it’s often not practical for companies to keep them on staff.  Instead, many organizations work to ramp up their existing staff’s analytics skills, including predictive analytics. But organizations need to proceed with caution. Predictive analytics is especially easy to get wrong. Here are the first three “don’ts” your team needs to learn, and their corresponding remedies.
  1. Don’t Fall for Buzzwords — Clarify Your Objective
  2. Don’t Lead with Software Selection — Team Skills Come First
  3. Don’t Leap to the Number Crunching — Strategically Plan the Deployment
2018-10-03 00:00:00 Read the full story.   Microsoft Pushes Further into Voice Synthesis, Challenging Google Microsoft has rolled out updates to its various A.I. platforms for the enterprise, including Azure Cognitive Services. Some of those advances could place Microsoft in a position to compete directly with Google over next-generation features such as speech synthesis.The Microsoft releases took place in the context of this year’s Ignite conference, which is aimed primarily at tech pros and developers. Azure Cognitive Services now has a more advanced neural text-to-speech service, one that aspires to sound remarkably human. “Microsoft has reached a milestone in text-to-speech synthesis with a production system that uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people,” Xuedong Huang, a Microsoft technical fellow in Cloud and AI, wrote in a Sept. 24 posting on the Azure corporate blog. “With the human-like natural prosody and clear articulation of words, Neural TTS has significantly reduced listening fatigue when you interact with AI systems.” 2018-10-04 00:00:00 Read the full story.   An Introduction to Random Forest using the fastai Library (Machine Learning for Programmers – Part 1) Programming is a crucial prerequisite for anyone wanting to learn machine learning. Sure quite a few autoML tools are out there, but most are still at a very nascent stage and well beyond an individual’s budget. The sweet spot for a data scientist lies in combining programming with machine learning algorithms. Fast.ai is led by the amazing partnership of Jeremy Howard and Rachel Thomas. So when they recently released their machine learning course, I couldn’t wait to get started. What I personally liked about this course is the top-down approach to teaching. You first learn how to code an algorithm in Python, and then move to the theory aspect. While not a unique approach, it certainly has it’s advantages. 2018-10-08 09:11:22+05:30 Read the full story.   Managing Business Leadership Expectations Around the AI Hype Like many technologies, AI is now in a full-scale hype cycle, both in the industry and the societal/consumer mainstream. While it’s nice to have broad interest and appreciation in what we technologists and Data Scientists are doing with AI, having the spotlight on it can also be disruptive to our work. It can lead to expectations among the C-Suite that are out of sync with what we practitioners – or indeed the technology – are able to deliver today. Managing these expectations is as critical to the success of your AI projects as is any technological consideration. Here are a few expectations that might emerge as a result of the current hype cycle, and how to manage them in a reasonable way.
  • What AI is, and What it isn’t
  • Speed of Delivery
  • Power of the Technology
  • Pervasiveness of Deployment
  • To Cloud or Not to Cloud
2018-10-08 00:35:08-07:00 Read the full story.   Data Intelligence, Data Governance, and the Fourth Industrial Revolution Klaus Schwab, founder of World Economic Forum, and author of the book, The Fourth Industrial Revolution, said that there is no historical precedent for the speed of current technological breakthroughs, and that the Fourth Revolution is evolving at an exponential, rather than linear pace. The Third Industrial Revolution introduced the personal computer and the internet, and moved industry from analog devices and processes to simple digitization. Schwab said the Fourth is a shift to “innovation based on combinations of technologies, forcing companies to re-examine the way they do business.” 2018-10-02 00:35:51-07:00 Read the full story.   Beaten Down IBM Is Poised for Major Gains Long-time unfavored Dow stock International Business Machines Corp. (IBM) has been picking up momentum over the past few months, rising more than 9% since the middle of the year, a few percentage points ahead of the broader market. While there is still much to dislike about IBM, whose shriveling revenues over recent years has some analysts calling for further declines, some analysts are predicting a turnaround in revenues as the company focuses o… 2018-10-05 04:00:00-06:00 Read the full story.   Google CEO Sundar Pichai went hat in hand to the Pentagon to patch up its relationship with the military after an employee backlash Google CEO Sundar Pichai met with Pentagon officials during a recent trip to Washington DC, according to The Washington Post. Pichai sought to “smooth over tensions” after Google decided to sever an agreement to help the military analyze drone video footage. Google’s decision to sever the contract followed a protest by thousands of Google employees who objected to the company’s relationship with the military. But Google has never said it would stop working with the military on projects that didn’t violate the company’s values. 2018-10-05 00:00:00 Read the full story.   Moving from Matlab to Python – Cuemacro What are the best ways to move from Matlab to Python. For many financial firms, it is a very pertinent question. I used Matlab extensively during my career, particularly when I was at Lehman Brothers. At the time, it was the best tool to rapidly develop market analytics and systematic trading strategies. Over the years, Python usage has grown and as a result, many folks are now thinking about migrating from Matlab to Python. From a personal perspective, I’ve switched over from Matlab and haven’t used it for many years. It’s true that not everyone necessarily wants (or needs) to switch from Matlab. Below, I’ve outlined reasons for switching and also the process you might use to migrate from Matlab to Python. 2018-10-06 00:00:00 Read the full story.   ML Enlisted to Slow the Spread of Fake News As should be obvious by now, traditional fact-checking methods have failed to stop “fake news” from spreading like wildfire via social media circuits. While platforms like Facebook (NASDAQ: FB) are developing new algorithms for ferreting out fake new that woulod supplement a planned army of human moderators, others are taking more pro-active approaches. MIT’s CSAIL and the Qatar Computing Research Institute have come up with a machine learning tool used to determine if a news source is accurate or harbors a political agenda. “If a web site has published fake news before, there’s a good chance they’ll do it again,” said Ramy Baly, an MIT researcher and lead author on a paper about the fake news detector. “By automatically scraping data about these sites, the hope is that our system can help figure out which ones are likely to do it in the first place.” 2018-10-05 00:00:00 Read the full story.   Big data, big deal: What 7 Chicago data teams are working on right now Big data is oftentimes talked about like it’s magic, but behind the curtains are talented teams ensuring that everything works like it’s supposed to. And as with any emerging field, teams are made up of experts with backgrounds as divergent as the types of information they work with. We spoke to data leads at seven Chicago companies about the varied backgrounds that make up their teams, and what exciting projects they’re working on at the moment. 2018-10-03 00:00:00 Read the full story.   Steve Cohen-Backed ETF Platform Is About to Break New Ground From helping to bring gold investing to the masses to devising ETFs for short-selling hedge funds, Hector McNeil has been at the vanguard of the passive revolution for the past 20 years. Now, the 50-year old Yorkshireman is taking on the likes of BlackRock Inc. to shake up the industry in Europe. McNeil wants to fast-track the creation of passive products for wannabe issuers lacking in-house expertise or the stomach for execution risk through his firm HANetf, a white-label provider. And he has a high-profile backer: the venture-capital arm of Steve Cohen’s Point72 Asset Management LP. A spokesman for the firm confirmed the investment, but declined to comment further. 2018-10-04 09:30:13-04:00 Read the full story.   DeepMind Research Shows How To Build Safe AI Systems Safety of artificial intelligence systems has become more important as great advancements are done in the field of machine intelligence. The Safety Research team at DeepMind has put together a framework to build safe AI systems. Comparing AI systems to a rocket, DeepMind researchers said that everyone who “rides the rocket” will also enjoy the fruits of great AI. Also like rockets safety is one of the most important ingredients of building good AI. The team says that guaranteeing safety is paramount and requires carefully designing a system from the ground up. The safety research team has therefore focussed on building systems that are very reliable and work as advertised. They also work on discovering and avoiding possible near-term and long-term risks in AI. DeepMind is one of the very few organisations that works on Technical AI safety and the field is rapidly evolving. The work is mostly theoretical and high level but contains technical ideas that could be used in the design of practical systems. They published a research article that talks about the three most important aspects of AI safety:
  • Specification
  • Robustness
  • Assurance
2018-10-08 06:20:11+00:00 Read the full story.   3 Stages of Creating Smart By William Schmarzo, Hitachi Vantara. The technology is advancing at a pace that should enable any company to create “smart” products, things and spaces. But how does one go about actually creating smart? “Tomorrow’s market winners will win with the smartest products. It’s not enough to just build insanely great products; winners must have the smartest products!” – Bill Schmarzo Okay, that’s a pretty bold statement on my part (especially to challenge the famous Steve Jobs statement about building insanely great products), but then again I’m an analytics dude and think that analytics should be a part of every product and space – smart cities, smart cars, smart vacuums, smart hospitals, smart Chipotle… 2018-10-03 00:00:00 Read the full story.   Big Data Makes Multilingual Responsive Design A Reality Multilingual responsive design is an important tool and capability that has been made plausible by big data. Every website needs to provide a great user experience for its visitors. This is a principle that has been true since the earliest days of the Internet. However, it is becoming even more important, especially as user expectations are increasing and Google has started heavily relying on engagement statistics from analytics data as part of its ranking algorithm. This is one big reason that multilingual responsive design is so important and more achievable than ever. This has created some significant challenges for businesses that operate in different regions. If they don’t offer the best possible user experience for those customers, then their rankings can drop for keywords some of the languages is that provide a lot of traffic. This will translate into a much lower ROI from their international marketing strategy since ranking for the best keywords is key to getting the most value from your site. The good news is that new advances in big data have made improving user experience for website visitors in different parts of the world easier than ever. Here are some ways that digital marketers are tapping big data to create responsive website designs for users all over the world. 2018-10-04 22:31:06+00:00 Read the full story.   Female.AI: The Intersection Between Gender and Contemporary Artificial Intelligence The cinematic depiction of artificial intelligence as female and rebellious is not a new trend. It goes back to the mother of all Sci-Fi, “Metropolis” (1927), which heavily influenced the futuristic aesthetics and concepts of innovative films that came decades later. In two relatively new films, “Her” (2013) and “Ex-Machina” (2014), as well as in the TV-series “Westworld”, feminism and AI are intertwined. Creators present a feminist struggle against male dominance at the center of a larger struggle of seemingly conscious entities (what might be called AGI — Artificial General Intelligence) against their fragile human makers. In all three cases, the seductive power of a female body (or voice, which still is an embodiment to a certain extent) plays a pivotal role and leads to either death or heartbreak. The implicit lesson that keeps arising is: be careful what you wish for, both with women and with tech. The exploitation and oppression of intelligent machines (sadly this is was also the case in the lovely and optimistic “Her”) might be as finite and eventually painful as the exploitation and oppression of women. 2018-10-08 10:01:02.484000+00:00 Read the full story.   The Bank Branch of the Future – manned by Humans or Humanoids? We are living in one of the most technologically connected eras in civilization which means that we should be emotionally healthier than ever before as people who matter to us are only a click away and we can connect with them anytime. Therefore it came as a bit of a surprise when in January, United Kingdom announced that it was appointing a Minister for Loneliness thereby acknowledging the sad reality of modern life and loneliness as a worldwide growing epidemic. Thanks to proliferation of technology and advancements in areas such as artificial intelligence and robots and the millennial being more comfortable with texting than talking to the person next to them, we may as well imagine centers of human interactions such as shops and bank branches of the future being reduced to a center of high end devices and machines to execute the transaction with humanoid robots assisting people where necessary. This imagination is further backed by a survey that claims that in the coming 10 years, bankers believe that only 7% of interactions will be in person. 2018-10-07 18:24:40 Read the full story.   Announcing the first AI for Accessibility grantee: Zyrobotics In May, Microsoft CEO Satya Nadella announced AI for Accessibility to put AI tools in the hands of developers, universities, NGOs and inventors to accelerate the development of accessible and intelligent AI solutions to benefit people with disabilities around the world. Today, we are announcing the first AI for Accessibility grantee, Zyrobotics. They are developing unique solutions for accessible science, technology, engineering and math (STEM) education, like ReadAble Storiez, that are adaptive to the diverse needs of students. We are thrilled to have them as part of our program and we can’t wait to see the impact they will have! ReadAble Storiez is a reading fluency program for students with diverse learning needs, which also helps fill in the gaps for students from low-income homes who may not have access to speech-language or occupational therapists. By creating custom speech models with Microsoft Cognitive Services and Azure Machine Learning, they aim to identify when a student needs feedback, much like an occupational therapist would recognize and provide. 2018-10-04 00:00:00 Read the full story.   Measure distance between 2 words by simple calculation Calculating word distance in NLP is a routine task. Whenever you want to find the most nearest word or measuring metrics, word distance is a one of the way to achieve it. In my previous project, identifying target signal from OCR result is the critical part. To capture more valuable information, I have to tolerance a minor error from OCR result. Otherwise, I miss lots of important signal. Different from previous distance between word embeddings, string distance is calculating the minimum number of deletion, insertion or substitution required to change from one word to another word. There are numerous way to calculating distance while I will focus on two measurements in this sharing. 2018-10-07 15:09:30.028000+00:00 Read the full story.   Weekly Selection — Oct 5, 2018 – Towards Data Science
  • Beyond DQN/A3C: A Survey in Advanced Reinforcement Learning
  • Iterative Initial Centroid Search via Sampling for k-Means Clustering
  • Solving multiarmed bandits: A comparison of epsilon-greedy and Thompson sampling
  • Why Can a Machine Beat Mario but not Pokemon?
  • Neural Network Embeddings Explained
  • Convolution: An Exploration of a Familiar Operator’s Deeper Roots
  • Harnessing infinitely creative machine imagination
  • Experimenting with twitter data using Tensorflow
2018-10-05 13:23:30.129000+00:00 Read the full story.  
  Behind a Pay-Wall or Personal Data Collection Wall China’s tech giants spending more on AI than Silicon Valley China’s biggest tech companies have overtaken the giants of Silicon Valley in the race to invest in artificial intelligence and machine learning this year, according to new research for The Daily Telegraph. Out of around $14bn (£10.6bn) worth of AI investments made by the biggest eight US and Chinese tech companies this year, Chinese firms such as Baidu, Alibaba, Ant Financial and Tencent have taken a clear lead. Collectively, the four big Chinese groups have been involved in $12.8bn of the total, according to data compiled by Pitchbook, a financial data firm. 2018-10-07 00:00:00 Read the full story. Robots will take our jobs … and make more Traditional middle-class professions are about to be wiped out. Wages will fall as people compete desperately for the few jobs that remain. Unemployment will soar, and a super-rich elite will split away from the impoverished masses. There are plenty of dystopian visions out there about the likely impact of robotics and artificial intelligence on the way we all work – along with demand that capitalism be reshaped to cope. But here is something odd. Over the last 20 years, as technology has boomed and computers and smartphones have transformed the way we work and communicate, employment has been hitting record levels. 2018-10-05 00:00:00 Read the full story.
  The main source of this news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.  
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AI & Machine Learning News. 01, October 2018

AI Superpowers: China, Silicon Valley, and the New World Order by [Lee, Kai-Fu]U.S. should double A.I. funding, says the former head of Google China Kai-Fu Lee

As China becomes more active in artificial intelligence, the U.S. should double the amount it spends on research in the field, says investor and AI practitioner Kai-Fu Lee, who has worked for Google, Microsoft and Apple. The comments come after various parts of the U.S. government have made AI announcements, even as the U.S. overall lacks a formal AI strategy. Meanwhile, China introduced its plan last year: it’s aiming to be No. 1 in AI innovation by 2030. “Double the AI research budget would be a good start, given that all other countries are so much farther behind U.S., and we’re looking for the next breakthrough in AI,” said Lee. Doubling funding could double the chances that the next big AI achievement will be made in the U.S 2018-09-29 00:00:00 Read the full story.

In The New AI World Order, China Will Soon Surpass US In R&D Investment – Kai-Fu Lee

For years, China vs US in AI has been a hotly contested topic with the matter taking on a geopolitical slant with national security coming in the center of debate. However, a new report published in IEEE Spectrum, Kai-Fu Lee, noted venture capitalist reiterated how in the new world order governed by AI, the real crisis will emerge from automation that wipes out whole job sectors, reshaping economies and societies in both nations. In a similar vein, AI exponent Andrew Ng discussed how in the coming years, there will be lots of cases where humans and machines will work side by side but the technology will also whitewash jobs and displace human labour. Now Lee, whose new book — AI Superpowers: China, Silicon Valley, and the New World Order dwells on the world’s two AI titans explores the AI capabilities of China and the US and the battle for global dominance. But most importantly, it frames a positive rhetoric for China, which is slowly and surely edging closer to AI supremacy, thanks to the “speed, capital, China’s entrepreneurism and access to large amount of data”. 2018-09-27 10:43:28+00:00 Read the full story. CloudQuant Thoughts… Mr Lee’s book tour is certainly getting him a lot of coverage. “It’s not China that is taking away the academic leaders; it’s the corporates,” Lee said. Is this a bad thing? We certainly need educators to teach the next generation but the US also needs to stay ahead of China in this most vital of fields. There is no doubt that Britain’s embrace of the scientific method, its scientific discoveries and the application of those discoveries to its industries (only ever so slightly ahead of its European competitors) was key to its domination of the world during the 16th to early 18th centuries. The US’s distance from WWII  meant it was ideally placed to help to re-build the devastated nations. That plus the collection of German rocket technology gave it the financial and scientific upper-hand during the latter half of the 20th century. AI and ML certainly seem to be the keys to power in the coming century. The Chinese government does have access to huge amounts of data on its citizens. US Corporations famed ability to be nimble and capable of taking advantage new technology may be the overall advantage.  

Orchestrating data analytics to enhance the investor experience

The amount of data is growing exponentially. According to IDC, there were 16.3 zettabytes of information generated in 2017 alone; one zettabyte is 1 billion terabytes. However you cut it, that’s a huge number. One that is too large to comprehend. In simplistic terms, according to one industry professional “if every piece of data were a penny, it would cover the earth’s surface five times over”. Indeed, with Amazon and Apple both hitting the trillion dollar market cap mark, and Alphabet and Microsoft sitting at over USD900 billion, it is clear that the stock market values data as the most valuable resource, not oil or consumer products. Against this growing tsunami, investment managers and service providers alike are looking for ways to ingest and make sense of it all. To find information that they can translate into insights and turn into knowledge, that if done correctly, could lead to improved business performance and enriched customer relationships. 2018-10-01 08:36:07+01:00 Read the full story. CloudQuant Thoughts… An extremely interesting and detailed look into the role of Big Data, AI and ML at Investment Funds today .  

What is Inside the Black Box of Artificial Intelligence? XAI – Explainable AI

Artificial Intelligence (AI) is surprising the mankind with newer and awe-inspiring outcomes. But with the surprises comes the concern of trust. There is a gripping ‘black box’ problem with artificial intelligence, if people don’t know how AI comes up with its outcomes and insights, they seldom trust the technology. The lack of trust can be attributed to the not so successful AI efforts for IBM, Watson for Oncology in particular. If the doctors knew how the platform Watson for Oncology came to its conclusions, the adoption rate would have been different. Since an interaction with something which is not understood can cause an anxiety and make the mind feel that it is losing control and hence not trust on that same. It comes as no surprise that the US Department of Defence (DoD) is investing in Explainable AI (XAI) which will be very essential to understand the future war-fighters and trust the emerging generation of artificially intelligent machine partners. Efforts into XAI might soon lead new machine learning systems to explain their rationale, convey an understanding of how they behave in the future and characterize their strengths and weaknesses. 2018-09-26 00:01:48+05:30 Read the full story. CloudQuant Thoughts… In our role at CloudQuant.com, writing AI and ML based models and setting them free on the market is simply not an option, one has to be able to understand “Why” the model is making its decisions. It appears this clarity is equally important in the medical and defense fields. Trust and accountability.  

Why Did I Reject a Data Scientist Job? – Towards Data Science

Before diving in to tell you why I rejected a data scientist job, let us take a step back and try to answer another question — Why become a data scientist? Chances are you may have heard of the profession — Data Scientist was labeled by Harvard Business Review as the sexiest job of the 21st century and has been chosen as the best job in America, three years in a row according to Glassdoor. And more recently, IBM predicted demand for data scientists will soar 28% by 2020. All these attractive job prospects seem to point to a single direction where many people want to go after — and we all know — for some good reasons. 2018-09-30 11:43:25.416000+00:00 Read the full story. CloudQuant Thoughts… Not surprisingly, most companies do not even know what they are looking for. Is it a Research Scientist, a Data Scientist, a Programmer, a Business Analyst? All of the above?  

