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. 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, 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  

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
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.  
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AI & Machine Learning News. 27, August 2018

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Lines Blurred Between Traders and Programmers

This week’s Wall Street Journal weekend edition came with a great piece entitled “Trading Places.” The tag line under the title was this, “Wall Street used to have a strict hierarchy: Traders made money and won glory while programmers wrote code and stayed out of sight. Now, the line between the jocks and the nerds is disappearing.” The article focused upon details from Adam Korn, a 16-year veteran at Goldman. He stated that success today depends less on trusting one’s gut, rather much of a trader’s job is embedded in the computer code or algorithms, which do much of the work now. 2018-08-23 11:30:32-04:00 Read the full story.
CloudQuant Thoughts… This article and the article on which it is based (Trading Places from the WSJ) references new job titles for 200 or so Goldman Sachs employees who they originally called “Straders” and now title “Traders who Code”. At CloudQuant we beleive there are three groups: Traders who Code, Coders who Trade and Data Scientists who Trade. Right now you may be only one skill away from joining these elite groups. Register with CloudQuant, learn how to Trade, Code or Analyse. Ask any questions you have (we are here to help) and you could become a member of this very exclusive club!

Don’t be so scared of AI and new Tech

I read a lot of debates happening on the web and at coffee breaks and even at the company senior stakeholder meetings about applied AI is around the corner and the revolution will sweep away the way we work and major workforce redundancies are about to happen. The more you get involved in those discussions the magnitude grows and this leads for many of us to get anxious about theirs’ and their kids’ future. 2018-08-26 00:35:27 Read the full story. CloudQuant Thoughts… The Luddites thought all their work would be lost to the machines so they threw their clogs into them hence “clogging the machine”. This article brings the disruption closer to home with the upheaval around the birth of tech and its effect on the clerical sector (it destroyed it!), yet unemployment rates are, if anything, lower than before the birth of the tech age.  

Building a blood cell classification model using Keras, tfjs and Google Co-Lab

AI is really a major game changer. Applications of AI are huge and it’s scope in the field of healthcare is vast. Advanced AI tools can assist doctors and lab technicians to diagnose diseases with better accuracy, for example a doctor in Nigeria can use this tool to identify a disease from a sample of blood which he is not at all aware of, this helps him to better understand the disease and thus cures can be developed in a faster way, this is one such advantage of democratizing AI, because AI models and tools are accessible world wide , a doctor in Nigeria can use the same tools and technologies that are being used by research scholars in MIT or any other great universities in the world. Google co-lab provides a cloud based python notebook with a virtual instance tied to a GPU runtime, the GPU runtime of google colab is powered by NVIDIA k-80, a powerful GPU and expensive too. But co-lab allows us to use the GPU for free without having to pay for it. Maximum time of an instance is 12 hours, after 12 hours the instance will be destroyed and new one will be created, so we can perform only those computations that doesn’t last longer than 12 hours. 2018-08-26 04:02:13.840000+00:00 Read the full story. CloudQuant Thoughts… The article is interesting but the Google co-lab is fascinating. It would be cool if someone could create something like that for trading.. wait what? Someone over my shoulder is shouting “CloudQuant AI“. Watch this space!  

Artificial intelligence system detects often-missed cancer tumors

Medical scientists and engineers have come together to develop artificial intelligence system designed to detect often-missed cancer tumors, thereby helping to boots patient survival rates. Researchers based at University of Central Florida developed the system by teaching a computer platform the optimal way to detect small specks of lung cancer in computerized tomography (CT) scans. These are of the type, through size and appearance, that radiologists sometimes have difficultly in identifying. In trials, the healthcare artificial intelligence system was found to be 95 percent accurate in total. Moreover, this was ahead of the typical scores achieved by human medics, which typically fall within the range of 65 percent when accuracy. 2018-08-26 00:00:00 Read the full story. CloudQuant Thoughts… Scanning slide after slide of cell biopsies is tedious and obviously prone to error. Even an ineffective AI can cull the job load down to just those that appear “different” if not “abnormal”. In time AI will be able to massively outstrip human performance in this role to the benefit of us all.  