12 TED talks on Artificial Intelligence

 
  1. Daniel Dewey – The Long Term Future of AI
  2. Ray Kurzweil – How to create a mind
  3. Sam Spaulding – How AI is changing the way we view intelligence
  4. Henry Markram – A Brain in a SuperComputer
  5. George John – The Age of Artificial Intelligence
  6. Peter Bock – Emergence of Creativity in AI
  7. Hod Lipson – Robots that are “Self Aware”
  8. Boris Sofman – AI
  9. Alex Wissner – An equation for Intelligence
  10. Eric Horvitz – Making Friends with Artificial Intelligence
  11. Rudd Mattjeij – Artificial Intelligence
  12. Robin Hanson – The Next Great Era – Envisioning a Robot Society
2018-09-25 12:00:59+00:00 Read the full story.  

ML Powers Discovery In GE’s 500 PB* Lake

Like most Fortune 50 firms, General Electric relies on an abundance of computer systems to power its enterprise. And like most firms that size, synching up and aligning the data emitted by different systems is a major challenge. But thanks to an innovative data discovery solution powered by machine learning, GE found a solution. GE’s Hadoop-based data lake contains 500 PB of data that originated from about 120 different systems. Data is sourced from a variety of ERP packages, accounting systems, and other applications, such as Ariba, Concur, and Salesforce.com. Even LinkedIn and Twitter data makes it into the lake for downstream sentiment analysis. Getting actual value out of the data is a much tougher challenge. “Ingesting the data is the easiest part, I have the data from all of the sources. But now I have 130,000 entities in my data. Now, how do you identify all the relationships in all those entities?” There are things that customers know about their data, and there are things they don’t. “If you ingest so much data, you are ingesting to find insights in the data that you don’t know of,” Diwakar Goel continued. “That’s where you cannot rely on manual techniques to identify it and give you those insights. That’s why you use machine learning.” GE found a potential solution to the problem in Io-Tahoe, a New York City-based data management startup that emerged from Centrica, a £28-billion company that owns British Gas and other subsidiaries. Io-Tahoe has developed a data discovery tool that uses patent-pending machine learning technology to determine the relationships between disparate pieces of data. 2018-09-25 00:00:00 Read the full story. CloudQuant Thoughts… First… PB* –  A petabyte (PB) is 1,000 terabytes (TB) or 1,000,000 gigabytes (GB). GE is an enormous company and as such is one of the first to be dealing with this kind of volume of data.   Favorite Quote from this Week’s blogs… “The job of the data scientist is to ask the right questions. If I ask a question like ‘how many clicks did this link get?’ which is something we look at all the time, that’s not a data science question. It’s an analytics question. If I ask a question like, ‘based on the previous history of links on this publisher’s site, can I predict how many people from France will read this in the next three hours?’ that’s more of a data science question.” ―Hilary Mason, Founder, Fast Forward Labs.  
 

Cleaning and Preparing Data in Python – Towards Data Science

That boring part of every data scientist’s work

Data Science sounds like something cool and awesome. It’s pictured as something cool and awesome. It is a sexiest job of 21st century as we all know (I won’t even add the link to that article :D). All the cool terms are related to this field — Machine Learning, Deep Learning, AI, Neural Networks, algorithms, models… But all this is just a top of an iceberg. 70–80% of our work is data preprocessing, data cleaning, data transformation, data reprocessing — all these boring steps to make our data suitable for the model that will make some modern magic. And today I would like to list all the methods and functions that can help us to clean and prepare the data. So what can be wrong with our data? A lot of things actually… 2018-09-30 23:23:17.782000+00:00 Read the full story.  

Data And The Cloud: The Modern 21st Century Lawyer

We can then analyze digitized content using a variety of automated tools and platforms, which are powered by AI and machine learning technologies. The beauty of machine learning is that we can use it to build a model or algorithm, then continuously improve it over time. So, you start with a good baseline, but it only gets better and more accurate from there. That explains why the demand for tech-savvy attorneys and lawyers has grown considerably in recent years. 2018-09-27 23:18:49+00:00 Read the full story.  

Amazon scientist explains how Alexa resolves ambiguous requests

In a blog post today, Vishal Naik explained how Alexa leverages multiple neural networks — layered math functions that loosely mimic the human brain’s physiology — to resolve ambiguous requests. The work is also detailed in a paper (“Context Aware Conversational Understanding for Intelligent Agents with a Screen“) that was presented earlier this year at the Association for the Advancement of Artificial Intelligence. “If a customer says, ‘Alexa, play Harry Potter,’ the Echo Show screen could display separate graphics representing a Harry Potter audiobook, a movie, and a soundtrack,” he explained. “If the customer follows up by saying ‘the last one,’ the system must determine whether that means the last item in the on-screen list, the last Harry Potter movie, or something else.” 2018-09-28 00:00:00 Read the full story.  

Adobe Relaunches CX Platform

Adobe, Microsoft, and SAP founded the Open Data Initiative, an alliance that aims to make data silos a complete thing of the past. The Open Data Initiative promotes the development of tools that provide a seamless flow of customer data; everything from behavioral and transactional to financial and operational data comes together with one data model, making it possible to have a comprehensive, real-time view of customer data across multiple devices. Adobe Customer Experience (CX) Platform uses data science plus Adobe’s Sensei analytics artificial intelligence engine to synthesize all customer data in one place. So Adobe has quite a bit of investment in this whole initiative. New Virtual Analyst: Powered by Adobe Sensei, deep AI and machine learning will enable enterprises to unlock more value in the mountains of data they have. The AI will break out critical insights buried deep in data–without the user even having to ask. The cognitive analyst will get more intelligent over time as it learns from user behaviors and delivers more relevant insights. 2018-09-26 00:00:00 Read the full story.  

Microsoft teams up with Adobe and SAP on Open Data Initiative to link data across their products

Modern enterprise technology generates a ton of valuable data, but putting that data together can be quite tricky. Microsoft introduced a new industry partnership with Adobe and SAP Monday at Ignite 2018 that aims to bring customer data together into one package running on Azure. 2018-09-24 14:11:07-07:00 Read the full story.  

Volkswagen strikes deal with Microsoft to build cars connected to the cloud

Volkswagen and Microsoft have struck a deal that will see the car-maker embed internet services into its vehicles with the software giant’s cloud technology, the companies announced Friday.The partnership has been set up to create the “Volkswagen Automotive Cloud”, which will see all future digital services in Volkswagen’s cars facilitated by Microsoft Azure, a cloud computing service. From 2020, Volkswagen will integrate more than 5 million Volkswagen-branded vehicles per year to an internet of things network. The move forms part of a wider $4bn push from the German company to boost its digital transformation by 2025.Volkswagen is taking steps for a digital transformation that it hopes will serve as the technological basis for an “industrial automotive cloud”. 2018-09-28 00:00:00 Read the full story.  

Weekly Selection — Sep 28, 2018 – Towards Data Science

 
  • Wikipedia Data Science: Working with the World’s Largest Encyclopedia
  • Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
  • Neural Networks to Predict the Market
  • 5 Reasons why Businesses Struggle to Adopt Deep Learning
  • How to rapidly test dozens of deep learning models in Python
  • Convolutional Neural Networks for Beginners: Practical Guide with Python and Keras
  • Multi-Class Text Classification Model Comparison and Selection
  • Here’s how you can get a 2–6x speed-up on your data pre-processing with Python
  • Why do I Call Myself a Data Scientist?
  • See Robot Play: an exploration of curiosity in humans and machines
2018-09-28 12:53:12.232000+00:00 Read the full story.  

Enriching customer relationships in financial services: the role of automation

Since every major step forward in process efficiencies, in any industry, entails a moral consideration, it’s no surprise that there may be some hesitation around its adoption in the financial services sector. My colleague, Jason Bell, recently discussed the pressing need for banks to transform into technology companies in his blog, ‘The opportunity no bank can ignore’. Jason discussed the emphasis often given to the year 2020 as a watershed year, pointing out how close it is. It brings many observations about baking futures into sharp focus, such as this observation from the Accenture article Intelligent Automation in Financial Services: “70% of financial services executives believe artificial intelligence will completely or significantly change their organization by 2020.” Without doubt, Artificial Intelligence (AI) and Robotic Process Automation (RPA) are powerful efficiency drivers essential to the forward momentum of digital transformation. The moral side-bar is the impact these technologies may have on jobs. In 2013, an Oxford University research paper, The Future of Employment: How susceptible are jobs to computerisation?, predicted that up to 47% of workers in the US economy were at a high risk of being replaced by robots in the medium term. 2018-09-27 16:50:21 Read the full story.  

Bringing the power of Windows 10 to the Robot Operating System – Windows Experience Blog

People have always been fascinated by robots. Today advanced robots are complementing our lives, both at work and at home. Warehouse robots have enabled next-day deliveries to online shoppers, and many pet owners rely on robotic vacuums to keep their floors clean. Industries seeing benefits from robots are as diverse as manufacturing, transportation, healthcare and real estate. As robots have advanced, so have the development tools. We see robotics with artificial intelligence as universally accessible technology to augment human abilities. Windows has been a trusted partner of robotic and industrial systems for decades. With ROS for Windows, developers will be able to use the familiar Visual Studio toolset along with rich AI and cloud features. We’re looking forward to bringing the intelligent edge to robotics by bringing advanced features like hardware-accelerated Windows Machine Learning, computer vision, Azure Cognitive Services, Azure IoT cloud services, and other Microsoft technologies to home, education, commercial, and industrial robots. 2018-09-28 00:00:00 Read the full story.  

Microsoft brings Robot Operating System to Windows 10

In recent years, the robotics industry has experienced outsized growth. It’s expected to be worth almost $500 billion by 2025, and judging by recent funding rounds, investors are optimistic about the future. Warehouse robotics company GreyOrange raised $140 million for its platform in early September; in June, Bossa Nova scooped up $29 million in July for its store inventory robots and Starship Technologies secured $25 million for its fleet of automated delivery carts. One thing many of those startups’ machines share in common is Robot Operating System (ROS), open source robotics middleware originated by Willow Garage and Stanford’s Artificial Intelligence Laboratory that provides low-level device control, hardware abstraction, and other useful services. Previously, ROS was experimentally supported on Windows by the community. (As of September 2018, Core ROS had been ported to Windows.) But today, Microsoft debuted an official — albeit “experimental” — build for Windows 10. 2018-09-28 00:00:00 Read the full story.  

University spin-out raises cash for tech to help robots avoid crashes

A company spun out of Imperial College has raised $5m (£3.8m) from Amadeus Capital Partners and other investors to build more advanced navigation systems for drones and robots. SLAMcore develops artificial intelligence (AI) technology for robots to help prevent crashes. Its system – known as simultaneous localisation and mapping (SLAM) – allows robots to understand and navigate unfamiliar surroundings such as inside buildings or dense urban areas. Nearly all autonomous vehicles or robots, from driverless cars to robot vacuums, use SLAM technology in some way, and a huge variety of different systems are being developed by big and small technology companies. BIS Research expects the global market for SLAM technology to be worth more than $8bn by 2027. Owen Nicholson, chief executive, said: “We’re really trying to democratise robots. It shouldn’t be the reserve of just a handful of tech giants because that’s not good for innovation.”. SLAMcore’s technology would allow robotics companies to “get on with coming up with crazy new ideas”. 2018-09-27 00:00:00 Read the full story.  

RBC Capital and Orbital Insight Ink Alt Data Deal

Orbital Insight, the leader in geospatial analytics, and RBC Capital Markets, the corporate and investment banking arm of Royal Bank of Canada (RBC), today announced a global partnership giving RBC Capital Markets access to Orbital Insight’s Consumer and Energy analytics products. RBC Capital Markets will use this data to further advance its equity research products, taking advantage of the timeliness, objectivity and scale of geospatial analytics. By using artificial intelligence to automatically analyze data like satellite imagery, Orbital Insight is able to detect and track changes on the ground over time. Signals monitored include retailer parking lot car counts and crude oil storage tanks, among others. 2018-09-28 18:07:28+00:00 Read the full story.  

Why Convolutional Neural Networks Are The Go-To Models In Deep Learning

Over the years, research on convolutional neural networks (CNNs) has progressed rapidly, however the real-world deployment of these models is often limited by computing resources and memory constraints. What has also led to extensive research in ConvNets is the accuracy on difficult classification tasks that require understanding abstract concepts in images. Another reason why CNN are hugely popular is because of their architecture — the best thing is there is no need of feature extraction. The system learns to do feature extraction and the core concept of CNN is, it uses convolution of image and filters to generate invariant features which are passed on to the next layer. The features in next layer are convoluted with different filters to generate more invariant and abstract features and the process continues till one gets final feature / output (let say face of X) which is invariant to occlusions. But one of the reasons why researchers are excited about deep learning is the potential for the model to learn useful features from raw data. Now, convolutional neural networks can extract informative features from images, eliminating the need of traditional manual image processing methods. 2018-09-25 09:25:09+00:00 Read the full story.  

Instant Data Science

If you have skimmed through the high tech job listings lately, you’ve surely noticed the demand for data scientists. Although there is no consensus on the exact job definition, many companies want data experts and are willing to pay a high price for the right candidates. This article explores why good data scientists are hard to find, and explains how the shortage may soon be alleviated thanks to… data science! 2018-09-27 00:00:00 Read the full story.  

Daring to Head Towards an AI-Powered World

Some of us perceive Artificial Intelligence as the technology of a haunting future, just as projected in science fiction movies, The Matrix or Ex Machina. In the real world, it is changing lives in brilliant ways, beyond areas we dare to imagine. AI has taken over our world. Of course not in an apocalyptic way, because let’s be realistic, even thinking this is ridiculous! Most people do not realize, even today, when they interact with an AI. For example, with chat bots. I once had a friend ask me why do local businesses invest in 24/7 customer chat teams for websites. 2018-09-28 12:31:15.398000+00:00 Read the full story.  

Data Warehouses, Data Marts, Operational Data Stores, and Data Lakes: What’s in a Name?

Many organizations nowadays are struggling with finding the appropriate data stores for their data. Let’s zoom in on some key data structures to facilitate corporate decision making by means of business intelligence. More specifically, let’s look at data warehouses, data marts, operational data stores, data lakes, and their differences and similarities. 2018-09-26 00:00:00 Read the full story.  

Alexa, Tell Me About My Advisory Business

digital financial services, have a new tool to call upon when it comes to tracking their business metrics. Envestnet Intelligence for Financial Institutions, a new data intelligence tool, was announced Tuesday at FinovateFall 2018 in New York. Machine learning and natural language processing—two of the most commonly employed technology subsets within the field of artificial intelligence—are being applied to the massive amounts of business data Envestnet collects from, and on behalf of, its institutional customers. A homegrown data analytics engine then powers the platform, which can be accessed from a user’s desktop, mobile and Amazon Alexa-enabled devices. It’s not the first time Envestnet has voice-enabled one of its products with Alexa; the firm added this feature to its Envision IQ product in May. 2018-09-26 15:52:04-04:00 Read the full story.  

How Edge Computing Enables Real-Time Decision Making (Video) – Will Ochandarena

As more and more companies try to implement use cases that need to do things in real time, make decisions in real-time, collect in real-time, show dashboards in real-time, organizations have to build an architecture that’s able to collect data in real-time, said Will Ochandarena, senior director, product management, MapR Technologies during Data Summit 2018. “We’re talking every week with different oil companies and factories that are trying to improve their yields, productivity, and quality of their product output and it’s architectures like this that make it possible,” Ochandarena said. 2018-09-25 00:00:00 Read the full story.  

Data Science Hacks No One Talks About But Are A Must In Your Toolkit

With the data revolution in full swing, there is more information on the internet than a human can remember and process in his/her lifetime. Data Science is a demanding platform, where every forward looking enterprise and startup wants to increase their productivity with the help of intelligent systems. It is an interdisciplinary platform that involves numerous techniques and skills such as, analysis, programming, math and statistics. Now, it is commonly believed that a person with a hacker mindset can come up with an easier solution compared to an orthodox approach. Let us look at some of the little-known hacks in data science field which aren’t extensively talked about.
  • Hacker Mindset
  • Data Cleaning Tricks
  • Domain Knowledge
  • Never say “No More Learning”
  • Cheat SheetsHacker Mindset
  • Data Cleaning Tricks
  • Domain Knowledge
  • Never say “No More Learning”
  • Cheat Sheets
2018-09-25 05:25:19+00:00 Read the full story.  

This Mumbai Startup Is Attempting To Introduce Level 2 Autonomy In India With Its Innovative Solution

AI-powered autonomous technology is poised to see enormous growth over the coming years, however tech development is mostly concentrated in North America and China. In a first, Pinaki Laskar, co-founder of Mumbai-based, Fisheyebox, has developed Drivex.AI — dubbed as an all-in-one-solution for an intelligent drive system. In an exclusive by Express Computer, Laskar talked about the Project DriveX that can interface with automotive standard drive-by-wire platform, and turns it into a driverless unit. The news further stated that the startup has also successfully tested its autonomous technology in Maruti Celerio. 2018-09-28 11:45:12+00:00 Read the full story.  

Managing a Data Warehouse in the Cloud: 5 Challenges

One of the most important shifts in data warehousing in recent times has been the emergence of the cloud data warehouse. Previously, setting up a data warehouse required a huge investment in IT resources to build and manage a specially designed on-premise data center. Now, several cloud computing vendors offer data warehousing functions as a service (DWaaS), accessible via an Internet connection. This model negates the costly capital expenditure and management required for an on-premise data warehouse. The availability of cloud data warehouses makes data warehousing much more accessible to a wider range of companies. However, before you go rushing into choosing a vendor and getting set up, first understand that managing a data warehouse in the cloud presents a whole new set of challenges, regardless of whether you’ve managed an on-premise setup before. Five of the main challenges and some recommended solutions are outlined below to help you better prepare for managing a data warehouse in the cloud. 2018-09-26 00:00:00 Read the full story.  

Why You Need to Trust Your Data

There’s something that often gets lost in discussions about artificial intelligence and advanced analytics: the importance of the data. Having good, clean data is absolutely essential, but all too often, companies lack trust in that critical resource, which can lead business leaders to make bad decisions or resort to following gut instincts. No matter how good your analytics are, you’re not getting anywhere if you can’t trust your data. The goal for many companies when constructing predictive analytics is to get the data as clean as possible, a process that studies show can consume up to 80% of a data scientist’s time. But even if the data is 100% true, your model may give the wrong predictions if the data doesn’t accurately reflect the thing you’re trying to predict. 2018-09-26 00:00:00 Read the full story.  

Is Artificial Intelligence Replacing Jobs In Banking?

Over the past 12 months, the banking industry has become increasingly excited about AI. Virtually every leading consultancy has published research on the impact AI will have on the sector and investment continues to pour into developing innovative solutions. But, alongside all the buzz comes the inevitable concern that the implementation of this technology will reduce the need for actual human workers. 2018-09-26 00:00:00 Read the full story.  

10 must watch movies on Data Science and Machine Learning

Data science and machine learning are powerful technologies innovating the world in ways that sometimes seem straight out of a sci-fi film. Today’s machines are not just capable of tedious tasks, but also using complex mathematics to figure out how to chart a path for a rocket to follow or making weather predictions based on historical data. What better platform to explore the magic of data science and machine learning than film? We’ve rounded up 10 of the best data movies: 2018-10-01 15:52:23+00:00 Read the full story.  

Microsoft commits $40M over 5 years to AI for Humanitarian Action initiative

Microsoft is investing in a new program employing its vast artificial intelligence resources to deal with humanitarian crisis. Set against the backdrop of Hurricane Florence pummeling the Carolinas last week, Microsoft announced in conjunction with the United Nations General Assembly meeting on Monday morning that it is pledging $40 million over five years for a new AI for Humanitarian Action initiative. The program will focus on using AI to aid in four areas: disaster recovery, children’s needs, protecting refugees and displaced people and human rights. 2018-09-24 13:00:31-07:00 Read the full story.  

The State of Machine Learning in Business Today

Artificial Intelligence (AI), Machine Learning, and Deep Learning are all topics of considerable interest in news articles and industry discussions these days. However, to the average person or to senior business executives and CEO’s, it becomes increasingly difficult to parse out the technical differences which distinguish these capabilities. Business executives want to understand whether a technology or algorithmic approach is going to improve business, provide for better customer experience, and generate operational efficiencies such as speed, cost savings, and greater precision. Authors Barry Libert and Megan Beck have recently astutely observed that Machine Learning is a Moneyball Moment for Companies. I met last week with Ben Lorica, Chief Data Scientist at O’Reilly Media, and a co-host of the annual O’Reilly Strata Data and AI Conferences. O’Reilly recently published their latest study, The State of Machine Learning Adoption in the Enterprise. Noting that “machine learning has become more widely adopted by business”, O’Reilly sought to understand the state of industry deployments on machine learning capabilities, finding that 49% of organizations reported they were exploring or “just looking” into deploying machine learning, while a slight majority of 51% claimed to be early adopters (36%) or sophisticated users (15%). 2018-09-27 09:08:59+00:00 Read the full story.  