Forecasting GDP in the eurozone using Eurostat data & ARIMA modelling

This analysis uses public Eurostat datasets, to forecast future total quarterly GDP of all eurozone countries. Eurostat is the statistical office of the European Union situated in Luxembourg. Its mission is to provide high quality statistics for Europe. Eurostat offers a whole range of important and interesting data … 2018-08-26 12:05:18.706000+00:00 Read the full story. CloudQuant Thoughts…  A very interesting article, using the EU’s own data to predict the GDP of individual countries. You could adapt this R to Python, put it into CloudQuant and assign country ETFs to trade these shifts in GDP! What are you waiting for? Head over to CloudQuant now and write that model!  

Is democracy safe in the age of big data?

“I saw the beginnings of creating a private, parallel intelligence-gathering operation that reported solely to the president and his political advisors without any kinds of oversight or control.” So says Christopher Wylie, a self-described queer, Canadian vegan with pink hair and facial piercings. He built up the data team at Cambridge Analytica and then resigned when he saw the destructive ends to which his talents were being put. He later blew the whistle when he realised the extent of the monster he helped create. Cambridge Analytica’s psychographic modeling techniques are able to infer the hot-button issues for individuals based on their personality traits. The system was used in both the American presidential election in 2016 and the Brexit vote that same year. 2018-08-25 00:00:00 Read the full story. CloudQuant Thoughts… The real problem here is that the cat is out of the bag! Cambridge Analytica were not the only company doing this. The cat, in this case, is your personality. Most people do not change their fundamental beliefs from year to year, or even decade to decade. So if Cambridge and their ilk have already identified “who you are”, then they know you better than Google!  

A “Data Science for Good“ Machine Learning Project Walk-Through in Python

Solving a complete machine learning problem for societal benefit. Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media. In this article and the sequel, we’ll walk through a complete machine learning project on a “Data Science for Good” problem: predicting household poverty in Costa Rica. Not only do we get to improve our data science skills in the most effective manner — through practice on real-world data — but we also get the reward of working on a problem with social benefits. 2018-08-23 11:30:32-04:00 Read the full story. CloudQuant Thoughts… This article contains my Quote of the week: “The best approach is to spend some time creating a few features by hand using domain knowledge, and then hand off the process to automated feature engineering to generate hundreds or thousands more.” (Automated Feature Engineering)  

Kaggle Competition : Analyzing The Cure discography

Hi! In this kernel we are going to use the spotifyr package, which allows us to enter an artist’s name and retrieve their entire discography from Spotify’s Web API, along with audio features and track/album popularity metrics. Since The Cure are one of my favourite groups, we are going to analyze some metrics and audio features from their songs! The Cure are an English rock band formed in Crawley, West Sussex, in 1976. By the way, Friday I’m In Love is my favourite song. 2018-08-23 11:30:32-04:00 Read the full story. CloudQuant Thoughts… We started with Robin Thicke, let’s end with The Cure. Who knew there was a Spotify Package – spotifyr is a wrapper for pulling track audio features and other information from Spotify’s Web API in bulk. Available fields… acousticness, analysis_url, danceability, duration_ms, energy, id, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, track_href, type, uri, valence.  

Below the Fold

New Kaggle Data Sources : Drought and the War in Syria

  1.  💦 Did Drought Cause the War in Syria? (link)
  2. 📈 Time Series for Beginners with ARIMA (link)
  3. 🤔 Understand ARIMA and Tune P, D, Q (link)
  4. 💵 A Hitchhiker’s Guide to Lending Club Loan Data (link)
  5. 🦁 Yellowbrick — Regression Visualizer Examples (link)
  6. 💉 (Bio) statistics in R: Part #3 (link)
  7. 🤘 EDA – The Cure Discography (link)
  8. 🧠 Dataset: Example Brain Mapping Data (link)
  9. 🤤 Dataset: Face Dataset with Age, Emotion, Ethnicity (link)
  10. ⚽ Dataset: FIFA World Cup 2018 Tweets (link)
2018-08-23 00:00:00 Read the full story.  

AI took center stage at VentureBeat’s inaugural Transform event

If one theme defined VentureBeat’s inaugural Transform conference on artificial intelligence (AI), it’s metamorphosis. Luminaries from Samsung, Google, Gogo, Uber, Intel, Pinterest, and others spoke about AI‘s increasing ability to handle tasks no human could perform at scale, like creating onboarding guides for tens of thousands of ridesharing drivers and predicting hundreds of millions of users’ taste in fashion. “It’s about enabling companies to [innovate] faster,” said Faizan Buzdar, senior director and platform manager at cloud storage provider Box. “Think about data entry. When you replace it with machine learning, the validation process looks [the same], but you [as a business] saved a lot of money.” An air of optimism pervaded panel discussions, product showcases, and fireside chats about AI in apparel, travel, food delivery, retail, and countless other markets. The consensus? Predictive systems not only have the potential to boost bottom lines and optimize workflows, they are set to improve user experiences. 2018-08-24 00:00:00 Read the full story.  