Privacy at an inflection point: Why the time has come for meaningful U.S. regulation

As the privacy bus teeters at the edge of a steep road, the U.S. Congress and President seem to be asleep at the wheel. While we witness fiery rhetoric at televised hearings featuring high tech CEO’s and although a few members of Congress have put forth credible proposals to protect personal data, very little actual progress has been made to date on concrete consumer protection legislation. This paralysis at the federal level benefits neither companies nor consumers, as the time has come to craft new laws for an economy increasingly driven by data profiling and artificial intelligence. 2018-09-28 13:00:20-07:00 Read the full story.  

7 examples of Big Data Retail Personalization

Big data is a top trending buzzword. But, unlike overused buzzwords such as ‘omnichannel marketing’ or ‘growth hacking’, big data is very underhyped. According to IBM, 62% of retailers report that the use of big data is giving them a serious competitive advantage. Knowing what your customer wants and when they want it can be available at your fingertips with big data; all you need are the right tools and processes in place to make use of it. Let’s explore 7 innovative examples of big data personalization in retail for some inspiration. 2018-09-26 11:00:05+00:00 Read the full story.  

The Power of Personalization: Cramer’s ‘Mad Money’ Recap (Thursday 9/27/18)

Investing is all about the search for great stories, Jim Cramer told his Mad Money viewers on Thursday. But a good theme is not enough, you also need to invest at the right time. That’s why Cramer regularly visits Silicon Valley, to hear first-hand what the next big themes will be. This week, Cramer’s in San Francisco for the Dreamforce event, and visiting companies and CEOs in Silicon Valley. The biggest theme this trip? Personalization. Companies that know who their customers are and what they want before they do are able to dominate their industries, Cramer said, and the companies providing those companies with the tools to personalize are the winning investments. 2018-09-27 22:39:58-04:00 Read the full story.    

Assessing Annotator Disagreements in Python to Build a Robust Dataset for Machine Learning

  Having recently completed an MSc in Data Analytics and then working closely with a charity in machine learning, what is only now striking me as troubling about the state of formal education is the distinct under-focus on the bottom three building blocks, and an over-focus on the top two blocks. Despite it being troubling, the reason is clear: students aren’t attracted to the gritty pre-machine learning phase. But the fact remains that without an understanding of how to work on what might be less exciting, it simply won’t be possible to build a robust, strong, and reliable machine learning product. (VIDEO LINK) 2018-09-29 19:21:18.535000+00:00 Read the full story.  

NHS to trial Uber-style location service to match up patients, porters and equipment

The NHS is to trial a new Uber-style geolocation service to match up patients, porters and equipment to get people around hospitals more quickly. Currently, the complexity and vastness of hospital complexes make it difficult to match up porters with patients, meaning they are often left waiting to be taken to x-rays or theatre. But the new system designed by the University of Oxford and tech company Navenio Ltd, will make it easy to locate staff and match them to nearby patients, in the same way as the taxi app. It is hoped the project will increase staff productivity to 96 per cent and cut costs by up to 35 per cent and will be initially tested at hospitals in the Oxford area. 2018-09-28 00:00:00 Read the full story.  

Why tech VCs invest in people, not ideas

Venture capitalists are regarded in Silicon Valley as the heroes of many of technology’s startup successes, bringing in money and expertise when entrepreneurs needed to develop original ideas or expand into larger markets. But it still comes down most of all to the entrepreneurs more than their ideas. “What I’m really looking for is in investing more in the people than in the idea, because startups can always pivot,” said Patrick O’Reilly. “On the developer operations side, I’m looking for people using AI to get away with repetitive tasks,” O’Reilly said. “I would love to see someone have a system where it’s like, ‘Hey, we’ve noticed 90 times this week this guy’s done this exact same thing, 99 percent the same way.’ Let’s automate that away. We’ve been really good in the space to treat infrastructure like code, and be able to tear things up.” 2018-09-27 00:00:00 Read the full story.    

The Day Of The Machine Is Here: Are We Human Enough To Seize It?

You’re an executive, an employer, or maybe an up-and-comer in your organization, but either way, you have surveyed the moment and you know that big things are poised to happen. Unlike the big things of the past — the internet or mobile — the change won’t be the result of consumers embracing a shiny new object or buying into a piece of Apple hardware that other manufacturers will swiftly imitate. The change that’s coming will be more diffuse, more comprehensive, and at once harder to see coming in specifics yet impossible to miss in general. 2018-09-27 17:36:22+00:00 Read the full story.  

First Fully Automated Indoor Farm Being Built In Ohio

The next time you shop for cherry tomatoes at Whole Foods or another retailer, you may end up buying some grown in an indoor, controlled environment outfitted with the latest robotic technology. Ohio will get the first fully automated indoor farm in the United States. 80 Acres Farms plans to build one in Hamilton, a suburb of Cincinnati, by the end of the year. The farm will have grow centers for greens, such as herbs and kale, and will supply produce to multiple retailers and distributors. The indoor farm in Hamilton will include artificial intelligence, robotics, sensors and other tools to monitor the produce around the clock. 2018-09-25 00:00:00 Read the full story.  
Behind a Paywall or Registration page…

UK data science can lead a global privacy-first mantra

Privacy breaches present a repeating alarm we can’t ignore. On Tuesday, the day before major US-based technology companies such as Apple and Twitter testified at the Senate, Facebook discovered a breach that affected 50m users’ accounts and their linked applications. While the Facebook news wasn’t announced until Friday, the purpose of the Senate hearing was acutely apt. How do the major tech firms exercise their vast power in a way that protect… 2018-09-30 00:00:00 Read the full story.  

Big Data, the Next Generation: Faster, Easier, Smarter

A lot has happened since the term “big data” swept the business world off its feet as the next frontier for innovation, competition and productivity. Hadoop and NoSQL are now household names, Spark is moving towards the mainstream, machine learning is gaining traction and the use of cloud services is exploding everywhere. However, plenty of challenges remain for organizations embarking upon digital transformation, from the demand for real-time data and analysis, to need for smarter data governance and security approaches. Download this new report today for the latest technologies and strategies to become an insights-driven enterprise. 2018-09-26 00:00:00 Read the full story.  
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AI & Machine Learning News. 25, September 2018

Cboe turns to machines to police VIX; OCC’s capital plan; Chinese copper options

Tilray options on fire as pot-stock frenzy grips market.
The craziest stat from that article was “For the week so far, the combined trading volume for Tilray and Cronos Group options was roughly 680,000 contracts, or about 2.7 percent of overall U.S. single stock options activity, Trade Alert data showed.” One thing to keep in mind about Tilray in particular is that its float is only 17.8 million shares and there are a lot of shorts. Yesterday’s volume exceeded its float by nearly 14 million shares. 2018-09-20 20:47:11+00:00 Read the full story.   CloudQuant Thoughts… Although we picked this blogpost up because of its reference to the CBOE’s intent to use AI to police VIX, it was the opening opinion piece that caught my eye. TLRY has been THE symbol of the last couple of months since its IPO, and it is not hard to see why… Even those of you more involved in AI than the stock market can see the opportunity here. You can see the run up, the exhaustion, the capitulation. If you feel you have the technical skills to spot these movements algorithmically (ie via your math skills or programming skills) then head over to CloudQuant and show us. Success in the stock market is not about being 100% or 90% or even 80% right, it is more about pushing the odds slightly but consistently in your direction. These opportunities present themselves daily, maybe not to the extent of a TILRAY but there is lots of opportunity out there.  

How to Customize JupyterLab Keyboard Shortcuts

I often find myself wanting to restart and run all cells, so I wanted to make a keyboard shortcut. I made this tutorial to help other folks out, because it’s not a one-click-selection kind of thing. JupyterLab is like Jupyter Notebook on anabolic steroids — without the increased cancer risk and other negative side effects. JupyterLab is billed as the next generation UI for Project Jupyter. It was re… 2018-09-24 02:51:32.591000+00:00 Read the full story. CloudQuant Thoughts… Not only do we agree that Jupyter Labs is a huge improvement over Jupyter Notebooks and well worth seeking out but it is also at the heart of our new CloudQuant (soon to be released to the public) system, so it is definitely worth boning up on these useful keyboard shortcuts!  

Making drones work for you

The technical name for a drone is Unmanned Aerial Vehicle (UAV). A UAV can be either remotely piloted or it can be an autonomous vehicle. The history of drones dates as far back as 1849. Austrians equipped balloons with bombs that had timing devices on their fuses. The intended target was Venice. The wind was not correctly accounted for, however. As many bombs blew back across Austrian lines as there were exploded over Venice. Amazon (AMZN) has toyed with the use of UAVs for delivery of its packages. And the availability of recreational drones to hobbyists has become so widespread that air safety has become a big question. What are the stocks that are expected to grow with the advance of UAVs? 2018-09-21 00:00:00 Read the full story. CloudQuant Thoughts… I occasionally receive inquiries from users along the lines of “why this symbol is in this list on this date?” and I try to point out to them that they are being too precise with their model analysis. There will always be disagreements, noise and errors in big data. Your best chance to stand out is to not go with pre-defined lists but create your own, whether that be based on your own measure of how volatile a stock is likely to be, or creating your own unique industry/sector list. We see NVidea (NVDA) popping up with regard to just about every new technological breakthrough.. Automated Cars, AI server farms and now here in the automated drone environment. Whilst the automated drone sector may be a little risky, the likes of NVDA may be less so. Always be vigilant for trading opportunities, then build a model at CloudQuant and if you are right, we can help you turn that observation into $$$.  

How we used AI to translate sign language in real time

Imagine a world where anyone can communicate using sign language over video. Inspired by this vision, some of our engineering team decided to bring this idea to HealthHack 2018. In less than 48 hours and using the power of artificial intelligence, their team was able to produce a working prototype which translated signs from the Auslan alphabet to English text in real time. People who are hearing impaired are left behind in video consultations. Our customers tell us that, because they can’t sign themselves, they have to use basic text chat to hold their consults with hearing-impaired patients – a less than ideal solution. With the growing adoption of telehealth, deaf people need to be able to communicate naturally with their healthcare network, regardless of whether the practitioner knows sign language. Achieving universal sign language translation is no easy feat. The dynamic nature of natural sign language makes it a hard task for computers, not to mention the fact that there are over 200 dialects of sign language worldwide. Speakers of American Sign Language (ASL) have been fortunate in that a number of startups and research projects are dedicated to translating ASL in real time. In Australia however, where Auslan is the national sign language, speakers have not been so fortunate, and there is next to no work being done for the Auslan community. We thought we might be able to help. 2018-09-21 00:00:00 Read the full story. CloudQuant Thoughts… As AI and ML become more ubiquitous and easy to use it will begin to knock out these edge cases for the greater good.  

Wikipedia Data Science: Working with the World’s Largest Encyclopedia in Python

Wikipedia is one of modern humanity’s most impressive creations. Who would have thought that in just a few years, anonymous contributors working for free could create the greatest source of online knowledge the world has ever seen? Not only is Wikipedia the best place to get information for writing your college papers, but it’s also an extremely rich source of data that can fuel numerous data science projects from natural language processing to supervised machine learning. The size of Wikipedia makes it both the world’s largest encyclopedia and slightly intimidating to work with. However, size is not an issue with the right tools, and in this article, we’ll walk through how we can programmatically download and parse through all of the English language Wikipedia. 2018-09-23 15:25:36.733000+00:00 Read the full story. CloudQuant Thoughts… Big Data does not come much bigger than Wikipedia. Have a read through William Koehrsen’s algorithmic journey through the modern digital encyclopedia.  

Building A Deep Learning Model using Keras in Python

Deep learning is an increasingly popular subset of machine learning. Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. Then the model spits out a prediction. The weights are adjusted to find patterns in order to make better predictions. The user does not need to specify what patterns to look for — the neural network learns on its own. Keras is a user-friendly neural network library written in Python. In this tutorial, I will go over two deep learning models using Keras: one for regression and one for classification. We will build a regression model to predict an employee’s wage per hour, and we will build a classification model to predict whether or not a patient has diabetes. 2018-09-17 08:02:21.849000+00:00 Read the full story. CloudQuant Thoughts… Another nice introductory post that does not lean too heavily into the math but explains by example how to process wage data to build a regression model and diabetes data to build a classification model.  

From Scratch: AI Balancing Act in 50 Lines of Python

Hi everyone! Today I want to show how in 50 lines of Python, we can teach a machine to balance a pole! We’ll be using the standard OpenAI Gym as our testing environment, and be creating our agent with nothing but numpy. The cart pole problem is where we have to push the cart left and right to balance a pole on top of it. It’s similar to balancing a pencil vertically on our finger tip, except in 1 dimension (quite challenging!). You can check out the final demo on repl.it here before we get started.  If this is your first time in machine learning or reinforcement learning, I’ll cover some basics here so you’ll have grounding on the terms we’ll be using here. Reinforcement learning (RL) is the field of study delving in teaching agents (our algorithm/machine) to perform certain tasks/actions without explicitly telling it how to do so. Think of it as a baby, moving it’s legs in a random fashion; by luck if the baby stands upright, we hand it a candy/reward. 2018-09-18  Read the full story. CloudQuant Thoughts… A nice introduction to Re-inforcement Learning (RL) in Python.  

Semantic Segmentation with Deep Learning: A guide and code

Most people in the deep learning and computer vision communities understand what image classification is: we want our model to tell us what single object or scene is present in the image. Classification is very coarse and high-level.  Many are also familiar with object detection, where we try to locate and classify multiple objects within the image, by drawing bounding boxes around them and then classifying what’s in the box. Detection is mid-level, where we have some pretty useful and detailed information, but it’s still a bit rough since we’re only drawing bounding boxes and don’t really get an accurate idea of object shape.

Semantic Segmentation is the most informative of these three, where we wish to classify each and every pixel in the image, just like you see in the gif  here! Over the past few years, this has been done entirely with deep learning. In this guide, you’ll learn about the basic structure and workings of semantic segmentation models and all of the latest and greatest state-of-the-art methods.

2018-09-18  Read the full story. CloudQuant Thoughts… Another nice introduction, this time to Semantic Segmentation.  

10 Mind-Blowing TED Talks on Artificial Intelligence Every Data Scientist & Business Leader Must Watch

TED talks are simply fascinating. They provide tightly knit stories in short doses with mind-blowing information and experiences. It is amazing how much knowledge has been shared in this world using this simple and powerful medium. With Artificial Intelligence and Machine Learning getting so much attention in the spheres of research and business, I started looking out for TED talks on Artificial Intelligence in particular. I was in for such a treat – information treat to be precise. I gained much more from watching these TED talks than I have from following some of the most popular YouTube channels out there. Hence, I thought of sharing this incredible content with our community. To save your time – I have done all the hard work of watching all the talks on this topic till date and I have shortlisted the best ones for you.
  • The Incredible Inventions of Intuitive AI – Maurice Conti
  • How Algorithms Shape our World – Kevin Slavin
  • What Happens When our Computers Get Smarter than We Are? – Nick Bostrom
  • Can we Build AI without Losing Control Over it? – Sam Harris
  • How a Driverless Car Sees the Road – Chris Urmson
  • How We’re Teaching Computers to Understand Pictures – Fei-Fei Li
  • How Computers Learn to Recognize Objects Instantly – Joseph Redmon
  • The Jobs We’ll Lose to Machines – Anthony Goldbloom
  • How AI can Enhance our Memory, Work and Social Lives – Tom Gruber
  • How AI can Compose a Personalized Soundtrack to your Life – Pierre Barreau
2018-09-23 23:20:18+05:30 Read the full story. CloudQuant Thoughts… So much to learn.. so little time!  
Below the Fold…

How to Make the Most Effective Use of Data

Workday Analytics Vice President Pete Schlampp on how to ensure that data is collected, shared and visualized in a way that is transparent, truthful and transformative—as well as secure.
  • Create a company-wide culture of respect for data
  • Break down data silos to provide a single source of truth
  • Provide access to data in real time
  • Put data in the hands of decision makers
  • Make data visual
  • Tap into augmented analytics
  • Secure access to data
2018-09-20 00:00:00 Read the full story.  

Meet The World’s Most-Cited Deep Learning Researchers Whose Innovations Are Transforming The Industry

From pushing corporate messaging to aligning company’s artificial intelligence agendas, researchers courted by Silicon Valley’s big tech giants are now the ones deciding the future of technology. These AI researchers are also at the forefront of another type of arms race — the race to publish groundbreaking papers.
  • Yoshua Bengio – Montreal Institute for Learning Algorithms and Computer Science department at the University of Montreal
  • Geoffrey Hinton: University of Toronto and Google researcher
  • Yann LeCun: Facebook’s Chief AI Scientist and Founding Director of the NYU Center for Data Science
  • Andrew Zisserman: University of Oxford affiliated with Google-owned DeepMind
  • David Haussler: University of California’s Scientific Director of Genomics Institute, at the University of California in Santa Cruz.
  • Trevor Darrell: UC Berkeley Professor of Computer Science at the Electrical Engineering and Computer Sciences and Berkeley Artificial Intelligence Research lab (BEAR)
2018-09-24 07:00:29+00:00 Read the full story.  

Weekly Selection — Sep 21, 2018 – Towards Data Science

 
  • From Scratch: AI Balancing Act in 50 Lines of Python
  • Semantic Segmentation with Deep Learning: A guide and code
  • Building A Deep Learning Model using Keras
  • Deep Learning Framework Power Scores 2018
  • Simple Method of Creating Animated Graphs
  • How To Learn Data Science If You’re Broke
  • Illustrated Guide to Recurrent Neural Networks
  • Leuk Taal: Learning a New Language Through Data Science (and Art)
2018-09-21 16:55:32.406000+00:00 Read the full story.  

Deep Learning Power Scores 2018

I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Then I weighted and combined the data in this Kaggle Kernel. 2018-09-19 Read the full story.  

Wovenware is making AI to track disease-carrying mosquitoes in Puerto Rico

It’s been roughly a year since Hurricane Maria — the tenth-most intense Atlantic hurricane on record — a less-reported consequence of Hurricane Maria is an explosion of disease-carrying mosquitoes brought on by stagnant water. (The mosquitoes themselves don’t cause disease; rather, they pick up diseases from infected blood and spread them through bites.) Two years ago, Puerto Rico registered 38,058 confirmed cases of Zika, dengue, and chikungunya. But monitoring, testing, and labeling the more than 40 different species in Puerto Rico can be laborious. Currently, research scientists spend weeks capturing and classifying thousands of mosquitoes across difficult terrain. That’s why Wovenware tapped artificial intelligence (AI) to help. In early August, the company partnered with the Research Trust to develop a machine learning system that can automate the classification of Aedes aegypti,  a specious known to carry infectious diseases. Its small team of data scientists are putting together a dataset of mosquitoes images and labels that will be used to train a computer vision algorithm. 2018-09-21 00:00:00 Read the full story.  

AI helps Pixeldrive cut photo file sizes without substantially affecting quality

Typical compression schemes strip data away from files, and when it comes to photos, the results are often splotches, color banding, and other unpleasant artifacts. Try this experiment: Take a snapshot with your smartphone and upload it to Facebook or Twitter. Download the uploaded (and now compressed) image from the web and compare it to the original. You’ll spot the differences pretty quickly. But there’s a way to shrink pics without compromising their quality — or so claim Migel Tisserra, the former machine learning lead at oil and gas giant Santos, and Francis Doumet, a Stanford graduate with two successful businesses under his belt. They’re the cofounders of Pixeldrive, a web-based artificially intelligent (AI) tool that cuts photos down to as little as 10 percent of their original size. Pixeldrive — development around which kicked off eight months ago ahead of a planned launch in October — expands on research by Google and others investigating the use of neural networks in image compression.  “We set out to solve this general problem that we thought wasn’t really being addressed,” Doumet told VentureBeat in a phone interview. “Everyone is interested in this space. With the demand for bandwidth and cloud storage increasing, the more we can compress files intelligently, the better.” 2018-09-21 00:00:00 Read the full story.  

How New IBM Cloud Service Will Detect Bias in AI

AI has been viewed as a “black box” of sorts. Data goes in one end and findings, decisions and insights come out the other, but there’s little visibility into how those findings are reached, and as businesses and consumers alike learn to rely more on AI technology, there’s increasing worry about bias in the findings. Neural networks are only as good as the data that goes in, and that data can be influenced by the people who input it. IBM officials are now offering a cloud-based service designed to detect bias in AI and bring transparency to how AI-powered systems make decisions. The service runs on the IBM Cloud and can be used to manage AI systems from a wide array of tech vendors. At the same time, the company will release a toolkit to the open-source community that will include technology and education tools that others can use to detect and mitigate AI bias. Being able to detect bias and get more visibility into the decision-making process of AI systems is crucial as industries adopt the technology, according to Ruchir Puri, chief architect for IBM Watson and an IBM Fellow. 2018-09-19 00:00:00 Read the full story.  

LexinFintech CEO Speaks at World Economic Forum on Artificial Intelligence

Lexin’s CEO Jay Wenjie Xiao was invited to share his insights on AI as a representative from the fintech sector at a panel discussion under the theme of “A Global Conversation on Artificial Intelligence” on Sept. 18 with other panelists from business, venture capital, and academia. The financial and advertising industries are two sectors where AI has unlocked the most business potential with widespread adoption, Xiao said when asked about what AI means to business. “AI has largely contributed to a revolution in increasing efficiency in the financial industry by lowering the costs of lending, and therefore makes lending more accessible to consumers who were previously underserved,” he said. 2018-09-23 12:41:59-04:00 Read the full story.  

Machine Learning Could Make FX More Efficient

The Bank for International Settlements, the global regulator, said the increased use of machine learning in high-frequency foreign exchange trading could lead to more efficient markets, particularly the timely incorporation of diverse sources of data in market pricing. The BIS said in a report, Monitoring of fast-paced electronic markets, that a good understanding of artificial intelligence and machine learning is increasingly important for the effective monitoring of fast-paced markets and will require a change in risk management techniques. 2018-09-19 13:09:27-04:00 Read the full story.  

AI and Robotics to Create 60 Million More Jobs Than They Eliminate by 2022: WEF Study

While machines are on pace to perform more tasks than humans in the workplace by 2025, artificial intelligence (AI) will still add more jobs than it will take away over the next five years, according to a World Economic Forum report released on Monday. The WEF Report titled “The Future of Jobs 2018” was based on a survey of human resources officers, strategy executives and CEOs from over 300 global companies across industries, representing 15 million employees and 20 developed and emerging economies. Upon conducting the survey which accounted for roughly 70% of the global economy, the WEF estimates that development in automation technologies and AI could displace 75 million jobs by 2022, yet create another 133 million new roles as companies rework the division of labor between humans and machines. Meanwhile, employees should expect “significant shifts” in the quality, location, and format of new roles, meaning that the typical full time, permanent employee will be less dominant. Many firms may choose to hire temporary workers, freelancers and specialist contractors for tasks not automated by new technology. 2018-09-17 12:12:00-06:00 Read the full story.  

U.S. tech giants eye AI key to unlock China push

U.S. technology giants, facing tighter content rules in China and the threat of a trade war, are targeting an easier way into the world’s second largest economy – artificial intelligence. Google (GOOGL.O), Microsoft Inc (MSFT.O) and Amazon Inc (AMZN.O) showcased their AI wares at a state-backed forum held in Shanghai this week against the backdrop of Beijing’s plans to build a $400 billion AI industry by 2025. China’s government and companies may compete against U.S. rivals in the global AI race, but they are aware that gaining ground won’t be easy without a certain amount of collaboration. “Hey Google, let’s make humanity great again,” Tang Xiao’ou, CEO of Chinese AI and facial recognition unicorn Sensetime, said in a speech on Monday. 2018-09-18 10:52:02+00:00 Read the full story.  

IBM Launches ‘AI Fairness 360’ To Detect Bias In Artificial Intelligence

IBM is launching a new tool which will check biases in algorithms and try to understand how and why they crept in, and how to mitigate them. Called AI Fairness 360 (AIF360), this product by IBM is a comprehensive open-source toolkit of metrics to check for unwanted bias in datasets, machine learning models, and state-of-the-art algorithms. 2018-09-20 06:13:13+00:00 Read the full story.  

Personal finance bot Cleo raises $10m

British AI-based personal finance chatbot Cleo is set to start offering its own financial products after raising $10 million in a Series A funding round led by Balderton Capital. Balderton joins the likes of Skype founder Niklas Zennstrom, TransferWise founder Taavet Hinrikus and LoveFilm co-founder Simon Franks as Cleo investors. Launched in 2016, London-based Cleo integrates with users’ bank accounts and then uses AI to analyse spending habits and transaction histories to help with money management. 2018-09-21 14:18:00 Read the full story.  

U.K. Fintech Startup Cleo Raises $10 Million After Entering U.S.

A London-based company, Cleo, aiming to be the “default interface for millennials interacting with and managing their money,” has received a $10 million Series A funding round, according to TechCrunch, after the digital assistant launched in the U.S. six months ago. Powered by artificial intelligence, the chatbot plans to use the funding to expand to 22 more countries in the next 12 months. The chatbot is primarily accessed via Facebook Messenger and gives users financial feedback across multiple accounts and credit cards. Users are also able to set spending alerts and set aside money, among other features. 2018-09-21 14:19:00-04:00 Read the full story.  

Can the EU become another AI superpower?

ANGELA MERKEL, Germany’s chancellor, has a reputation for being dour. But if she wants to, she can be quite funny. When asked at a recent conference organised by Ada, a new quarterly publication for technophiles, whether robots should have rights, she dead-panned: “What do you mean? The right to electric power? Or to regular maintenance?” The interview was also striking for a different reason. Mrs Merkel showed herself preoccupied by artificial intelligence (AI) and its geopolitics. “In the US, control over personal data is privatised to a large extent. In China the opposite is true: the state has mounted a takeover,” she said, adding that it is between these two poles that Europe will have to find its place. 2018-09-22 00:00:00 Read the full story.  

This AI Is Scouting For Obesity-Prone Persons From Space, And It’s Actually A Good Thing

Neural networks are very popular in the field of artificial intelligence applications like object detection or image classification. Now researchers have discovered a way to find out obesity-prevalent areas with the aid of convolutional neural networks. Researchers from the University of Washington, Seattle, have been taking satellite images of the earth and using them to find which people are prone to obesity. The satellite records the surrounds of people living or working in a specific area which is later used to analyse obesity-prone people by gauging habits they might have or are likely to develop based on their surrounding environments. Though this analysis is 64.8 percent reliability, it is quite inexpensive and swift. 2018-09-21 12:54:44+00:00 Read the full story.  

News Site to Investigate Big Tech, Helped by Craigslist Founder

When the investigative journalist Julia Angwin worked for ProPublica, the nonprofit news organization became known as “big tech’s scariest watchdog. By partnering with programmers and data scientists, Ms. Angwin pioneered the work of studying big tech’s algorithms — the secret codes that have an enormous impact on everyday American life. Her findings shed light on how companies like Facebook were creating tools that could be used to promote racial bias, fraudulent schemes and extremist content. Now, with a $20 million gift from the Craigslist founder Craig Newmark, she and her partner at ProPublica, the data journalist Jeff Larson, are starting The Markup, a news site dedicated to investigating technology and its effect on society. 2018-09-23 00:00:00 Read the full story.  

Sloan Kettering’s cozy deal with start-up ignites a new uproar

An artificial intelligence start-up founded by three insiders at Memorial Sloan Kettering Cancer Center debuted with great fanfare in February, with $25 million in venture capital and the promise that it might one day transform how cancer is diagnosed. The company, Paige.AI, is one in a burgeoning field of start-ups that are applying artificial intelligence to health care, yet it has an advantage over many competitors: The company has an exclusive deal to use the cancer center’s vast archive of 25 million patient tissue slides, along with decades of work by its world-renowned pathologists. Memorial Sloan Kettering holds an equity stake in Paige.AI, as does a member of the cancer center’s executive board, the chairman of its pathology department and the head of one of its research laboratories. Three other board members are investors. The arrangement has sparked considerable turmoil among doctors and scientists at Memorial Sloan Kettering, which has intensified in the wake of an investigation by ProPublica and The New York Times into the failures of its chief medical officer, Dr. José Baselga, to disclose some of his financial ties to the health and drug industries in dozens of research articles. He resigned last week, and Memorial Sloan Kettering’s chief executive, Dr. Craig B. Thompson, announced a new task force on Monday to review the center’s conflict-of-interest policies. 2018-09-21 00:00:00 Read the full story.  

Machine Learning Is Driving The New Digital Marketing Renaissance

Data is the number one reason whydigital marketers have a huge advantage over those that traditional marketing channels. I have noticed one common theme among them. They have constantly found that intuition doesn’t bode with reality. They may think that a strategy that they took would work very well, but then discover that their data shows something very different. They live by the principle that you should always “build strategies around your data.” This is true and Mariya Yao, CTO of Metamaven, and Co-Author of “Applied Artificial Intelligence” states that machine learning is playing a very influential role in the future of digital marketing. Unfortunately, data can be very difficult for even the most astute marketers to analyze. This is why it is often best to allow algorithms to either automate the process or provide actionable feedback that would have otherwise been overlooked. Machine learning is changing the process in ways that we never envisioned before. This isn’t hype. It is a fact. One study by QuanticMind found that 97% of industry leaders believe machine learning is the future of their profession. 2018-09-19 15:45:12+00:00 Read the full story.  

Machine Learning is disrupting science research: Here’s how

It’s rare that one technology has the power to improve an entire industry, but machine learning is doing just that for scientists. Regardless of the specific subjects they study, machine learning allows them to make discoveries faster than they otherwise could. As such, it’s possible to make rapid progress that could benefit society at large in ways not even imagined yet. Scientists often process data through means called clustering and ranking. Clustering involves focusing on things sharing common characteristics. Then, ranking puts them in order of importance as defined by certain parameters. One of the tremendously helpful things machine learning can do is cluster and rank quantities of data that are much bigger than people could handle without extremely time-intensive processes. Then, scientists can arrange data in systematic ways and potentially find things out about it that they’d otherwise miss. 2018-09-18 11:00:44+00:00 Read the full story.  

AI Weekly: Transparency challenges stand in the way of ambient computing

In the coming weeks and months, Amazon’s voice assistant will leverage its wealth of accumulated knowledge about human behavior to anticipate which commands, information, and tasks are most relevant at any given moment. For example, if you tell an Echo speaker, “Alexa, good night,” it might say in response, “By the way, your living room light is on. Do you want me to turn it off?” This sort of personalized, contextual experience — commonly referred to as ambient computing — was once the stuff of science fiction, but advancements in artificial intelligence (and ambitious new startups taking full advantage of those advancements) are fast making it a reality. 2018-09-21 00:00:00 Read the full story.  

Is Robotic Process Animation The Next Evolution Of Big Data?

Big Data is the Future of Robotic Process Automation
  • Robotics and Big Data have been linked for years.
  • Robotic Process automation is the newest arena for big data
  • RPA is able to upload and index company documents.
  • RPA can create auto reply emails and letters.
  • RPA can check documents to see which templates were used.
  • RPA can conduct and gather data from online surveys.
2018-09-23 21:03:45+00:00 Read the full story.  

Why Small Businesses Shouldn’t Think Twice Before Embracing AI

If you are thinking AI is a big thing forsmall businesses, then think again. Sure enough, IT powerhouses like Amazon and USP are relying on AI robots to enhance employee experience. But then, the fact is, even start-ups are readily experimenting with AI technologies, big-time. For instance, there’s a pizza start-up in Mountain View, California that is employing several robots to assist humans in assembling and baking pizzas. The point is AI is not just the province of big businesses. Even small businesses are harnessing the power of AI to boost their productivity. 2018-09-20 17:30:59+00:00 Read the full story.  

Do you know how universities and colleges use Big Data?

Education across the globe today has become a competition. Where institutions vie for the attention of prospective students in order to increase the number of successful admissions. Rapid innovation in technology has made it easier for schools and colleges to reach out to more and more student candidates as the urge to market them intensifies. In order for advanced education institutions to receive the maximum number of accepted candidates, they are adopting the ability to analyze, access and manage vast volumes of data. Colleges now have more complicated ways of collecting and reacting to data about students, so they can target them in more specific ways than ever. So, with increased competition for gifted students and rising cost of education the pool of potential students remains limited. However, universities are working hard to identify relevant talent pools in order to appeal to suitable candidates. For example, students who are capable of contributing towards the development of the university. In order to identify these talent pools, big data analysis comes in handy. 2018-09-17 11:18:03+00:00 Read the full story.  

5 Reasons why Businesses Struggle to Adopt Deep Learning

So, you’ve heard the dazzling sales pitch on deep learning and are wondering whether it actually works in production. The top question companies have is on whether the promised land of perennial business benefits is a reality. In a previous article, we saw a simple business introduction to deep learning, a technology that seems to have a swashbuckling solution to every problem. A good gauge of an innovation’s maturity level is by understanding how it fares on the ground, long past the sales pitches. At Gramener AI Labs, we’ve been studying advances in deep learning and translating them into specific projects that map to client problems. I’ll share some of our learnings from project implementations of DL solutions over the past year. It’s been a mixed bag, with success stories and some setbacks, where we saw the initial charm fade away due to hurdles on the ground. 2018-09-24 02:52:03.856000+00:00 Read the full story.  

Vertical Markets That Desperately Need an Edge Networking Strategy

With the escalating number of internet of things-enabled devices, businesses across a variety of industry verticals are feeling the pressure to figure out their own IoT strategies. Organizations that may not have historically prioritized their networking strategy are now challenged to implement a robust infrastructure that can handle data from countless wired and wireless endpoints—and the network edge (smartphones, laptops, purpose-driven server… 2018-09-19 00:00:00 Read the full story.  

AI offers a unique opportunity for social progress

As a global community, we’ve made stunning strides in recent decades, tackling some of the world’s cruellest tragedies. Consider one: child mortality. Every day 17,000 new lives get to be lived by children who would have died just a quarter-century ago. Peace and innovation have been the driving forces of this spectacular progress. Yet some of our toughest challenges, like inequality, haven’t improved—they’ve actually become worse. Malnutrition and preventable disease continue to kill millions, straining health-care systems in both rich and poor countries. And the devastating threat of climate change looms, hitting the poorest the hardest. 2018-09-20 00:00:00 Read the full story.  

Microsoft’s New AI-Backed Services Threaten Salesforce

Microsoft Corp. (MSFT) has launched a series of artificial intelligence (AI) -powered tools meant to help businesses improve their customer service, marketing and manufacturing processes. The new services, which will be made available as part of the company’s Dynamics 365 cloud-based program offerings, form part of Microsoft’s strategy to make its products more competitive with Salesforce.com Inc.’s (CRM) premium AI features. 2018-09-19 02:49:00-06:00 Read the full story.  

Revenge Of The Humans: Why Open AI Five Could Not Win The Dota 2 Championship

The International, which is the FIFA of Dota 2, a complex battle arena game, had an artificial intelligence system compete with professional players in the 2018 tournament. Earlier this August, an AI player called Five, created by OpenAI, failed to defeat professional human gamers. Despite having the training and “experience” of over 180 years, the AI was unable to achieve the feat. Why was it so? To give a brief to the uninitiated, Dota 2 is a popular online multiplayer video game which has 115 heroes, categorised according to strength, agility and intelligence. There are two teams of five players each and every team player has to pick a hero, which has different powers and characteristics, and destroy the opposite team’s base while encountering a lot of hurdles. 2018-09-20 12:47:47+00:00 Read the full story.  

NLP Primer: Help Machines Understand Our Language

Understanding the human language is one of the most complex tasks for a machine, but with the current artificial intelligence trend, it is getting easier day by day. With tons of frameworks and libraries available in Python, natural language processing can be used to analyse the text and help the computer to understand, translate or reproduce data into desired languages. One can implement these techniques on our hackathon platform to test their skills In this article we shall analyse a text document consisting of a short description of Vikings’ era. We will also implement techniques used in NLP to process the data for the machine to understand. 2018-09-18 05:28:28+00:00 Read the full story.  

Amazon’s Alexa will use ‘intuition’ to know what you want before you do

Amazon has unveiled a new feature that will allow its AI assistant, Alexa, to tell you what you want and remind you if you’ve forgotten anything. Known as “Alexa Hunches”, the tool allows Alexa to learn how you interact with your smart devices by monitoring everything from your home TV to your lights and kitchen appliances. It can then pick up a pattern, such as turning the TV off before bed or locking the doors at night. If you forget to do something in your routine, Alexa will remind you, either through an Echo speaker or an Android phone. Daniel Rausch, the company’s vice president of the ‘smart home’ division, said: “We’ve reached a point with deep neural networks and machine learning… 2018-09-21 00:00:00 Read the full story.  

AstraZeneca plots China robot offensive to counter price cuts

With smart cancer diagnostics, one-stop-shop diabetes kits and AI systems to improve ambulance pick-ups for patients with chest pain, AstraZeneca (AZN.L) aims to move from simply supplying drugs to become a broad healthcare provider in China. Tech tie-ups with the likes of Alibaba (BABA.N) and Tencent (0700.HK) will not directly lift the British group’s drug sales, since they are not specific for any one company’s products and in many cases will be low-cost or free. But it will expand the overall market and represents a soft power play that dovetails neatly with Beijing’s support for Internet-based healthcare systems to alleviate a lack of doctors, overcrowding and poor grassroots healthcare. 2018-09-19 06:43:33+00:00 Read the full story.  

Trump’s new strategy means the U.S. could get more aggressive with Russia and China over hacking

The White House released a new cybersecurity strategy today, with several important changes in direction meant to give government agencies and law enforcement partners a greater ability to respond to cybercrime and nation-state attacks. The 40-page document mostly stays the course for past initiatives — like working to strengthen the organizations that make up the country’s “critical infrastructure” industries, including electrical operators and financial institutions. But some of the changes emphasize a shift toward a more offensive cybersecurity posture, a longtime request fromm the National Security Agency and cybersecurity branches of the U.S. Armed Forces. 2018-09-21 00:00:00 Read the full story.  

A new plot theme for Matplotlib — Gadfly – Towards Data Science

I’ve made a plotting theme for Matplotlib that’s inspired by the default plotting theme used in Gadfly for the Julia programming language. Typically I’d just write the code in Julia, which is what I’ve done for many of my previous blog posts. However, since upgrading to Julia v1.0, I’ve been unable to import Gadfly, which means no more pretty Gadfly plots. So I said to myself “Jonny, it’s time to just create the theme yourself”. And I did! 2018-09-24 02:51:49.164000+00:00 Read the full story.  

Artificial intelligence weaponry successfully trialled on mock urban battlefield

A cutting edge weapon that uses artificial intelligence to scan the battlefield for enemy movements has been successfully tested for the first time. The system, that was developed by British experts, uses space age technology to monitor and track opposing forces in built up areas. It can then rapidly flag dangers to soldiers, giving them an “edge” in a warzone, the Ministry of Defence (MoD) said. 2018-09-24 00:00:00 Read the full story.  

Commentary: Alexa, stop! Amazon’s smart home just sounds more and more like a lazy home

I have three Amazon Echo Dot devices in my home, which is kind of an ironic admission to make at the start of a rant about new devices from the tech giant. Oh well, I don’t make excuses for what the grandparents send to my kids, or the free stuff that ends up on my desk. There may have been a point in the last few years where I thought voice-integrated stuff sounded like a cool idea. I introduced my son to Alexa and actually reveled in my freedom from his homework as he turned to her instead of me for the answer to “What’s 9×9?” or whatever. But these days the devices have fallen mostly silent. 2018-09-20 20:31:22-07:00 Read the full story.  
Behind a Paywall and/or Registration page…  

Real-Time Deep-Link Analytics: The Next Stage in Graph Analytics

Deep-link analytics has been out of reach until now. This white paper discusses how how and why graph analytics has evolved and the benefits of being able to perform subsecond queries of big data and stream over 2B daily events in real-time to a graph with 100B+ vertices and 600B+ edges on a cluster. 2018-09-18 00:00:00 Read the full story.  

Native Parallel Graphs: The Next Stage in Graph Database Evolution

m early generation Native Graph Technologies, and introduces TigerGraph – the world’s first and only NPG system. TigerGraph is a complete, distributed graph analytics platform that supports web-scale data analytics in real-time, delivering incredible loading speed, fast query execution and real-time update capabilities…. 2018-09-18 00:00:00 Read the full story.  