12 Dimensionality Reduction Techniques in Python

Have you ever worked on a dataset with more than a thousand features? How about over 50,000 features? I have, and let me tell you it’s a very challenging task, especially if you don’t know where to start! Having a high number of variables is both a boon and a curse. It’s great that we have loads of data for analysis, but it is challenging due to size. It’s not feasible to analyze each and every variable at a microscopic level. It might take us days or months to perform any meaningful analysis and we’ll lose a ton of time and money for our business! Not to mention the amount of computational power this will take. We need a better way to deal with high dimensional data so that we can quickly extract patterns and insights from it. So how do we approach such a dataset? 2018-08-27 02:02:43+05:30 Read the full story.  

KAGGLE : Getting Started with Kaggle Competitions

In the field of data science, there are almost too many resources available: from Datacamp to Udacity to KDnuggets, there are thousands of places online to learn about data science. However, if you are someone who likes to jump in and learn by doing, Kaggle might be the single best location for expanding your skills through hands-on data science projects. While it originally was known as a place for machine learning competitions, Kaggle — which bills itself as “Your Home for Data Science” — now offers an array of data science resources. Although this series of articles will focus on a competition, it’s worth pointing out the main aspects of Kaggle:
  • Datasets: Tens of thousands of datasets of all different types and sizes that you can download and use for free. This is a great place to go if you are looking for interesting data to explore or to test your modeling skills.
  • Machine Learning Competitions: once the heart of Kaggle, these tests of modeling skill are a great way to learn cutting edge machine learning techniques and hone your abilities on interesting problems using real data.
  • Learn: A series of data science learning tracks covering SQL to Deep Learning taught in Jupyter Notebooks.
  • Discussion: A place to ask questions and get advice from the thousands of data scientists in the Kaggle community.
  • Kernels: Online programming environments running on Kaggle’s servers where you can write Python/R scripts, or Jupyter Notebooks. These kernels are entirely free to run (you can even add a GPU) and are a great resource because you don’t have to worry about setting up a data science environment on your own computer. The kernels can be used to analyze any dataset, compete in machine learning competitions, or complete the learning tracks. You can copy and build on existing kernels from other users and share your kernels with the community for feedback.
2018-08-22 00:00:00 Read the full story.  

The Formula Of Using Artificial Intelligence In F1 Races

Formula One sport is known to use the best of technologies and has never hesitated to spend on the safety of their drivers. It is not just a car race, but also a race of the technology and is popularly crowned as the Pinnacle of Motorsport. Now the F1 teams are gearing up to introduce artificial intelligence in the races. They are set to use cloud-based real-time analysis and machine learning techniques to enhance race metrics. 2018-08-27 10:39:03+00:00 Read the full story.  

Researcher Steven Riddiough on Volume and Alternative Data in FX

It’s an industry at the dawn of its life and it’s undoubtedly going to evolve considerably over the next few years. I think we’re still trying to understand the best way to implement AI and machine learning in practice. It’s natural that many people at this stage are being employed to get the data aspect of the problem right. Focusing on making sure that datasets are cleaned and in a usable format for analysis is crucial and at the heart of the industry. But the follow-on stage of understanding whether the data is actually valuable is equally important and likely requires a different set of skills to those possessed by the people cleaning and organizing the data. It’s here where individuals with skills at the intersection of data science and economic science become really important. 2018-08-24 06:36:30+00:00 Read the full story.  

How to select the Right Evaluation Metric for Machine Learning Models: Part 1 Regression Metrics

Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric. Usually, the answers to the following question help us choose the appropriate metric: Type of task (Regression/Classification)? Business goal? What is the distribution of the target variable? Well, in this post, I will be discussing the usefulness of each error metric depending on the objective and the problem we are trying to solve. Part1 focuses only to the regression evaluation metrics. 2018-08-26 19:29:04.560000+00:00 Read the full story.  