China’s tech giants have conquered the East, now for the West

At a global summit on artificial intelligence in Washington last November, Eric Schmidt, former executive chairman of Alphabet, the parent company of Google, delivered a stark warning on China’s technological prowess. “By 2020, they will have caught up. By 2025, they will be better than us. By 2030, they will dominate the industries of AI,” he said. But Schmidt’s keynote speech at the Centre for a New American Security only addressed part of the story of China’s emergence as a technology powerhouse – and the growing threat its big companies pose to the hegemony of Silicon Valley. Over the past year, the best-known US technology giants – Facebook, Amazon, Apple, Netflix and Google – the so-called… 2018-09-23 00:00:00 Read the full story.  

Amazon unveils Alexa-powered microwave

Amazon has unveiled a line of electric plugs, microwaves and clocks powered by its Alexa digital assistant as it sought to position itself at the centre of the smart home. The gadgets see the company’s Alexa voice assistant extend well beyond the line of Echo smart speakers it first released in 2014. Amazon’s Echo, which uses artificial intelligence to respond to voice commands, has become a surprise hit in many homes, but the company’s grip on the market for smart speakers has come under threat by rival devices from Google and Apple. By significantly expanding the gadgets it sells that work with the Echo, the company hopes it will lock consumers into using its software. 2018-09-20 00:00:00 Read the full story.  

The best way to avoid killer robots and other dystopian uses for AI is to focus on all the good it can do for us, says tech guru Phil Libin

When it comes to how artificial-intelligence technology might affect society, there are a host of things to worry about, including the massive loss of jobs and killer robots. But the best way to avoid such negative outcomes may be to ignore them, more or less. That’s the advice of Phil Libin, CEO of All Turtles, a startup that focuses on turning AI-related ideas into commercial products and companies. In a recent conversation with Business Insi… 2018-09-23 00:00:00 Read the full story.  
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
bulls and bears

SMB Quant Video 1 – Developing a Model

SMB Capital recently started a new series of Videos/Podcasts where they interview Quants both inside and outside of their company. In the first video, Jeff Holden of SMB steps through his process for developing a model :
  • Data Curation – What data do I need for this model?
  • Feature Analytics – Taking informative features and turning them into actual investment algorithms
  • Strategy – Take the analysis and turn it into a Theory – an understanding of WHY it works, what is the reason or motivation that causes the other trader on the opposite side to lose money to us. Often you finds something other than what you were looking for or even the exact opposite.
  • Backtesting – Assess – Test in unusual market environments – try to break it.
  • Deployment – Testing the model with live market data, Optimization, Scaling and Portfolio selection.
  • Q&A Session
Applications used : CloudQuant, Jupyter Notebooks, Gr8Trade (KTG Group), Gr8Py (KTG Group) Duration : 51m
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SMB Quant Video 2 – Democratization of Trading – CloudQuant

In video two of SMB’s new Quant series they interview yours truly, Paul Tunney, to discuss what CloudQuant is and why you should use it to develop an algorithmic trading model. In this video we discuss…
  • The Goal of CQ – The democratization of Trading Data and systems.
  • Pre-Requisites – You will need to know Python, but the skill of programming will be hugely beneficial for your entire life.
  • How to create a model – Why the question “How do I create a model?” is similar to the question “How do I write a song?”
  • What is unique about CQ – Built by real world Quants for real world Quants 
  • What about ML and AI – Requires a lot of data, getting, managing and using that data is difficult.
  • What we hope people will do with CQ – We hope to get a few great new smart Algo creators who are currently outside of Wall Street.
  • Funding – We will fund your models, at a level you could never manage yourself. And we take on the risk.
  • The Future – CloudQuant AI
  Duration : 63m
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AI & Machine Learning News. 17, September 2018

Key Takeaways from Andrew Ng’s ‘Heroes of Deep Learning’ Series

Andrew Ng is the most recognizable personality of the modern deep learning world. His machine learning course is cited as the starting point for anyone looking to understand the math behind algorithms. But even the great Andrew Ng looks up to and takes inspiration from other experts. In this amazing and in-depth video series, he has interviewed some of the most eminent personalities in the world of deep learning (eight heroes, to be precise). The interviews span the length and breadth of deep learning, including topics like backpropogation, GANs, transfer learning, etc. Even artificial intelligence crops up in between conversations. But don’t worry if these terms sound overwhelming, we have listed down the key takeaways from each interview just for you. The 8 interviewees are Geoffrey Hinton, Ian Goodfellow, Yoshua Bengio, Pieter Abbeel, Yuanquing Lin, Andrej Karpathy, Ruslan Salakhutdinov, Yann LeCun. 2018-09-14 00:58:57+05:30 Read the full story. CloudQuant Thoughts… If you are already deep into AI and ML you will love this series of interviews!  

The creator of Google’s self-driving car project is now working to automate boring office functions

 
  • Stealthy enterprise start-up Cresta AI is trying to automate repetitive tasks, starting with sales chats.
  • Sebastian Thrun, the creator of Google’s self-driving car project, is a co-founder of the company.
  • Cresta recently raised a seed round of funding.
Sebastian Thrun, one of the best known entrepreneurs in Silicon Valley, is taking on a new challenge that’s a big shift from his work in autonomous transportation or online education: He’s working to automate sales chats. Thrun, who founded Google’s research lab X and its autonomous car project, education start-up Udacity, and electric aircraft company KittyHawk, is a co-founder and chairman of a stealthy enterprise company called Cresta AI. He’s the elder statesman of the founding team, which includes Zayd Enam and Tim Shi, who are both in their late 20s. 2018-09-16 00:00:00 Read the full story. CloudQuant Thoughts… The big names in AI and ML are out in force this week. This story is of little interest except for the fact that it involves Sebastian Thrun!  

3 into 1 will go – Take three interesting news articles and create a model…

New Globant Report Debunks Common AI Myths to Arm Decision Makers with Fresh Investment Perspectives

Today Globant (NYSE :GLOB ), a digitally native technology services company, releases its 2018 Artificial Intelligence Technology Business Guide ‒ a playbook for organizations considering investing in AI, complementing its recently launched book, “Embracing the Power of AI.” The report found that the majority of decision makers (80 percent) believe AI can make an immediate impact on their business by doing things like enhancing routine tasks and sorting through massive sets of data. However, despite this excitement, many are still navigating what investing entails and how ready they are to implement the technology. The report debunks common AI myths among decision makers to offer a modern perspective for how the technology can be effective for businesses:
  • Myth No.1: AI will replace humans.
  • Myth No.2: AI can’t help my business.
  • Myth No.3: AI’s sophistication leads to magical rewards.
  • Myth No.4: AI is just for tech experts.
2018-09-17 09:11 EST Read the full story.  

Big Data 50 – Companies Driving Innovation

The wealth of data now available to organizations from internet-scale applications and the growth of the Internet of Things, is fueling the use of data lakes, artificial intelligence (AI), machine learning (ML), and predictive analytics solutions. These and other technology initiatives for managing and analyzing data were explored in a recent Unisphere Research survey. In the report, analyst Joe McKendrick identified key trends that are shaping the way enterprises leverage their data, as well as the evolving priorities of data managers. Data lakes, a place to store diverse datasets without having to build a model first, are perhaps the most mature technology initiatives seen among enterprises in the survey. 2018-09-13 00:00:00 Read the full story.  

Data Science Skills: Web scraping using python

One of the first tasks that I was given in my job as a Data Scientist involved Web Scraping. This was a completely alien concept to me at the time, gathering data from websites using code, but is one of the most logical and easily accessible sources of data. After a few attempts, web scraping has become second nature to me and one of the many skills that I use almost daily. In this tutorial I will go through a simple example of how to scrape a website to gather data on the top 100 companies in 2018 from Fast Track. Automating this process with a web scraper avoids manual data gathering, saves time and also allows you to have all the data on the companies in one structured file. 2018-09-13 00:00:00 Read the full story. CloudQuant Thoughts… There are real trading opportunities here if you can identify the companies who are leading the charge into AI/ML in their respective industries. Perhaps some news scanning, perhaps utilize a list like the big 50 above or just some good old fashioned web scraping?  Either way, if you can identify the leaders you may have a model you can code up on CloudQuant.com  

Another Machine Learning Walk-Through and a Challenge

Don’t just read about machine learning — practice it! After spending considerable time and money on courses, books, and videos, I’ve arrived at one conclusion: the most effective way to learn data science is by doing data science projects. Reading, listening, and taking notes is valuable, but it’s not until you work through a problem that concepts solidify from abstractions into tools you feel confident using. The New York City Taxi Fare prediction challenge, currently running on Kaggle, is a supervised regression machine learning task. Given pickup and dropoff locations, the pickup timestamp, and the passenger count, the objective is to predict the fare of the taxi ride. Like most Kaggle competitions, this problem isn’t 100% reflective of those in industry, but it does present a realistic dataset and task on which we can hone our machine learning skills. 2018-09-10 Read the full story. CloudQuant Thoughts… A nice detailed walkthrough using a huge dataset (55 million rows of data). Mapping data onto real world maps, and he includes a Jupyter Notebook for you to follow along. Kudos William Koehrsen!  
Below the fold…

Labeling and Meta-Labeling Returns for ML Prediction

This post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado. In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and also meta-labeling which is a technique designed to address several issues. Those issues include how to improve the f1-scores and recall accuracy of a primary model such e.g. a moving average crossover model, and how to reduce the likelihood of overfitting a model by splitting up the decision of which side to trade from the decision to trade at all. Read the full story.  

Linear Regression using Gradient Descent – Towards Data Science

In this tutorial you can learn how the gradient descent algorithm works and implement it from scratch in python. First we look at what linear regression is, then we define the loss function. We learn how the gradient descent algorithm works and finally we will implement it on a given data set and make predictions. 2018-09-16 13:46:35.747000+00:00 Read the full story.  

A Comprehensive Guide to the Grammar of Graphics for Effective Visualization of Multi-dimensional Data

Visualizing multi-dimensional data is an art as well as a science. Due to the limitations of our two-dimensional (2-D) rendering devices, building effective visualizations on more than two data dimensions (attributes or features) starts becoming challenging as the number of dimensions start increasing. 2018-09-12 Read the full story.  

Computer Science in the Data Science World with Dr. Jeannette M. Wing

Have you noticed that the recent surge of data scientists have a background in computer science? It’s not a coincidence. These two domains are important in their own right but when merged together, they produce powerful results. We are thrilled to announce the release of episode 10 of our DataHack Radio podcast with none other than Professor Jeannette M. Wing! She has over 4 decades of experience in academia and the industry, and there is no one better to give a perspective on how computer science has evolved, and how it meshes with the data science world. I have briefly summarized the key takeaways from this episode below…
  • Professor Jeannette Wing’s Background
  • Using Formal Methods Techniques to Improve Machine Learning Algorithms
  • Research Projects in Academia and Microsoft
  • Difference between Working in Academia v Industry
  • Where are Computer Science and Data Science Heading in the Next 5 Years?
2018-09-17 08:33:42+05:30 Read the full story.  

The Real Reason behind all the Craze for Deep Learning

Deep learning has created a perfect dichotomy. On the one hand, we have data science practitioners raving about it, and every one and their colleague jumping in to learn and make a career out of this supposedly game-changing technology in analytics. And then there is everyone else wondering what the buzz is all about. With a multitude of analytics technologies projected as the panacea to business’ problems, one wonders what this additional ‘cool thing’ is all about. For people on the business side of things, there are no easy avenues to get a simple and intuitive understanding. A Google search gets one entangled in the deep layers of neural networks, or gets them bowled over by the math symbols. Online courses on the subject haunt one with a bevy of stats terms. One eventually gives in and ends up taking all of the hype at face value. Here’s an attempt to demystify and democratize the understanding of deep learning (DL), in simple english and in under 5 minutes. I promise not to show you the cliched pictures of human brains, or a spider web of networks. 2018-09-10 Read the full story.  

Fixing the Last Mile Problems of Deploying AI Systems in the Real World

Last mile problems are the final hurdles to realizing AI’s promised values. Reaping the benefits of AI systems requires more than solid business cases, well-executed AI implementations, and powerful technology stacks. It often requires the collaboration of AI and staffs to provide the right experience to customers. But, companies usually struggle to do this well. Many analyses highlight how to build AI systems from the lenses of executives and data scientists. Instead, this case study looks at the issues using a personal anecdote and from new perspectives: the ones through the eyes of front line staffs and customers. I discuss various practical solutions, such as 80–20 rules in AI and smoothing hand-offs between machines and humans, to help teams to overcome the last mile hurdles of AI delivery in the real world. 2018-09-09 Read the full story.  

How Machine Learning Algorithms & Hardware Power Apple’s Latest Watch and iPhones

This is a great time to be a data scientist – all the top tech giants are integrating machine learning into their flagship products and the demand for such professionals is at an all-time high. And it’s only going to get better! Apple has been a major advocate of machine learning, and has packed it’s products with features like FaceID, Augmented Reality, Animoji, Healthcare sensors, etc. While watching Apple’s keynote event yesterday, I couldn’t help but wonder at the new chip technology they have developed that uses the power of machine learning algorithms. 2018-09-13 13:37:32+05:30 Read the full story.  

Machine Learning Dominates Conversations at Strata Data Conference

Machine learning and AI continue to be the hottest topic at tech conferences around the country and Strata Data was no different. Data professionals converged at the Jacob Javits Center in New York City from September 11 – 13 and the event was humming with the latest talk of how ML and AI will change the future. Byron Banks, VP, product marketing, analytics at SAP, said that while machine learning and automation is predicted to replace certain human tasks, it’s really not emerging to replace us all, instead it’s coming to help users become better at their jobs. “It’s that idea of harmony,” Banks said. “There’s always an end user group of people involved in working with the machine learning to make it the most effective rather than having the person disappear from the transaction.” David Judge, vice president, SAP Leonardo at SAP, agreed and said it’s not about replacing people, it’s about removing manual, arbitrary tasks. Customers may have machine learning teams and are investing heavily in AI, however, the number of companies managing it effectively are still small, said Will Davis, director of product marketing, Trifacta. 2018-09-14 00:00:00 Read the full story.  

MapR Unveils Six Service Offerings to Help Companies Get More from AI and ML

MapR (provider of a Data Platform for AI and Analytics) has announced six new data science service offerings to help customers gain immediate value from machine learning (ML) and artificial intelligence (AI). According to MapR, the new offerings addresse the fact that AI and ML can be complex, as well as that organizations don’t always have the capacity to execute on AI and ML ideas, and those that do, may not be able to bring those ideas to production. The six new MapR data science lifecycle service offerings include an AI/ML Hack-a-thon offering in which the MapR Data Science team works with the organization to identify a business use case and prototype a solution to deliver a real ML and AI solution that the organization will continue to improve and maintain over time. 2018-09-11 00:00:00 Read the full story.  

Creating a Hybrid Content-Collaborative Movie Recommender Using Deep Learning Written by Adam Lineberry and Claire Longo

In this post we’ll describe how we used deep learning models to create a hybrid recommender system that leverages both content and collaborative data. This approach tackles the content and collaborative data separately at first, then combines the efforts to produce a system with the best of both worlds. Using the MovieLens 20M Dataset, we developed an item-to-item (movie-to-movie) recommender system that recommends movies similar to a given input movie. To create the hybrid model, we ensembled the results of an autoencoder which learns content-based movie embeddings from tag data, and a deep entity embedding neural network which learns collaborative-based movie embeddings from ratings data. 2018-09-10 Read the full story.  

Why Python is So Popular with Developers: 3 Reasons the Language Has Exploded

Python is the fastest-growing programming language in the world, as it increasingly becomes used in a wide range of developer job roles and data science positions across industries. But how did it become the go-to coding language for so many tasks? “Python is very popular because of its set of robust libraries that make it such a dynamic and a fast programming language,” said Kristen Sosulski, clinical associate professor of information, operations, and management sciences in the Leonard N. Stern School of Business at New York University, and author of Data Visualization Made Simple. “It’s object-oriented, and it really allows for everything from creating a website, to app development, to creating different types of data models.” 2018-09-13 Read the full story.  

KPMG acquires a minority stake in fintech startup AdviceRobo

AdviceRobo is a Dutch fintech startup developing technology that predicts financial risk of people and companies taking out loans. To predict risk, the company applies artificial intelligence(AI) on non-financial data including the behavior of potential borrowers. AdviceRobo’s technology enables lenders, such as banks and retailers, to limit the risks of lending and reach a larger group of clients. “AdviceRobo is definitely a frontrunner in the sector”, says Rob Fijneman, Head of Advisory at KPMG. Fijneman: “We are very pleased that the alliance with AdviceRobo will enable us to add these types of AI-based predictive behavioral models to our services for lenders. AdviceRobo’s models will enable lenders to improve their credit risk models and thus reduce costs, especially in areas where data is the limiting factor. 2018-09-14 00:00:00 Read the full story.  

A.I. and robotics will create almost 60 million more jobs than they destroy by 2022, report says

 
  • Developments of machines and automation software in the workplace could create 58 million new jobs in the next five years, according to a report from the World Economic Forum.
  • The outlook for job creation is more positive today because companies better understand what kind of opportunities are available to them due to developments in technology, according to WEF.
  • There would also be “significant shifts” in the quality, location and format of new roles, the report found.
Machines will overtake humans in terms of performing more tasks at the workplace by 2025 — but there could still be 58 million net new jobs created in the next five years, the World Economic Forum (WEF) said in a report on Monday. 2018-09-17 00:00:00 Read the full story.  

Kx technology and related machine learning libraries now available on Anaconda’s distribution platform

Kx announces a partnership with Anaconda, Inc. to add the kdb+ database system, and related machine learning libraries, to Anaconda’s popular Python and R open source distribution platform. Kdb+ is now much more easily integrated into Python-based projects and technologists outside of the kdb+ community can now take advantage of the power of kdb+ without having to learn how to program in q. Kdb+ is the world’s fastest time-series database at the forefront of high-performance streaming, real-time and historical analytics. Its unified, elegant q language includes first-class tables, functions and time-series features. Its tiny footprint efficiently scales vertically and horizontally. 2018-09-12 00:00:00 Read the full story.  

Kx technology selected by Canadian Securities Administrators for advanced market surveillance

Kx, a division of First Derivatives plc, announces that it has been selected by the Canadian Securities Administrators (CSA) to build and manage a next generation market analytics platform designed to assess, investigate and explain potential market abuse cases. Kx will combine the power of the technology’s existing suite of analytics with machine learning algorithms to deliver a Market Analysis Platform (MAP) that will improve insight and support market integrity. 2018-09-13 00:00:00 Read the full story.  

Could Python’s Popularity Outperform JavaScript in the Next Five Years?

JavaScript and Python are two influential programming languages for building a wide range of applications. While JavaScript has been the dominant programming language for many years, Python’s fast-growth threatens to dethrone the widely popular technology. 2018-09-17 11:36:01.542000+00:00 Read the full story.  

Carnegie Mellon’s Andrew Moore to join Google Cloud as new head of AI later this year

After an interesting year for Google Cloud’s artificial intelligence group, Andrew Moore, dean of computer science at Pittsburgh’s Carnegie Mellon University, will become head of the division at the end of the year, with current leader Fei Fei Li returning to Stanford in a move that Google said was all part of the original plan. Moore, a former Google employee, will rejoin the company at the end of the current semester at Carnegie Mellon, Google Cloud CEO Diane Greene announced in a blog post. “We are incredibly fortunate to have Andrew’s leadership at this point in our development as we define how we will expand bringing AI and ML technologies and solutions to developers and organizations all over the world,” she wrote. 2018-09-10 15:57:48-07:00 Read the full story.  

Nvidia Aims Tesla T4 GPUs at AI Inferencing in Data Centers

Nvidia officials are looking to press their advantage in the fast-growing artificial intelligence space with the introduction of the company’s new Tesla T4 GPUs and a new platform and software aimed at the inference side of the AI equation. At the vendor’s GTC technology conference in Tokyo this week, Nvidia CEO Jensen Huang showed off the new GPU, which is based on Nvidia’s Turing architecture that was introduced last month. At the time, the first of the Turing GPUs unveiled were aimed primarily at gamers. At the GTC Japan event, Huang turned his attention to the data center, including hyperscale environments. Along with the Tesla T4 GPUs, the CEO announced the TensorRT software to help drive the development of voice, video, image and recommendation services and the TensorRT Hyperscale Inference Platform, powered by the T4 GPUs and aimed at enhancing inference tasks in such industries as automotive, manufacturing robotics and health care. 2018-09-13 00:00:00 Read the full story.  

AI Weekly: Siri needs people’s trust for Shortcuts to succeed

Siri Suggestions will surface recommended actions using more than 100 different factors such as time of day, location, or even the Wi-Fi network you’re on to determine whether or not a shortcut suggestion surfaces on your smartphone lock screen or Apple Watch face. A standalone Shortcuts app also lets users create custom commands. Whereas before with SiriKit you could connect with a limited number of apps for use cases like sending messages or hailing a ride from Uber, Shortcuts appears to be more flexible than similar products from Alexa and Google Assistant, and is capable of connecting Siri with apps that enable Shortcuts APIs. 2018-09-14 00:00:00 Read the full story.  

Fintech tackles op risk

Organisations are met with operational risk wherever they turn. On the one hand, they encounter risks relating to employee behaviour, third parties, statistics and controls. On the other, equally important cultural, moral and ethical risks are also causing disruption. And everywhere they look, companies are faced with risks associated with ordinary evolution, as organisations continue to embrace innovative technologies like automation, robotics and artificial intelligence. 2018-09-17 00:00:00 Read the full story.  

Cooking With Robots: MIT Students Teach AI To Make Pizza

Have you ever used artificial intelligence to make pizza? A group of MIT students tried it, and they are quite pleased with how it came out. The pizza included the AI-made “wale[sic] walnut ranch dressing” as a topping. In the sea of reports hinting at the dangers of artificial intelligence, the Massachusetts Institute of Technology group found a way to show that robots and humans could have a bright, peaceful future together. On Monday, MIT student Pinar Yanardag and her colleagues launched a new project called “How to Generate (Almost Anything.) Each week they will release a new product, such as art, perfume or food, that was created through the work of MIT students and their AI-powered robot. “By augmenting human capabilities and pushing the boundaries of creativity, can AI inspire us to create things that wouldn’t have existed otherwise? A dress designed with a crazy hat, a pizza made with shrimp & jam or a scent that has never been smelled before?” In the first chapter of their project, the MIT students wanted to teach their AI to make pizza. To do this, the robot’s neural network had to process hundreds of artisan pizza recipes and come up with new topping combinations that would go nicely together. “In general, AI models are very good at connecting different pieces of information together – that’s why there is usually a surprise factor in anything that an AI generates,” Yanardag said. “In our pizza experiment, we saw something similar where AI combined ingredients like shrimp and Italian sausage with jam, which it picked up from a dessert pizza.” 2018-09-14 09:40:37-04:00 Read the full story.  

Nvidia researchers develop AI system that generates synthetic scans of brain cancer

Artificially intelligent (AI) systems are as diverse as they come from an architectural standpoint, but there’s one component they all share in common: datasets. The trouble is, large sample sizes are often a corollary of accuracy (a state-of-the-art diagnostic system by Google’s DeepMind subsidiary required 15,000 scans from 7,500 patients), and some datasets are harder to find than others. Researchers from Nvidia, the Mayo Clinic, and the MGH and BWH Center for Clinical Data Science believe they’ve come up with a solution to the problem: a neural network that itself generates training data — specifically, synthetic three-dimensional magnetic resonance images (MRIs) of brains with cancerous tumors. It’s described it in a paper (“Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks”) being presented today at the Medical Image Computing & Computer Assisted Intervention conference in Granada, Spain. 2018-09-16 00:00:00 Read the full story.  

Microsoft acquires Lobe, an AI startup working on easy-to-use deep-learning development tools

Artificial intelligence tools will never be widely used if it takes decades of expertise to put them into action, which is why cloud companies have been working hard to make them easier to use and more accessible. Microsoft took another step in that direction Thursday with the acquisition of San Francisco-based Lobe. Founded by Mike Matas, Adam Menges, and Markus Beissinger in 2015, Lobe created visual tools that can build deep-learning models with a drag-and-drop user interface, rather than lines of code. “We look forward to continuing the great work by Lobe in putting AI development into the hands of non-engineers and non-experts,” Scott said in his post. 2018-09-13 17:38:19-07:00 Read the full story.  

Base10 Partners launches $137 million early-stage AI startup fund

Base10 Partners today announced the launch of a $137 million fund to invest in early-stage startups that will use AI to change industries by empowering workers instead of automating them out of jobs. The prime directive of the debut fund will be to back companies in industries like real estate, construction, waste management, and logistics — what managing partner Adeyemi Ajao calls “automation for the real economy” and “solving problems for 99 percent of people.” 2018-09-17 00:00:00 Read the full story.  

Cisco Builds UCS C480 ML Rack Server to Accelerate Deep Learning

Cisco Systems on Sept. 10 unveiled its UCS C480 ML rack server, a system designed to accelerate deep learning workloads powered not only by two Intel “Skylake” Scalable Processors but also eight Tesla V100 GPUs from Nvidia that are connected by the chip maker’s NVLink interconnect. The system comes with as many as 24 disk drives offering as much as 182TB of storage and up to six NVMe drives. Plus, it can support up to four 100 Gigabit Ethernet switches. The tightly integrated offering will use artificial intelligence and subsets like machine learning and deep learning to address the challenges facing enterprises as they try to drive competitive advantages from the massive amounts of data they’re generating. 2018-09-11 00:00:00 Read the full story.  

Simple Method of Creating Animated Graphs – Towards Data Science

After the publication of one of my latest articles, many people asked me for tips on how to create animated charts in Python. Indeed, there are often situations when a static chart is no longer sufficient and in order to illustrate the problem we are working on we need something more powerful. There are of course many libraries that allow us to make animated and sometimes even interactive graphs like Bokeh, Pygal or my personal favorite Plotly. This time however, we will go old school — I will show you how to create really impressive charts using only “simple” Matplotlib and a few command line tricks. Inside the article I will place only the most important parts of the code. But on my GitHub, you can find full notebooks that were used to create shown visualizations. 2018-09-15 12:18:56.488000+00:00 Read the full story.  

Basic concepts of neural networks – Towards Data Science

After presenting, in two previous posts, the factors that have contributed to unleashing the potential of Artificial Intelligence and related technologies as Deep Learning, now is time to start to review the basic concepts of neural networks. In the same way that when you start programming in a new language there is a tradition of doing it with a Hello World print, in Deep Learning you start by creating a recognition model of handwritten numbers. Through this example, this post will present some basic concepts of neural networks, reducing theoretical concepts as much as possible, with the aim of offering the reader a global view of a specific case to facilitate the reading of the subsequent posts where different topics in the area will be dealt with in more detail. 2018-09-16 20:51:07.330000+00:00 Read the full story.  

Saykara raises $5M as its AI voice assistant cuts time doctors spend on paperwork by 70%

If you are a doctor in the U.S., chances are you spend a lot of time doing paperwork — sometimes double the amount of time you spend seeing patients. “Physicians today spend an inordinate amount of time, a tremendous amount of time, looking at a screen because they are required to capture documentation for a visit with a patient,” said Harjinder Sandhu, the CEO and founder of Saykara. Saykara is trying to change that with its AI-powered voice assistant, and the company just raised $5 million to help scale its technology. Another nugget of information the company revealed: Physicians using SayKara cut down their paperwork time by 70 percent. 2018-09-13 16:15:50-07:00 Read the full story.  

Adding emotional intelligence to artificial intelligence

While no one can deny AI is having an enormous effect on the finance industry, there are still limitations to the technology, especially in terms of its ability to react to emotional cues. Your customer’s emotions are an extremely important part of a business relationship, and the ability to read and comprehend these signals plays a huge part in tailoring the customer experience. Chatbots can’t easily detect a shift in tone or tension in a conversation and aren’t able to quickly appease a customer, and this is where the emotional intelligence is needed. For example, while a robo-advisor is great for an inexpensive and basic service, the issue comes when you have a more challenging or unique financial situation. A human advisor is best served here as they have the ability to account for personal needs or complex and sensitive circumstances, for example debt or divorce. This is where human employees are still required, to maintain customer relationships and avoid frustrating customers – or causing them to take their business elsewhere. 2018-09-12 00:00:00 Read the full story.  

Intake: Caching Data on First Read Makes Future Analysis Faster

Intake provides easy access data sources from remote/cloud storage. However, for large files, the cost of downloading files every time data is read can be extremely high. To overcome this obstacle, we have developed a “download once, read many times” caching strategy to store and manage data sources on the local file system. How It Works : Intake catalogs are composed of data sources, which contain attributes describing how to load data files. To enable caching, catalog authors can now specify caching parameters that will be used to download data file(s), store them in a configured local directory, and return their location on local disk. 2018-09-10 11:37:37-05:00 Read the full story.  

Baidu sets its sights on taking A.I. and self-driving cars outside China

Online search provider Baidu — referred to as the Google of China — has been expanding aggressively into cutting edge technology such as artificial intelligence and autonomous vehicles. 2018-09-14 00:00:00 Read the full story.  

5 Takeaways From Mark Zuckerberg’s Security Manifesto

Facebook Inc. (FB) co-founder and CEO Mark Zuckerberg has pledged to write a series of lengthy blog posts explaining how the social network is working to reduce the amount of divisive messages, propaganda and fake news engulfing its website. On Wednesday, Zuckerberg published his first post in the series, a roughly 3,270 word insight into how Facebook is progressing towards its goal to protect its website from election interference. Humans and Machines Blocking Fake Accounts : Zuckerberg revealed that Facebook hired more than 10,000 extra people this year and has been building systems based on advancements in machine learning to block millions of fake accounts every day. While admitting that “these systems will never be perfect,” he added that Facebook’s automated technology and 20,000-plus workforce are beginning to have a positive impact. 2018-09-13 04:35:00-06:00 Read the full story.  

Google’s music identification tool now recognizes tens of millions of songs

Google today announced the integration of Sound Search into Now Playing to vastly improve its ability to recognize songs on Android phones. Now Playing, which was introduced last year, uses on-device machine learning to recognize tens of thousands of popular songs. Sound Search, which predates Now Playing but was incorporated into the tool today, can identify tens of millions of songs. Sound Search operates on servers and is available via a long press of the Google Assistant button on Android phones or in the Google Search app. You can also add a shortcut to your home screen to quickly identify a song. 2018-09-14 00:00:00 Read the full story.  

JD.com CEO to no longer attend China AI forum after allegation of rape

SHANGHAI — JD.com CEO Richard Liu will no longer attend a high-profile state-run tech forum in Shanghai this week — an absence that comes after he was arrested on suspicion of rape in the US last month. Liu was arrested August 31 in Minnesota and was released the following day without charges and without paying bail but remains under investigation by the US police. He has, through his lawyers, denied any wrongdoing and returned to work in China. A spokeswoman for the e-commerce giant said Liu would not attend the forum but did not elaborate on the reason. The event is scheduled to run from Monday to Wednesday. 2018-09-16 00:00:00 Read the full story.  

AI merges Amazon reviews for a DVD workout kit with Morrissey lyrics to make one great song

In order to fully understand what’s happening here, it’s best to know that Morrissey is the mopey former frontman of the British indie rock band The Smiths. The P90X is an “extreme home fitness” kit featuring 12 DVDs designed to help users “get lean, bulk up, or grow stronger.” The creative minds at Seattle-based Botnik Studios decided there was comedy in both of those things, especially when artificial intelligence could be used to mash together the lyrics of Morrissey with the Amazon customer reviews for the P90X. 2018-09-10 22:43:25-07:00 Read the full story.  

ACM’s Code of Ethics Offers Updated Guidelines for Computing Professionals

The Association of Computing Machinery (ACM) has released an update to its Code of Ethics and Professional Conduct geared at computing professionals. The update was done “to address the significant advances in computing technology and the degree [to which] these technologies are integrated into our daily lives,” explained ACM members Catherine Flick and Michael Kirkpatrick, writing in Reddit. This marks the first update to the Code, which the ACM maintains “expresses the conscience of the profession,” since 1992. The goal is to ensure it “reflects the experiences, values and aspirations of computing professionals around the world,’’ Flick and Kirkpatrick said. The Code was written to guide computing professionals’ ethical conduct and includes anyone using computing technology “in an impactful way.” It also serves as a basis for remediation when violations occur. The Code contains principles developed as statements of responsibility in the belief that “the public good is always the primary consideration.” 2018-09-10 14:21:39+00:00 Read the full story.  

Weekly Selection — Sep 14, 2018 – Towards Data Science

 
  • Data Science Skills: Web scraping using python
  • When Bayes, Ockham, and Shannon come together to define machine learning
  • A Comprehensive Guide to the Grammar of Graphics for Effective Visualization of Multi-dimensional Data
  • Freezing a Keras model
  • Another Machine Learning Walk-Through and a Challenge
  • Fixing the Last Mile Problems of Deploying AI Systems in the Real World
  • Creating a Hybrid Content-Collaborative Movie Recommender Using Deep Learning
  • The Real Reason behind all the Craze for Deep Learning
2018-09-14 12:48:38.174000+00:00 Read the full story.  

GFT launches new AI platform accelerating enterprise-wide digitization for financial institutions

NEW YORK, September 12, 2018 – GFT, global IT engineering and technology specialists for the financial services industry for over 30 years – and Google Cloud services partner, has announced the launch of their GFT Streaming Enterprise Analytics Platform (SEAP) solution powered by Google Cloud. GFT introduced the solution in July at Google Cloud Next ’18 in San Francisco, alongside other industry innovators. SEAP provides real-time data ingestion and processing to support the consumption of actionable insights. With the ability to ingest any type of unstructured data, in a streaming format, the platform enables inflow analytics through use of AI and machine learning technology. Powered by Google Cloud technology, the on-premise design utilizes the latest big data streaming technology and can migrate readily for a cloud implementation. 2018-09-12 00:00:00 Read the full story.  
Behind Paywall or Data Farmers….

2018 NEXT-GENERATION DATA DEPLOYMENT STRATEGIES REPORT

Download the “Next-Generation Data Deployment Strategies” report to learn about the current state of machine learning, data lakes, Hadoop, Spark, object storage and more! Don’t miss out on these new insights about the hottest technology trends today. 2018-09-13 00:00:00 Read the full story. 2.8000000000000003  

Citigroup has found a new way to offer hedge funds obscure data that can give them an edge — and it’s part of a $2 billion investing gold rush

Alternative data sets aren’t so alternative anymore. At least according to Citigroup, which last week started giving clients access to data collected and analyzed by Thinknum, a four-year old startup which provides insights into a company’s health that aren’t readily available from conventional sources like financial reports and economic indicators. The banking giant becomes one of the first on Wall Street to gi… 2018-09-15 00:00:00 Read the full story. 1.714474118034949  
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AI & Machine Learning News. 10, September 2018

Self-driving (very small) cars — Part I – Towards Data Science – Doing A.I. in the wild with Python and Bluetooth cars

A.I. meets the physical world (on a budget). “I’m one of those boys that appreciates a fine body, regardless of the make” – Dominic Toretto, The Fast and the Furious. Autonomous vehicle startups are so hot right now: even without considering the usual suspects (Uber, Waymo, Cruise, etc.), there is a ton of less known and relatively less funded startups attacking the space (there’s even a Udacity course!). The key word here is relatively: it does not matter how you do it, it is just a steep price to pay compared to your standard SaaS company (as cool tools like lidar are very expensive + collecting training/test data is a pain). Why should these people have all the fun? Isn’t there anything we could do in our scrappy garage to start playing around? As it turns out, a common move in the industry is using simulators, from ad hoc physics engines to off-the-shelf video games (you can also try something in your browser!). However, we tried something else in our weekend hacking: instead of trading the physical world for a simulation of it, we chose to scale down the problem and work with (literally) a toy universe. 2018-09-09 22:11:31.214000+00:00 Read the full story. CloudQuant Thoughts… Jacopo had an idea, using AI to control cars in the real world for a low price. He opted for a cheap track based toy car game only to discover that just getting the Bluetooth via SDK communication going was a hard task all to itself. In part two he promises to deliver some AI experiments using his python controlled real-world driving system. This is so often the case with attacking a project, the initial chicanes prove way more difficult than you expected, but in the words of Randy Pausch, “the brick walls are there for a reason”. If you are like Jacopo and driven to succeed, why not apply that drive (instead of to tiny cars) to making real-world money in the stock market. We invite you python programmers to try CloudQuant and see if you can accelerate your wealth acquisition!  

Chicago Crime Mapping: Magic of Data Science and Python

Predictions, Forecasts and Loss scores. Sound too mainstream, don’t they? In the era of increasing interest towards Machine Learning and its algorithms, we are hugely ignoring important duties of being a data scientist, and one of those is Data Exploration. We, the modern data scientists are so naive that we forget the beauty of Visualizations and the quality it stands for. Today, allow me to present you an Exploratory Data Analysis of the Kaggle Dataset: Crime in Chicago. 2018-09-08 17:42:24.033000+00:00 Read the full story. CloudQuant Thoughts… As Uddeshya shows us, there is so much data out there that has simply not been looked at in enough ways. When we saw this story we were reminded of this post we saw on Reddit in the r/DataIsBeautiful subreddit. Even though the data is beautiful, the content is rather sad.
One year of accumulation of crime in central Chicago [OC] from dataisbeautiful
 

Failed AI Hedge Fund? Don’t Blame Artificial Intelligence, Blame the Program

I read the following piece from Bloomberg and all I could think was “DON’T BLAME THE AI”. Artificial intelligence is an incredible tool, and has myriads of applications both within and outside the trading realm. But it also has its limitations, inasmuch as it’s only as good as its’ programming. So it was with much fanfare that it was recently announced that an AI-based hedge fund, Sentient Investment Management, closed after less than 2 years, and after only earning 4% its first year. It used algorithms to create the equivalent of trillions of “virtual” traders and was said to be able to squeeze 1800 trading days into a few minutes, while scouring its multiple computers for the best trading strategies. NOW MAYBE (or obviously), that wasn’t such a great strategy to try to create. It managed to demonstrate how many terrible trading strategies might be possible, so much so that they overwhelmed the returns. 2018-09-09 12:59:02-04:00 Read the full story. CloudQuant Thoughts… “Before this year, the Eurekahedge AI Hedge Fund Index gained an average of 10.5 percent annually since its 2011 inception. This year, the measure of 15 funds is little changed…”. This story and the failure of Millenium’s Prediction Company demonstrate that, whilst AI and ML are definitely game changers in the financial industry and stock trading, they are not a magic pill. Human imagination should be the starting point for any strategy, right now these new technologies are working best when paired up with talented humans.  
Below the Fold…

Apple veteran: ‘Fast fail’ won’t work with health tech

Think about the last piece of technology you bought that didn’t work as expected. What did you do? Return it? Give it away? Put it in a drawer with its sad digital cousins? Most likely the stakes accompanying your poor experience were low, and you simply chalked it up to the cost of being an early adopter. What you didn’t do was abandon the field completely. If you were lucky enough to have spent your hard earned money on a Betamax, when that platform failed you didn’t swear off all forms of recorded entertainment. If you thought Chumby was the future of internet appliances, you haven’t refused to use an iPad or Alexa strictly on principle. 2018-09-06 00:00:00 Read the full story.  

Trends Poised to Disrupt the Wealth Management Industry

Competition in the wealth management industry is increasing. Looming on the horizon is a significant evolution in how business is conducted. Big data and advanced analytics technologies are key elements driving transformation in operations, risk, and compliance. Incumbents are using advanced analytics to garner insight from historical data, forecast behavior patterns, and predict outcomes and emerging trends. According to Boston Consulting Group, approximately 75% of wealth managers are planning to increase their use of big data and smart ana… 2018-09-06 04:34:49-04:00 Read the full story.  

NASDAQ’s  Oliver Albers : The Growing Value of (Alternative) Data (Video)

“When I started in this business, data was very, very uncool. Trust me.” Data is not uncool any longer. Consider that every minute there are more than 12 million texts sent and more than 4.3 million YouTube videos watched. It is estimated that humanity’s accumulated digital data haul will be more than 44 trillion gigabytes by 2020. There is even a band called Big Data. 2018-09-06 20:46:43+00:00 Read the full story.  

AI Weekly: Contrary to current fears, AI will create jobs and grow GDP

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week. Artificial intelligence’s growth comes as no surprise — a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 — but this week’s findings lay out in detail the likely socioeconomic impacts in the coming decade. 2018-09-07 00:00:00 Read the full story.  