A Hands on Guide to Automated Feature Engineering using Featuretools in Python

Anyone who has participated in machine learning hackathons and competitions can attest to how crucial feature engineering can be. It is often the difference between getting into the top 10 of the leaderboard and finishing outside the top 50! I have been a huge advocate of feature engineering ever since I realized it’s immense potential. But it can be a slow and arduous process when done manually. I have to spend time brainstorming over what features to come up, and analyze their usability them from different angles. Now, this entire FE process can be automated and I’m going to show you how in this article. We will be using the Python feature engineering library called Featuretools to do this. But before we get into that, we will first look at the basic building blocks of FE, understand them with intuitive examples, and then finally dive into the awesome world of automated feature engineering using the BigMart Sales dataset. 2018-08-22 16:44:53+05:30 Read the full story.  

Intel Eyes Deep Learning with Vertex.AI Acquisition for its Movidius Unit

Intel is going strong over acquisitions, with a $1 billion spending spree on AI tech companies, including Mighty AI, DataRobot, Lumiata, AEye and others; Intel is building its AI capabilities. Additionally, Intel has also acquired Israel-based autotech player Mobileye for $15.3 billion and Movidius which is into specialised low-power processor chips development for computer vision. Intel acquired Movidius in September 2016 for an undisclosed sum, rumoured to around $300 million. Vertex.AI the newest acquired tech platform will join the Movidius group, and assist to strong Intel’s AI capabilities to build powerful processors and deploying them to build AI into apps. 2018-08-26 13:34:32+05:30 Read the full story.  

Academia to data science: a work in progress – Towards Data Science

Leaving academia for data science in a way seems like a natural next (albeit alternative) step. I spent most of my time since starting college being interested in the external and biological factors that make people and other animals do the things they do. More recently I began to become interested in the idea of measuring human behavior on a larger scale and actually using that insight to influence decisions. While this seems like a tough ask in the world of academia it is essentially the job description of many data science positions. Since making the decision to leave academia, there are paths and pieces of advice that that I have so far found useful and others that I could have done without. There are things that I think I did well and things I am not particularly proud of. There have been resources that took me a long time to find that I wish I had found earlier. This post is a collection of ramblings about some of it with the purpose of passing that on to anyone that is interested. 2018-08-26 03:41:52.388000+00:00 Read the full story.  

These engineers see the patterns driving social media trends

Keeping track of the latest news on social media is daunting enough, so imagine if your job were to dissect and analyze every single post, tweet and Instagram story on the internet. Enter the data science and analytics team at Networked Insights. The team develops technology to make sense of billions of daily posts, categorizing the text using more than 30,000 classifications to help companies better understand their customers. Four members of the company’s data team gave us the lowdown on how they extract insights from social media — and the machine learning tools they use to get the job done. 2018-08-23 00:00:00 Read the full story.  

Machine learning environment setup within 10min

One of the main problems with getting started learning AI and ML is installing software because we need to choose the algorithm, framework and software library for our application. Installing new software & libraries takes time. We may also face some issues during the setup process. To overcome these problems we use a docker environment. 2018-08-27 10:51:23.700000+00:00 Read the full story.  

How Intel’s recent move will affect Deep Learning – Towards Data Science

Who hasn’t heard of Intel, the tech giant setting the pace with its processors? While it used to lead the computing devices industry, its reputation was slowly being eclipsed lately due to competitors sprouting up, with processors for mobile and other next-generation devices. Fortunately, this tech leader does not plan on getting submerged anytime soon. To quote Ingrid Lunden in her TechCrunch article, “The company has set its sights on being at the centre of the next wave of computing, and that is the wider context for its focus on R&D and other investments in AI.” 2018-08-20 15:32:08.038000+00:00 Read the full story.  

Consider Predictive Analytics a Necessity, Not an Alternative

As a customer, you receive an email or an SMS notification stating that the groceries are about to expire, and asking if you would want to re-order them. How does the supermarket know? Well, they are not mind readers ; instead, it is predictive analytics being leveraged across huge datasets. The retail industry is known for being the front runners for utilizing and identifying data. They initiated the use of prescriptive analytics and now have entered the arena of predictive analytics. Facebook leveraged predictive analytics techniques to become one of the largest data collecting companies identifying trends, and them to their users. They have more than 2 billion active users to date. You don’t need to be as massive as Facebook, but you definitely need to use predictive analytics model to experience the positives of considering it as a necessity, and not an alternative. 2018-08-24 12:02:52+00:00 Read the full story.  

AI creating big winners in finance, but risks emerge

Artificial intelligence is changing the finance industry, with some early big movers already monetising their investments in back-office AI applications. But as this trend widens, new systemic and security risks may be introduced in the financial system, warns a new report from the World Economic Forum and Deloitte. The report, based on more than 200 interviews with industry players as well as a host of workshops, concludes that AI is “fundamentally changing the physics of financial services”. 2018-08-24 00:01:00 Read the full story.  

The Importance of AI for a Web Developer

When it comes to artificial intelligence (AI), it is no longer limited to sci-fi but is expected to grow into a market of $153 billion in the years to come. This has spurred the need for artificial intelligence courses, thus readying students and professionals to take part in the next wave of change for web development. In website development, an efficient user interface is at the forefront of the service. Artificial intelligence provides a sophisticated customer experience through reply predictions, voice optimisation, and some unique value-adds:
2018-08-23 16:21:51+00:00 Read the full story.  

This Is The Right Time To Standardise Big Data Technologies

Is it time to standardise big data technologies? The once siloed, inaccessible, and mostly underutilised data has now become crucial to enterprises for success. And experts say that there is still room to promote interoperability between the available tools. But how does one define ‘standardisation’? Northeastern University’s College of Computer and Information Science defines “standard” as a formal agreement of meaning of a collection of concepts among groups — in this case, tech companies and enterprises. 2018-08-26 07:46:01+00:00 Read the full story.  

Big data, big responsibility: The ethical question guiding Networked Insights’ CTO

Integrity is doing the right thing even when no one is looking. Networked Insights CTO Brad Burke knows a thing or two about that — it’s his guidepost in the evolving world of marketing analytics. Burke’s team sorts through billions of social media posts every day and turns them into consumer insights for companies. It’s a lofty task that comes with a lot of responsibility. While recent news has shown how easily that kind of information can be misused, Burke has adopted a simple guiding philosophy to ensure he can be proud of the work his team does. 2018-08-23 00:00:00 Read the full story.  

Hype kills value, and other hard lessons from veteran voice app developers

Three industry veterans offer tried-and-true advice for successful voice computing. Perhaps more than any other portion of the tech industry, bots and artificial intelligence have made great strides in the past few years while simultaneously suffering from overhyped and even false claims. After a while, it can become tough to tell truth from fiction. 2018-08-26 00:00:00 Read the full story.  

ICICI Lombard Launches India’s First AI To Automate Health Insurance Claims

ICICI Lombard General Insurance has used artificial intelligence to provide instant health insurance claim approval. Reportedly, the system can scan the documents sent by the hospital and match them with the medical coverage. In the past, this was a cumbersome process involving many people:
  • Doctors looked at the cases
  • Executives entered the data and balance
  • Checking the balance sum insured
  • Going through the room rent limits for the insured
This entire process would take over 60 minutes. Now, because of this new AI-powered system aided by OCR, the process can be carried out in just one minute. 2018-08-22 11:54:05+00:00 Read the full story.  

Waymo sets up subsidiary in Shanghai as Google plans China push

Alphabet’s self-driving unit Waymo has set up a subsidiary in Shanghai, according to a business registration filing, the latest sign that the U.S. internet giant is attempting to make new inroads into China. Waymo established a wholly-owned company called Huimo Business Consulting on May 22 in Shanghai’s free trade zone with registered capital of 3.5 million yuan ($509,165), according to China’s National Enterprise Information Publicity System. 2018-08-23 00:00:00 Read the full story.  

Artificial Intelligence Is Now a Pentagon Priority. Will Silicon Valley Help?

In a May memo to President Trump, Defense Secretary Jim Mattis implored him to create a national strategy for artificial intelligence. Mr. Mattis argued that the United States was not keeping pace with the ambitious plans of China and other countries. With a final flourish, he quoted a recent magazine article by Henry A. Kissinger, the former secretary of state, and called for a presidential commission capable of “inspiring a whole of country effort that will ensure the U.S. is a leader not just in matters of defense but in the broader ‘transformation of the human condition.’” Mr. Mattis included a copy of Mr. Kissinger’s article with his four-paragraph note. 2018-08-26 00:00:00 Read the full story.  