The Gaming Industry Is Revolutionising Artificial Intelligence, One Win At A Time

Today, artificial intelligence is dominating most of the games — from board games to interactive fiction games. They are providing complex, decision-making environments for AI to experiment with. The ability of games to provide interesting and complex problems, offering creativity and expression, has made them one of the most popular and meaningful domain for AI researchers. Games offer one of the most meaningful domains that can process, interpret and stimulate human behaviour. The current gaming industry is not only deploying better graphics but is also exploring the area of virtual gameplay. The two-way relationship of gaming and AI has begun to tread a new road and it can be said that the gaming industry is largely revolutionising the way AI works. 2018-09-08 08:46:45+00:00 Read the full story.  

How Do Machine Learning Algorithms Differ From Traditional Algorithms?

Machine learning is an algorithm or model that learns patterns in data and then predicts similar patterns in new data. For example, if you want to classify children’s books, it would mean that instead of setting up precise rules for what constitutes a children’s book, developers can feed the computer hundreds of examples of children’s books. The computer finds the patterns in these books and uses that pattern to identify future books in that category. Essentially, ML is a subset of artificial intelligence that enables computers to learn without being explicitly programmed with predefined rules. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. This predictive ability, in addition to the computer’s ability to process massive amounts of data, enables ML to handle complex business situations with efficiency and accuracy. 2018-09-10 04:15:39+00:00 Read the full story.  

Unanimous AI achieves 22% more accurate pneumonia diagnoses

It’s no great mystery that artificial intelligence’s (AI) predictive prowess can significantly improve health care outcomes. AI’s been shown to outperform dermatologists in diagnosing melanoma, and a recent study by Google subsidiary DeepMind found that algorithms were on par with clinicians in detecting eye conditions. But what if AI could perform even better with the aid of humans? That’s the pitch Unanimous AI, a startup headquartered in San Francisco, has been giving for the better part of four years. There’s merit to it: In a study conducted with the Stanford University School of Medicine, the startup‘s system diagnosed pneumonia “significantly” more accurately — 22 percent — than a team of radiologists working alone and reduced errors by 33 percent. 2018-09-10 00:00:00 Read the full story.  

Understanding the Math behind the XGBoost Algorithm

Ever since its introduction in 2014, XGBoost has been lauded as the holy grail of machine learning hackathons and competitions. From predicting ad click-through rates to classifying high energy physics events, XGBoost has proved its mettle in terms of performance – and speed. I always turn to XGBoost as my first algorithm of choice in any ML hackathon. The accuracy it consistently gives, and the time it saves, demonstrates how useful it is. But how does it actually work? What kind of mathematics power XGBoost? We’ll figure out the answers to these questions soon. Tianqi Chen, one of the co-creators of XGBoost, announced (in 2016) that the innovative system features and algorithmic optimizations in XGBoost have rendered it 10 times faster than most sought after machine learning solutions. A truly amazing technique! In this article, we will first look at the power of XGBoost, and then deep dive into the inner workings of this popular and powerful technique. It’s good to be able to implement it in Python or R, but understanding the nitty-gritties of the algorithm will help you become a better data scientist. 2018-09-06 09:02:21+05:30 Read the full story.  

Samsung opens robotics-focused AI research hub in New York City

Samsung has opened its second U.S. artificial intelligence (AI) research facility (sixth globally), as the Korean electronics giant continues to double down on its investments in transformative technologies. Samsung announced last year that it was planning a new AI research hub, and in the intervening months it actually opened centers in Canada, the U.K., and Russia, in addition to existing facilities in Seoul (South Korea) and Mountain View, California. Its latest center, which will focus chiefly on robotics, is located in Chelsea, New York City and was officially opened at a ceremony featuring renowned AI expert Daniel D. Lee, executive vice president of Samsung Research. Lee joined the company a couple of months back and will lead the new AI center. 2018-09-10 00:00:00 Read the full story.  

US presidential hopeful: Free money can help save the country from jobs lost to robots

Entrepreneur Andrew Yang is running for U.S. president, and he’s made the robot revolution a central pillar of his election campaign for the 2020 race. Yang is the founder of Venture for America, a nonprofit that helps entrepreneurs create jobs, and he was previously the CEO of education firm Manhattan Prep. A Democrat, he has said the rise of automation and artificial intelligence will soon render millions of American jobs obsolete. To prevent widespread unemployment, he’s proposing monthly stipends of $1,000 for all citizens aged 18 to 64, no strings attached. 2018-09-10 00:00:00 Read the full story.  

Why Self-Service Analytics Has Gone Backward–and What To Do About It

During the past decade, the assertion that the data warehouse is required to be the center of an enterprise data system started to break down in a variety of ways. Reasons were numerous; they included such unwanted results as increasing complexity, loss of speed and agility and increasing costs. As a result, instead of analytics becoming increasingly self-service-oriented, for the first time the world of analytics was actually going backward, away from the self-service ideal. Progress hasn’t been completely undone, but compared to where IT was a few years ago, effective and easy-to-navigate self-service has become much more difficult to achieve. As a result, analysts are much more dependent on IT tools than ever before. 2018-09-06 00:00:00 Read the full story.  

Artificial intelligence poses greater threat than terrorism, expert warns

Jim Al-Khalili, the incoming president of the British Science Association has warned that the advent of artificial intelligence (AI) is posing more threat than terrorism in the world. The progress of AI is more rapid than previously thought and as of now, no regulations are made in this sector so far. He argued that the advent of AI in all courses of our life will lead to inequality as thousands of people will lose their jobs. He also added that the drastic rise in AI will make Britain more probe to dangerous cyber attacks. 2018-09-10 13:19:46+08:00 Read the full story.  

From Black Box ML to Glass Box XAI (Expandanble AI)

One of the difficulties in stepping into a red-hot technology space is you’re not sure what to expect. As you’re grappling with unexpected technical curve balls what if your own stakeholders beat you to death after seeing the results which are wrong by their expectations or understanding. The current nascent implementation of Artificial Intelligence and Machine Learning models are not a smooth sailing by any stretch of the imagination either. As much I am excited to work with the new technology and get new futuristic visions to see the sunshine of reality, I also know the labor that goes in to get it out there in a production system is no less than breaking a mountain, and what if you realize that the rocks underneath is not conducive to build a smooth road. All that effort gets dumped in a breakneck speed and with it goes the dream of bringing something new to being and get it implemented. 2018-09-08 03:14:10 Read the full story.  

Researchers develop a method that reduces gender bias in AI datasets

Word embedding — a language modeling technique that maps words and phrases onto vectors of real numbers — is a foundational part of natural language processing. It’s how machine learning models “learn” the significance of contextual similarity and word proximity, and how they ultimately extract meaning from text. There’s only one problem: Datasets tend to exhibit gender stereotypes and other biases. And predictably, models trained on those datasets pick up and even amplify those biases In an attempt to solve it, researchers from the University of California developed a novel training solution that “preserve[s] gender information” in word vectors while “compelling other dimensions to be free of gender influence.” They describe their model in a paper (“Learning Gender-Neutral Word Embeddings“) published this week on the preprint server Arxiv.org. 2018-09-07 00:00:00 Read the full story.  

Trump’s battle against Silicon Valley may create an opening for China in artificial intelligence

There is little doubt that the Department of Defense needs help from Silicon Valley in order to compete with China in the race for artificial intelligence. The question is whether Silicon Valley is willing to cooperate and whether President Donald Trump’s combative nature risks damaging the vital partnership. Last week reports surfaced that Secretary of Defense Jim Mattis had warned Trump that the United States is not keeping pace with the ambitious plans of China in artificial intelligence. Instead, Trump attacked Google, Facebook and Twitter on Twitter last week, accusing the tech giants of intentionally suppressing conservative news outlets supportive of his administration. More than that, he aggravated the rift between the government and tech industry. 2018-09-08 00:00:00 Read the full story.  

5 Reasons Amazon May Double to $2 Trillion

Amazon Inc. (AMZN) has seen its market value jump seven-fold in just five years, becoming the second U.S. corporation to surpass the $1 trillion mark briefly on Tuesday and reaching that milestone nearly twice as fast as smartphone maker Apple Inc. (AAPL). Now, bulls see at least five forces which could double the value of the e-commerce and cloud-computing giant to reach $2 trillion, including its cloud business, surging ad sales, opening of physical stores, artificial intelligence (AI) push and health care business, according to a detailed story by CNBC. 2018-09-06 04:00:00-06:00 Read the full story.  

BrainChip Announces the Akida™ Architecture, a Neuromorphic System-on-Chip

BrainChip Holdings Ltd., the leading neuromorphic computing company, today establishes itself as the first company to bring a production spiking neural network architecture – the Akida Neuromorphic System-on-Chip (NSoC) – to market. This architecture announcement firmly positions BrainChip as the leader in acceleration for artificial intelligence (AI) at the edge and the enterprise. The Akida NSoC is small, low cost and low power, making it ideal for edge applications such as advanced driver assistance systems (ADAS), autonomous vehicles, drones, vision-guided robotics, surveillance and machine vision systems. Its scalability allows users to network many Akida devices together to perform complex neural network training and inferencing for many markets including agricultural technology (AgTech), cybersecurity and financial technology (FinTech). 2018-09-10 04:01:00+00:00 Read the full story.  

A Beginners Guide To Dopamine Reinforcement Learning Framework

Reinforcement learning algorithm, soon becoming the workhorse of machine learning is known for its act of rewarding and punishing an agent. This acts as a bridge between human behaviour and artificial intelligence, enabling leading researchers to work on artistic discoveries in this domain. The recent success of Deepmind’s AlphaGo in defeating the world champion at Go and OpenAI’s Dota 2 bots thrashing the game’s veteran players with just six months of training is a notable achievement in the area of Reinforcement Learning. This versatile research platform required an environment to test the new ideas and to play with the models in the mind of researchers. This is the reason why Dopamine was built and to enhance the work of individuals and teams passionate about reinforcement learning. Dopamine, the newest research framework released by Google, is geared at fast prototyping development of reinforcement learning algorithms. It provides that key missing piece for researchers, that is, benchmarking abilities with 60 different atari arcade games. Agents such as DQN, C51, Rainbow Agent and Implicit Quantile Network are the four-values based agents currently available. 2018-09-07 09:53:58+00:00 Read the full story.  

When Bayes, Ockham, and Shannon come together to define machine learning

It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase which fuses some of the core concepts of statistical learning, information theory, and natural philosophy into a single three-word-combo. And, it is not just a obscure and pedantic phrase meant for machine learning (ML) Ph.Ds and theoreticians. It has a precise and easily accessible meaning for anyone interested to explore, and a practical pay-off for the practitioners of ML and data science. I am talking about Minimum Description Length. And you may be thinking what the heck that is… Let’s peal the layers off and see how useful it is… 2018-09-08 22:08:19.092000+00:00 Read the full story.  

From IBM To Mastercard; Tech Giants Are Using Predictive Analytics To Reduce Employee Attrition

Predictive analytics has been pegged as the key to addressing employee attrition. It has emerged as the missing link for the human resources department which lacks the analytical ability in bolstering their reporting. Also, the combination of right analytical approach is crucial to address the biggest pain point for HR — retaining talent. The other important issue is also about identifying the employees who have a propensity to leave, and how to retain them. Predicting employee turnover is one of the most common use cases in HR analytics. The turnover rate can be identified in HR reporting, by assessing various parameters such as employee profile, satisfaction evaluation, performance evaluation, project planning and evaluation, absence and time sheets and communications and interaction schemas, among others. However, a certain amount of attrition is unavoidable and largely unpredictable, says a whitepaper from TCS, since companies can never gather all the data that went into each decision. 2018-09-10 06:06:36+00:00 Read the full story.  

Scalable methods for explaining machine learning – Towards Data Science

Machine learning is often called the part of AI that works. Yet, there is a growing unease that we do not really understand why it works so well. There is also the fear of algorithms gone wild. These criticisms are not entirely fair. The stalwarts in the field who designed some of the most successful algorithms do understand why things work. However, this is a form of understanding that is as amorphous as it is deep. Machine learning algorithms are complex, and explaining complex objects is never easy. But as machine learning matures as a field and becomes ubiquitous in software, it is perhaps time to talk about more tangible forms of understanding and explanation. In this post I will discuss why our conventional methods of understanding are not very useful for machine learning. And then I will discuss an alternate method of understanding that might be more suitable for the specific context of machine learning. Nothing that I discuss in this post is new. It should all be very familiar to the seasoned practitioners. This is merely my attempt to make explicit some of the implicit wisdom in the field. 2018-09-09 17:28:58.135000+00:00 Read the full story.  

MIT CSAIL uses AI to teach robots to manipulate objects they’ve never seen before

Few fields have been transformed by artificial intelligence (AI) more than robotics. San Francisco-based startup OpenAI developed a model that directs mechanical hands to manipulate objects with state-of-the-art precision, and Softbank Robotics recently tapped sentiment analysis firm Affectiva to imbue its Pepper robot with emotional intelligence. The latest advancement comes from researchers at the Massachusetts Institute of Technology’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who today in a paper (“Dense Object Nets: Learning Dense Visual Object Descriptors and Application to Robotic Manipulation”) detailed a computer vision system — dubbed Dense Object Nets — that allows robots to inspect, visually understand, and manipulate object they’ve never seen before. 2018-09-09 00:00:00 Read the full story.  

Koch Disruptive Technologies’ Jason Illian to discuss investing in AI at VentureBeat’s Blueprint event

Jason Illian, managing director of Koch Disruptive Technologies, is one of the speakers who will be appearing at VentureBeat’s upcoming Blueprint conference in York, Pennsylvania on October 9-11. At the event, speakers including Illian will discuss how both private companies and Heartland cities can prepare to capitalize on advancements in automation and AI. Koch Disruptive Technologies is a subsidiary of the 70-year-old Koch Industries. Though Koch Industries is most well known for its oil and gas and manufacturing subsidiaries, KDT isn’t looking to invest exclusively in those industries. Since the fund started operating in November, KDT has invested in two companies: medical device company Insightec, which develops MRI-guided ultrasounds for surgery, and Mesosphere, which develops a hybrid cloud platform that helps companies automate operations for container, data engineering, and machine learning tools. 2018-09-07 00:00:00 Read the full story.  

Deep Learning Algorithms Are Performing Calculations At The Speed Of Light

Hardware forms the core aspect of AI applications. Apart from computing power, an efficient and quick mode of relaying information between network layers is equally important. This is where optical techniques are now explored by researchers. In fact, these optical systems can be aggregated into hardware elements such as GPUs. This article discusses a particular research study by scholars from The University of California, Los Angeles (UCLA), where the team designed neural networks for two tasks, handwritten digit recognition and as an imaging lens. 2018-09-10 09:02:05+00:00 Read the full story.  

Gadi Singer interview — How Intel designs processors in the AI era

Intel is the world’s biggest maker of processors for computers, but it hasn’t been the fastest when it comes to capitalizing on the artificial intelligence computing explosion. Rival Nvidia and other AI processor startups have jumped into the market, and Intel has been playing catchup. But the big company has been moving fast. It acquired AI chip design firm Nervana in 2016 for $350 million, and Intel recently announced that its Xeon CPUs generated $1 billion in revenue in 2017 for use in AI applications. Intel believes that the overall market for AI chips will reach between $8 billion and $10 billion in revenue by 2022. And the company is focused on designing AI chips from the ground up, according to Gadi Singer, vice president and general manager of AI architecture at Intel. 2018-09-09 00:00:00 Read the full story.  

Years after patenting the concept, Amazon admits putting workers in a cage would be a bad idea

Warehouse workers confined in cages? That’s the dark vision evoked by an essay delving into the worries that come along with the development of artificial-intelligence devices such as the Amazon Echo speaker. “Anatomy of an AI System” was published on Friday by the AI Now Institute and Share Lab — and it’s already gotten a rise from the executive in charge of Amazon’s distribution system, who says the cage concept never ended up being used. 2018-09-08 20:40:04-07:00 Read the full story.  

Market Basket Analysis on Online Retail Data – Towards Data Science

Have you ever noticed that bread and milk are often far away from each other in a grocery store, even though they are normally purchased together? Why is that? That’s because they want you to walk all over the store and notice other items inbetween bread and milk and perhaps buy some more items. This is a perfect example of an application of Market Basket Analysis (MBA). MBA is a modeling technique based upon the theory that if you buy a certain set of items, you are more or less likely to buy another set of items. It is an essential technique used to discover association rules that can help increase the revenue of a company. In one of my previous post (Preprocessing Large Datasets: Online Retail Data with 500k+ Instances) I explained how to wrangle a huge data set with 500000+ observations. I am going to use the same data set to explain MBA and find the underlying association rules. 2018-09-08 20:53:32.978000+00:00 Read the full story.  

Standard Cognition beats Amazon to cashierless store in San Francisco

Startup Standard Cognition today announced plans to open a cashierless store in San Francisco in the coming days. Named Standard Market, the store will operate with limited hours and is a testing site for Standard Cognition’s artificial intelligence that uses cameras to track the movement of shoppers throughout a store. When you arrive at Standard Market, located at 1071 Market Street, you use the Standard Checkout app to check in, then just walk out with whatever you want. Initial products for sale in the 1,900-square foot store will include a mix of food, cleaning supplies, and general household or convenience store items. 2018-09-07 00:00:00 Read the full story.  

Weekly Selection — Sep 7, 2018 – Towards Data Science

 
  • Practical Advice for Data Science Writing
  • Recurrent Neural Networks: The Powerhouse of Language Modeling
  • Probability concepts explained: Rules of probability
  • Deep learning and Soil Science (Part 1, Part 2)
  • How to Create Animated Graphs in Python
  • Storytelling for Data Scientists
  • How machines understand our language: an introduction to Natural Language Processing
2018-09-07 16:19:09.528000+00:00 Read the full story.
Behind a Paywall  

NHS will need 50 per cent more staff in a decade if it does not embrace technology

he NHS will need 50 per cent more staff in a decade if it does not embrace the use of new technology like artificial intelligence, one of its top bosses has warned. Prof Ian Cumming, chief executive of Health Education England, which is in charge of NHS staffing, said persuading the public to overhaul dangerously unhealthy lifestyles was also key to lifting increasing demand on the service. He issued the stark warning as the Government prepares to publish a 10 year plan for the health service. 2018-09-08 00:00:00 Read the full story.    
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.
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AI & Machine Learning News. 03, September 2018

Baidu launches EZDL, an AI model training platform that requires no coding experience

Without the technical know-how and the right tools, training machine learning algorithms can be an exercise in frustration. Luckily, for folks who don’t have the wherewithal to wade through the jargon, Baidu this week launched an online tool in beta — EZDL — that makes it easy for virtually anyone to build, design, and deploy artificial intelligence (AI) models without writing a single line of code. Baidu’s EZDL was built with performance, ease of use, and security in mind, said Youping Yu, general manager of Baidu’s AI ecosystem division, and it targets three broad categories of machine learning: image classification, object detection, and sound classification. It’s aimed at small and medium-sized businesses, with the goal of “breaking down the barrier” to allow everyone to access AI “in the most convenient and equitable way,” Yu said. 2018-09-01 00:00:00 Read the full story. CloudQuant thoughts… “Super Simplification”. By limiting the roles to three well-established ML categories and steering the users upload process (20-100 labeled images, or more than 50 labeled audio files), Baidu has “Super Simplified” machine learning for businesses. We can all identify some simple ML processes we could design with such a basic but simple to use system. “Generated algorithms can be deployed in the cloud and accessed via an API, or downloaded in the form of a software development kit that supports iOS, Android, and other operating systems.” Super Simplification is the way of the future. Developing tools that make life easier for the end user to achieve massive results in a short space of time is always the ideal software goal. The type of things ordinary users can achieve with Image Software like Photoshop or Music Software like GarageBand was unimaginable twenty years ago. We are aiming to bring some of this simplification to the Auto-Trading model generating world. We believe we have hit the ideal point between making the data easy to use without making it so basic that your creativity is limited. Watch this space!  

Detecting ‘deepfake’ videos in the blink of an eye using Machine Learning

A new form of misinformation is poised to spread through online communities as the 2018 midterm election campaigns heat up. Called “deepfakes” after the pseudonymous online account that popularized the technique – which may have chosen its name because the process uses a technical method called “deep learning” – these fake videos look very realistic. 2018-08-30 Read the full story (CNBC). 2018-08-30 Read the full story (ibtimes).