IBM researchers propose ‘factsheets’ for AI transparency

We’re at a pivotal moment in the path to mass adoption of artificial intelligence (AI). Google subsidiary DeepMind is leveraging AI to determine how to refer optometry patients. Haven Life is using AI to extend life insurance policies to people who wouldn’t traditionally be eligible, such as people with chronic illnesses and non-U.S. citizens. And Google self-driving car spinoff Waymo is tapping it to provide mobility to elderly and disabled people. But despite the good AI is clearly capable of doing, doubts abound over its safety, transparency, and bias. IBM thinks part of the problem is a lack of standard practices. 2018-08-22 00:00:00 Read the full story.  

Weekly Selection — Aug 24, 2018 – Towards Data Science

  • Deep Dive into Math Behind Deep Networks
  • Recent Advances for a Better Understanding of Deep Learning
  • Use Kaggle to start (and guide) your ML/ Data Science journey – Why and How
  • A “Data Science for Good” Machine Learning Project Walk-Through in Python
  • Everything you need to know about AutoML and Neural Architecture Search
  • Generative Adversarial Nets and Variational Autoencoders at ICML 2018
  • Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management
  • Measuring Model Goodness
2018-08-24 16:12:35.125000+00:00 Read the full story.  
Behind a paywall…

Why the artificial intelligence bubble looks like it has already burst

In 1964, an American computer scientist named John McCarthy set up a research centre at California’s Stanford University to explore an exciting new discipline: artificial intelligence. McCarthy had helped coin the term several years earlier, and interest in the field was growing fast. By then, the first computer programs that could beat humans at chess had been developed, and thanks to plentiful government grants at the height of the Cold War, AI researchers were making rapid progress in other areas such as algebra and language translation. 2018-08-25 00:00:00 Read the full story.  

Google’s plans for a return to China has many up in arms

When Google announced in 2010 that it would no longer censor results on its Chinese service, a prerequisite for operating in the communist state, internet users laid flowers at the search giant’s Beijing headquarters to mourn its exit. People outside China praised Google’s decision, as it revealed attempts to spy on Chinese dissidents in Gmail, an assault on liberties that the company thought went too far. 2018-08-25 00:00:00 Read the full story.  

Computer-created portrait to be sold by Christie’s in sale which marks ‘the arrival of AI art on the world auction stage’

A portrait created by a computer is to be sold at Christie’s in New York, marking the first time a leading auction house has dealt in art made by artificial intelligence. The “painting” of a fictional man, who has been named Edmond Belamy, is the work of a Paris-based trio of 25-year-olds, who are making a name for themselves with pioneering computer-generated art. Gauthier Vernier, one of the three co-founders of the Obvious art collective, laughed when asked if they intended to put human artists out of business. 2018-08-22 00:00:00 Read the full story.  

The challenges of a no-deal Brexit are as nothing compared with those of Artificial Intelligence

Britain, it would seem, is in a state of total unpreparedness for the coming storm. The Government pays lip service to the necessary precautionary work, but in practice does nothing beyond warning of the potential dangers and trying to ensure continuation of as much of the status quo as possible. I’m not talking here of the possibility of a “no-deal” Brexit. I’ll come back to that. Rather, I’m referring to the advent of Artificial Intelligence. 2018-08-24 00:00:00 Read the full story.  
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stock exchange evolution panel

AI & Machine Learning News. 20, August 2018

This startup is raising $750 million to outmaneuver Domino’s and Pizza Hut with pizzas made by robots

Robots could kill off jobs in the future – but at least they come bearing pizza. Founded in 2015, Zume Pizza uses robotics and artificial intelligence to make pizza more quickly. Machines press mounds of dough, squirt and spread sauce, and lift pizzas in and out of the oven, in a fraction of the time it would take human workers to do the same. Now SoftBank is in talks to invest up to $US750 million in Zume,Bloomberg reports. The cash infusion could help ramp up the pizza delivery company’s side hustle, creating technology for other restaurants that want to get into the automated food truck game. An increasing number of pizza eaters are ditching legacy brands like Domino’s and Pizza Hut for newer fast-casual and delivery chains. In 2016, Business Insider toured Zume’s headquarters in Mountain View, California, to see if the pizza is as good as its tech. 2018-08-09 00:00:00 Read the full story. CloudQuant Thoughts… I cannot believe we missed this story last week, possibly the most important tech story of the last 10 years. I am going to go and shoot my AI robotic ML based web-scraper. Wait, it has told me I am wrong, that this story does not contain Machine Learning or Artificial Intelligence data. OK. OK. I need a pizza!  

How to create a virtuous cycle of data with your customers

Over the last decade, technology companies like Amazon, Apple, Google, and Facebook have risen to the top of brand value lists by outgrowing many of the traditional consumer companies like Disney, Toyota, and McDonald’s. There are many factors driving this rapid growth in tech brand value, but a large portion of the growth can be attributed to the virtuous cycle of data for tech companies. Technology companies know their customers, even anonymously, much better than the companies behind traditional consumer products, and they use that customer data to continuously improve their products which, in turn, drives brand affinity and loyalty. 2018-08-19 00:00:00 Read the full story. CloudQuant Thoughts… One man’s virtuous circle is another man’s descending spiral consuming creativity. Amazon farming data from sales of independent products, so that they can create their own lines of AMAZON BASICS, is just destroying the original creators. Eventually, you end up with no creativity. How is that good for anyone? But this article contains the answer to the most asked model development question of all, “How do I come up with an idea for a trading system?”. The shift from the top 5 brands in 2006 to the top 5 in 2017 mentioned near the top of this article immediately got my juices flowing. How do you predict/detect a shift like this? Can we look back and see how/when it happened? Can we code up a model to detect those moves? Will it pick out some other movers and shakers? Do you think you can do it? Perhaps you can utilize ML and AI. Try it out using CloudQuant.  

Yewno Launches Blockchain Index in Partnership with STOXX

Last week, Yewno announced that it had launched the iSTOXX Yewno Developed Markets Blockchain Index, the second index it has released in partnership with STOXX. In January, Yewno launched the STOXX AI Global Artificial Intelligence Index leveraging its AI based algorithm. The creation of the iSTOXX Yewno Developed Markets Blockchain Index leverages Yewno’s artificial intelligence technology and an underlying dynamic knowledge graph which aggregates a large volume of structured and unstructured data in order to identify companies that are exposed to Blockchain technology and research. The selection of companies included in the index spans multiple sectors and industries, and each component is selected based on the highest exposure to Blockchain technology. 2018-08-20 02:30:37+00:00 Read the full story. CloudQuant Thoughts… Another example of finding the potential future leaders in the markets, this time based on their investment in AI and Blockchain technology. But I cannot imagine that AI and ML are needed to pick these companies out. Also, where there are leaders there are also laggards so the opposing theory is an optional play. Can you come up with a basket of Tickers that are in a hot sector or their opposing cooler competitors? Can you code up a model to test trade them? Head over to CloudQuant and give it a go.  

D.E. Shaw Dives Into Machine Learning

The D. E. Shaw group, a global investment and technology development firm and a pioneer in quantitative approaches to trading and investment, today announced the formation of a new, independent machine learning research and development effort. The new Machine Learning Research Group, which will operate in parallel to the firm’s longstanding machine learning efforts, will be overseen by Dr. Pedro Domingos, who will join the firm as a Managing Director. Dr. Domingos will report to Cedomir Crnkovic, Managing Director, who joined the firm in 1997 and for much of his tenure ran the firm’s futures and curren… 2018-08-16 10:33:39-04:00 Read the full story. CloudQuant Thoughts… D.E. Shaw is a very successful global investment management firm. They use quantitative analysis, algorithmic trading and applied mathematical techniques for investment management. With over a decade of Machine Learning experience, they reportedly are using alternative data sets to seek out new alpha signals and to enhance their fundamental research. D.E. Shaw is often compared with Renaissance Technologies and AQR Capital Management. These firms are leading the trading and investment industry in the use of mathematical models and computers to plot out trading techniques. Innovation is the name of the game at D.E. Shaw.  

Patent Application : Drone delivery of coffee based on a cognitive state of an individual…

Coffee or other drink, for example a caffeine containing drink, is delivered to individuals that would like the drink, or who have a predetermined cognitive state, using an unmanned aerial vehicle (UAV)/drone. The drink is connected to the UAV, and the UAV flies to an area including people, and uses sensors to scan the people for an individual who has gestured that they would like the drink, or for whom an electronic analysis of sensor data indicates to be in a predetermined cognitive state. The UAV then flies to the individual to deliver the drink. Patent Application CloudQuant Thoughts… Beaten to it by in 2014 Image result for coffee by drone cartoon  