How Machine Learning Developed the Face of MCU’s Thanos

The very look of Thanos has changed slightly from movie to movie. One of several visual effects companies working on Infinity War, Digital Domain (which worked on the live-action Beauty and the Beast) wanted to include more of Josh Brolin’s facial features in Thanos’ character. Marvel Studios started working with the company several months before the official shooting for effects testing. That’s where AI stepped in to help with the “movie magic.” Masquerade, a new machine learning software, was used to obtain the desired effect. Between 100 and 150 tracking dots were attached to the actor’s face and then recorded by a pair of HD cameras. These are actually pretty low-quality recordings, but they serve their purpose. The recordings are then sent through the machine learning algorithm which contains a vast collection of high-resolution facial scans featuring a broad range of emotions. 2018-09-02 16:55:48.997000+00:00 Read the full story.   CloudQuant Thoughts… Unfortunately AI and ML have already made it so that we can no longer trust what we see. Always be skeptical, always check your sources. The tech seen above, from DeepFake to Fake dancing may look obviously fake right now but the technology used to create the new Thanos at an extreme HD cinema resolution will be on our desktops or even our phones before you know it.  

Predicting Popularity of The New York Times Comments in R (Part 1)

The New York Times (NYT) has a large reader base and plays an important role in shaping public opinion and outlook on current affairs and also in setting the tone of the public discourse, especially in the U.S. The comments sections for articles in the NYT are quite active and give insights to readers’ opinions on the subject matter of the articles. Each comment can receive other readers’ recommendations in the form of upvotes. Challenges for NYT moderators :
  • Up to 700 comments per article with NYT moderators manually reviewing ~12,000 comments in a day.
  • Moderators need to make faster decisions on screening and sorting comments based on their predicted relevance and popularity.
  • Finding an easier way to group similar comments and maintain a useful conversation among readers.
2018-09-04 00:55:55.107000+00:00 Read the full story. CloudQuant Thoughts… An interesting article by Sakshi Gupta, in R for her capstone project for her big data certification (Ryerson University, Toronto). Of particular interest was the use of a BING word sentiment scoring system. Perhaps you can adapt this to scan news articles coming through with our trading data at app.cloudquant.com, or perhaps you can just use our pre-classified sentiment data from various high-quality sources.  

5 Tech Hardware Stocks to Outperform: MarketWatch

While the market is swooning over hot tech names in new markets like artificial intelligence (AI), cloud computing, autonomous driving and virtual reality, one less-exciting segment of the tech space is set for major gains, according to a recent story by MarketWatch that highlighted five hardware stocks to buy in 2018. (See also: Chip Stocks on Verge of Big Breakout: Todd Gordon.) While the big-picture trends make emerging technology markets compelling, MarketWatch’s Jeff Reeves highlighted the “rather boring but equally powerful subsector of high-tech hardware,” which has had its fare share of outperforming companies in recent years. Within the group, comprised of companies that rely on the sale of chips, semiconductors and related components, he likes Advanced Micro Devices Inc. (AMD), Micron Technology Inc. (MU), Seagate Technology PLC (STX), NVIDIA Corp. (NVDA) and Pure Storage Inc. (PSTG). 2018-08-30 12:15:00-06:00 Read the full story. CloudQuant Thoughts… Do you agree with Jeff? A number of these symbols have already doubled or tripled in the last two years. I would say they have already achieved “major gains”. Can you do better, can you come up with a list algorithmically? Give it a try on our free backtesting system (Python knowledge required) at app.cloudquant.com.  

Facebook is using unsupervised machine learning for translations

Facebook has begun using unsupervised machine learning to translate content on its platform when it doesn’t have many examples of translations from one language to another — such as from English to Urdu. The method was devised by Facebook AI Research (FAIR) and is being used on the platform in a collaborative effort between FAIR and the Applied Machine Learning division of the company, FAIR Paris lab director Antoine Bordes told VentureBeat in a phone interview. The approach performs about as well as supervised models with 100,000 translations from one language to another, and it outperforms systems for language pairings for which Facebook has few examples. “When you are on cases like English-Urdu, where there’s very few [translations], there we show that our system is better than the supervised system. So it’s better to train an unsupervised system than a supervised system that doesn’t have enough data,” Bordes said. 2018-08-31 00:00:00 Read the full story.

Python “Speech Recognition Data Collection” with Youtube API, FFMPEG, Mask-RCNN and Google Vision API

With the rapid development of Machine Learning, especially Deep Learning, Speech Recognition has been improved significantly. Such technology relies on a large amount of high-quality data. However, models built for less-popular languages perform worse than those for the popular ones such as English. This is due to the fact that there is only a limited training dataset, and that it is hard to collect high-quality data effectively. While Mozilla has launched an open-source project called Common Voice, which encouraged people to contribute their voices last year, most people are either not aware of this project, or not willing to participate in it.  Thanks to the abundance of TV shows and dramas available on Youtube, it is possible to collect speech recognition data in a highly efficient manner with almost human involvement. This blog post will show you how to efficiently collect speech recognition data for any language. 2018-08-30 Read the full story. CloudQuant Thoughts… Lots of people are trying to solve the translation to/from less-popular languages. Facebook is relying on an unsupervised system to carry out these translations, and very successful it sounds with Antoine Bordes saying “We could go now on a planet where people speak a language that nobody else speaks — okay, the aliens — and you can actually go and try to have a decent translation of what is said there”.  黃功詳 Steeve Huang’s process is to go through a large number of individual steps to “create a data set” for training a speech recognition system using TV shows with subtitles on YouTube. He downloads them, removes the audio via FFMPEG, then removes individual frames and processes them for characters that have been encoded as subtitles on the video. Finally, he produces an audio file and a subtitle file that can be passed to a Speech Recognition training system. It is a meticulously planned process that seems to work extremely well for him. Major Kudos!  
 

AiServe is developing assistive AI that ‘learns to walk like a human’

Visual impairment is one of the world’s most common ailments. An estimated 253 million people live with vision problems, with blindness affecting 36 million of those. Canes, guide animals, and specially adapted crosswalks make navigating busy sidewalks and city streets a little easier, but they’re not always practical. The folks behind Berlin-based AiServe believe that artificial intelligence — specifically, natural language processing and computer vision — might be able to lend an unobtrusive helping hand. The system will run on a wearable with a camera, microphone, and a battery that lasts a few hours on a charge. As it ingests new visual data, it’ll start to recognize sidewalks, corners, and pathways with greater confidence, and in time it will map out entire city blocks and neighborhoods. The computer vision algorithm’s data will inform a navigational component that, through voice commands and other cues, will help wearers get from one place to another. Its instructions will be much more precise than most mapping apps, Madico said — rather than naming particular streets or thoroughfares, it’ll say something like “Make a left turn at this corner” or “Walk straight ahead for 100 feet.” 2018-09-01 00:00:00 Read the full story.  

5 Cloud Computing Trends To Prepare For in 2018 – Hacker Noon

Cloud computing can literally be anything that allows you to achieve development tasks or run software through some other service provider over the internet. Cloud computing can help shorten development times, use less human resources, and provide service and availability guarantees to your clients. We had a chat with Justin Kilimnik, Director, Technology Advisory at EY to see which trends he’s excited about this year. “What key trends in cloud computing do you see emerging in the year ahead?”
  1. Docker
  2. Micro-service architecture and solutions
  3. Function-as-a-Service & Serverless
  4. Machine Learning Optimised platforms, IoT architectures
  5. Poly-cloud strategies
2018-09-03 16:53:50.504000+00:00 Read the full story.  

AdobeStock_15670237510 Big Financial Technology Trends for 2018

2018 promises to be the year we see the culmination of some key technologies — from blockchain and intelligent AI, to design thinking and the cloud. Here are the 10 biggest trends identified by reports from Synechron and Capgemini.
  1. Massive Investments in Digital Transformation
  2. The Frontiers of Innovation: AI & Blockchain
  3. Digital-Only Banks Become a Real Threat
  4. Design Thinking
  5. Real-Time Risk Decisions
  6. Alternative Lenders Leverage Alternative Data
  7. RegTech
  8. Big Data Gets Even Bigger
  9. Connecting With Third-Party Providers to Drive Customer-Centricity
  10. The Cloud: Creeping Into Every Corner
2018-09-04 Read the full story.  

Trump, forget Google — focus on national security AI

This week, President Trump took shots at Google for what he calls unfair search results for his name and unfair treatment of conservatives by Silicon Valley liberals. In this same vein, he talked about how some people see “an antitrust situation” with Google, Amazon, and Facebook. Before Trump’s latest Twitter tirade began, on Sunday the New York Times reported that Defense Secretary Jim Mattis wrote a memo to President Trump earlier this year asking him to create a national strategy for AI akin to the kind China has created. China’s strategy was introduced last year and aims to make China the world leader in AI by 2030, in part through “military-civil fusion” with companies like Baidu and Tencent. But even if he does actually believe Google treated him unfairly, it may not be in the best interest of the United States to argue with a company closely associated with the growth of AI and tools like TensorFlow. Right now, he should probably be listening to his defense secretary and thinking about what a national AI strategy should look like for the United States, and exploring the topic with companies like Google. If you believe, as Vladimir Putin does, that the nation that leads in AI will control the world, apparently there’s a lot at stake, and national security is a president’s first responsibility. 2018-08-31 00:00:00 Read the full story.  

Thinking differently about data: the new opportunity for banks

Often described as the ‘new oil’ or even the currency of the digital economy, data is key to the business strategy of every organization. For banks, building an understanding of how to harness the variety, velocity and volumes of data available to them will dictate the difference between success and failure in the very near future. To understand the forces at work here, it helps to paint a picture of why and how data is front and center of business planning. One reason is the absolute magnitude of data available: it’s said that 90% of all data that exists today was generated in the last two years. Data is created by social media, in the cloud, by IoT devices, on increasingly open and hyper-connected IT systems and is accessible via high performance network bandwidths. 2018-09-03 00:00:00 Read the full story.  

High Hopes for Artificial Intelligence Don’t Always Match Reality

If you’re worried about your Echo Dot becoming self-aware and morphing into a job-stealing killing machine, worry not: That’s not happening anytime soon. From Amazon’s Alexa to Apple’s Siri, tech companies love to talk up the limitless potential of AI and machine learning to solve all manner of problems, great and small. Salesforce’s Einstein tool, described as an AI layer to its core product suite, offers suggestions to speed up sending emails and other business processes. Facebook CEO Mark Zuckerberg has repeatedly suggested that AI will solve the social network’s many issues with data security and abuse. Meanwhile, Alphabet’s DeepMind unit – the result of a $500 million acquisition in 2018 – has gotten really, really good at board games. But the line between technology, marketing-speak and overactive imaginations isn’t always that clear. AI has the potential to excite investors — some have even speculated that AI businesses could someday be worth five to ten times more than today’s consumer Internet companies. There are serious roadblocks to getting there, and one is a shortage of AI expertise. 2018-08-31 20:59:03-04:00 Read the full story.  

An army of bots supporting Sweden Democrats is growing explosively ahead of September’s election

The Swedish Defence Research Agency FOI issues a warning with less than two weeks until the general election. The number of fake Twitter accounts discussing Swedish politics is soaring – and almost every other is tweeting support for the Sweden Democrats.
  • Fake accounts tweeting about Swedish politics have doubled in number ahead of the country’s general election.
  • A much larger than proportionate share is expressing support for the right-wing populist party Sweden Democrats.
  • This according to a new study by the Swedish Defence Research Agency FOI.
2018-08-29 16:54:48 Read the full story.  

LG To Strengthen AI, Robotics Business Amid Struggling Smartphone Unit

LG CEO Jo Seoung-Jin showed up at Internationale Funkausstellung (IFA) Berlin late last week and told reporters about LG’s plans moving forward. According to him, the company is strengthening its AI and robotics business this year by increasing the number of its engineers and expanding the support base for its AI technology and robots. “The world is heading toward an era of AI and that embracing the new trend is critical”. 2018-09-03 09:22:12-04:00 Read the full story.  

JPMorgan Hires Top AI Exec Away From Google

As the race on Wall Street to deploy artificial intelligence (AI) ramps up, JPMorgan Chase & Co. has shown how serious it is about its next-gen tech initiative with another major hire this week. The bank hired Apoorv Saxena, Alphabet Inc.’s head of product management for cloud-based AI, according to a memo obtained by CNBC. The senior Google executive will start at JPMorgan on Aug. 31 as head of AI and machine learning services and will also be responsible for leading the firm’s AI-powered asset and wealth management technology initiative. Recruiting talent has been an integral part of a larger strategy among traditional financial institutions in order to develop AI for improved and automated services like fraud detection, internal operations and loan approval. 2018-08-29 11:41:00-06:00 Read the full story.  

Lite Intro into Reinforcement Learning

This is a brief introduction into Reinforcement Learning (RL) going through the basics in simplified terms. We start with a brief overview of RL and then get into some practical examples of techniques solving RL problems. In the end you may even think of places you can apply these techniques. I think we can all agree building our own Artificial Intelligence (AI) and having a robot do chores for us is cool… so let’s get to it! 2018-09-03 21:10:09.394000+00:00 Read the full story.  

Is hybrid cloud the best of both worlds? Five experts explain how to get it right

Fervent cloud computing evangelists used to look down their noses at the notion of hybrid cloud, seeing the approach a cop-out for slow-moving and shallow-minded tech organizations. Things have most certainly changed. We invited five experts to help attendees at the 2018 GeekWire Cloud Tech Summit understand how big companies and growing startups are implementing hybrid cloud strategies.
  • Alex Legault, Associate Director of Products, PitchBook
  • Jin Zhang, Director, Product Management VMware & Hybrid Computing, Amazon
  • Nicholas Criss, Sr. Manager, Cloud Center of Excellence, T-Mobile
  • Madhura Maskasky, Co-founder, Platform9
  • Anthony Skinner, CTO at iSpot.tv
2018-09-02 19:37:33-07:00 Read the full story.  

Apple’s Pious Privacy Pledges Ring Hollow

Privacy is the new battleground. Sounds simple, but it’s not. This war is really about the power of networks, and legacy versus disruption. There is no debate. Apple is succeeding in China. Oddly, its advocacy for user data privacy does not extend to the People’s Republic. Apple transferred the operation, including the security keys, of its Chinese iCloud service to Guizhou-Cloud Big Data, in February. GCBD is a state-owned enterprise. 2018-09-03 08:00:00-04:00 Read the full story.  

Musk Personally More Likely Than Tesla to Have to Pay Back the Shorts

That might have been a billion-dollar tweet. If anyone is going to pay for Elon Musk’s misleading tweet that he had secured the funding to take Tesla (TSLA – Get Report) private, it will likely be the Tesla CEO himself, and not his electric car company, lawyers said. Short sellers who target Tesla because of the view that the company’s stock price isn’t justified by its profit prospects may have lost about $1.3 billion on August 7, the day of the misleading tweet, although most investors with short positions in Tesla didn’t cover their positions that day, according to financial data analytics firm S3 Partners. Total losses in August for the short sellers may be have been as much as $3 billion, S3 said. The losses prompted at least two lawsuits to be filed in federal court in San Francisco, and law firm Pomerantz LLP has even provided a draft complaint on its website for investors contemplating suing both Musk and Tesla. 2018-08-28 16:45:10-04:00 Read the full story.  

Google releases AI-powered Content Safety API to identify more child abuse images

Google has today announced new artificial intelligence (AI) technology designed to help identify online child sexual abuse material (CSAM) and reduce human reviewers’ exposure to the content. The move comes as the internet giant faces growing heat over its role in helping offenders spread CSAM across the web. Last week, U.K. Foreign Secretary Jeremy Hunt took to Twitter to criticize Google over its plans to re-enter China with a censored search engine when it reportedly won’t help remove child abuse content elsewhere in the world. News emerged last year that London’s Metropolitan Police was working on a AI solution that would teach machines how to grade the severity of disturbing images. This is designed to solve two problems — it will help expedite the rate at which CSAM is identified on the internet, but it will also alleviate psychological trauma suffered by officers manually trawling through the images. Google’s new tool should assist in this broader push. 2018-09-03 00:00:00 Read the full story.  

Silicon Valley is under pressure again, this time over online paedophilia

Home Secretary Sajid Javid has warned about the sheer volume of pedophilic content available online. He is expected to announce new measures to combat the problem later on Monday. The UK’s National Crime Agency revealed that it received over 80,000 industry referrals for child sex abuse images in 2017, which represents a 700% increase since 2012. The agency asked tech companies to cooperate with law enforcement more to combat online child sexual abuse. Google also announced on Monday that it is rolling out a new AI tool to help NGOs and industry partners track down child abusers. 2018-09-03 00:00:00 Read the full story.  

Google launches AI tool to identify online child sex abuse images (Paywall)

Google has unveiled new technology designed to identify images of child abuse, as the Silicon Valley giant sought to stave off mounting pressure for action to tackle exploitative content. The US company said it was releasing free ‘cutting edge’ artificial intelligence software that would help web moderators root out abusive content on the Internet on a large scale. Google said the technology “significantly advances our existing technologies to dramatically improve how service providers, NGOs, and other technology companies review this content at scale.” 2018-09-03 00:00:00 Read the full story.  

Crowdsourcing in the age of artificial intelligence: How the crowd will train machines

It was over 10 years ago that I was introduced to the concept of crowdsourcing. I was a student at London Business School when a professor one day came into the classroom with a jar of pennies. He asked us each to take a look at the jar and guess the correct amount of money inside. The jar went around the classroom and I gave it an estimate of £30 in good faith. The professor duly wrote down each of our 100 guesses on the whiteboard and then opened a sealed envelope where the real amount was revealed: £18.76. While my initial lesson learned was that I shouldn’t ever try a career in penny guessing, the amazing surprise was still in store for me: The professor calculated the average of all our 100 guesses and it magically came down to £18.76. The wisdom of the crowd was spot on and was better than 99 percent of our own estimates (only one of us actually guessed the right amount). 2018-09-01 00:00:00 Read the full story.  

The man in the red polo: Meet Scott Guthrie, Microsoft CEO Satya Nadella’s front-line general in the cloud wars with Amazon and Google

Meet Scott Guthrie, the executive VP of Microsoft’s cloud and artificial intelligence business. He’s a long-time Microsoft exec, and a trusted lieutenant to CEO Satya Nadella — the two worked together to get the Microsoft Azure cloud business off the ground. He’s as well known for his red polo shirt as he is for his technical acumen and leadership skills. In this interview, Guthrie discusses what he learned from working with Netflix on streaming, how he works with Nadella, and what he believes is Microsoft’s secret weapon in the cloud wars with Amazon and Google. 2018-09-03 00:00:00 Read the full story.  

Weekly Selection — Aug 31, 2018 – Towards Data Science

 
  • How to construct valuable data science projects in the real world
  • Convolutional Neural Networks: The Biologically-Inspired Model
  • Making Music: When Simple Probabilities Outperform Deep Learning
  • How to Run Parallel Data Analysis in Python using Dask Dataframes
  • Named Entity Recognition and Classification with Scikit-Learn
  • A Day in the Life of a Marketing Analytics Professional
  • Changing The Engineer’s Mindset : From How to Why
  • Tutorial: Double Deep Q-Learning with Dueling Network Architecture
  • Automatic Speech Recognition Data Collection with Youtube V3 API, Mask-RCNN and Google Vision API
  • How to build a non-geographical map #1
2018-08-31 13:17:44.165000+00:00 Read the full story.  
Behind a Paywall…

Britain faces an AI brain drain as Silicon Valley raids its top universities for talent

Around a third of leading machine learning and AI specialists who have left the UK’s top institutions are currently working at Silicon Valley tech firms. More than a tenth have moved to North American universities and nearly a tenth are currently working for other smaller US companies. Meanwhile just one in seven have joined British start-ups. 2018-09-02 Read the full story.  

Tech giants keep British artificial intelligence in mind

For mere mortals job hunting can be a slog. Updating your CV, writing a cover letter, having a phone interview, an in-person interview, a second interview. It can seem like a never-ending process. But for specialists in the red-hot field of artificial intelligence (AI), job offers rain down with little effort. With billions of dollars flowing into the sector, around the world big technology companies are engaged in a scramble for talent. 2018-09-02 00:00:00 Read the full story.  
This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. If you would like to add your blog or website to our search crawler, please email customer_success@cloudquant.com. We welcome all contributors. This news clip and any CloudQuant comment is for information and illustrative purposes only. It is not, and should not be regarded as “investment advice” or as a “recommendation” regarding a course of action. This information is provided with the understanding that CloudQuant is not acting in a fiduciary or advisory capacity under any contract with you, or any applicable law or regulation. You are responsible to make your own independent decision with respect to any course of action based on the content of this post.