Tech Moves: NFL tight end joins Amperity; Vision Critical founder launches Rival Technologies; and more

Amperity, the Seattle-based customer data platform, has hired a sports star for its sales team. NFL tight end Cooper Helfet has joined the company to lead its efforts in sports and entertainment, Amperity CEO Kabir Shahani told GeekWire in an email. “Cooper is extraordinarily talented and brings to Amperity the championship mindset that has allowed him to achieve at the highest levels in the NFL,” Kabir Shahani told GeekWire in an email. “That mental game is a huge asset both for his professional success in a post-NFL career, and for our organization broadly.” 2018-08-14 13:00:40-07:00 Read the full story. CQ Thoughts… We wonder what the electronic trading experts at Rival Systems think about the name Rival Technologies?  

Alexa and Cortana may be working together, but the smartphone is still king

After nearly a year of waiting, Amazon and Microsoft this week brought Alexa to Windows 10 PCs and Cortana to Echo speakers. Overnight, the move delivered Cortana to millions of Echo speakers and Alexa to hundreds of millions of personal computers in the United States. The Echo line of smart speakers continues to enjoy the largest market share, and Windows 10 is installed on nearly 700 million computers. The partnership was made in recognition of the fact that we live in a multi-assistant world. “The world is big and so multifaceted. There are going to be multiple successful intelligent agents, each with access to different sets of data and with different specialized skill areas. Together, their strengths will complement each other and provide customers with a richer and even more helpful experience,” Amazon CEO Jeff Bezos said in a statement when the partnership was announced last August. 2018-08-17 00:00:00 Read the full story. CloudQuant Thoughts… This seems like a big win for Amazon and a loser for Microsoft. Who is going to call on Cortana on their Amazon Echo device?  
Below the Fold.  

Bank of England chief economist warns on AI jobs threat

The chief economist of the Bank of England has warned that the UK will need a skills revolution to avoid “large swathes” of people becoming “technologically unemployed” as artificial intelligence makes many jobs obsolete. Andy Haldane said the possible disruption of what is known as the Fourth Industrial Revolution could be “on a much greater scale” than anything felt during the First Industrial Revolution of the Victorian era. 2018-08-20 00:00:00 Read the full story.  

WaveSense’s ground-penetrating radars could make self-driving cars safer

Radar. Lidar. Cameras. They’re the components that help give autonomous vehicles from Uber, GM’s Cruise Automation, Google spinoff Waymo, and countless others a sense of their surroundings. But WaveSense CEO Tarik Bolat thinks they have a blind spot. “A massive transformation in transportation and mobility is underway around the world as autonomous systems advance at an unprecedented pace,” Bolat said. “But before broad adoption of self-driving vehicles can occur, navigation safety and reliability must improve significantly, particularly in adverse weather conditions like snow, rain, and fog.” 2018-08-20 00:00:00 Read the full story.  

Recent Advances for a Better Understanding of Deep Learning − Part I

I would like to live in a world whose systems are build on rigorous, reliable, verifiable knowledge, and not on alchemy. […] Simple experiments and simple theorems are the building blocks that help understand complicated larger phenomena. This call for a better understanding of deep learning was the core of Ali Rahimi’s Test-of-Time Award presentation at NIPS in December 2017. By comparing deep learning with alchemy, the goal of Ali was not to dismiss the entire field, but “to open a conversation”. This goal has definitely been achieved and people are still debating whether our current practice of deep learning should be considered as alchemy, engineering or science. Seven months later, the machine learning community gathered again, this time in Stockholm for the International Conference on Machine Learning (ICML). With more than 5,000 participants and 629 papers published, it was one of the most important events regarding fundamental machine learning research. And deep learning theory has become one of the biggest subjects of the conference. 2018-08-19 13:23:32.997000+00:00 Read the full story.  

Lean startup and machine learning – Towards Data Science

The term Lean startup was coined about ten years ago. Since that time it has grown to become one of the most influential methodologies for building startups, especially those that fall in the category of web-based software companies. Lean came of age during the internet revolution. We now sit on the cusp of a different revolution — one ushered in by machine learning algorithms. It is safe to assume that most or all software in the near future will contain some element of machine learning. But how compatible is Lean with machine learning, in principle and in practice? (See here for an interesting perspective) 2018-08-19 22:03:25.287000+00:00 Read the full story.  

Google, stop trying to sell us AutoML – Hacker Noon

Google’s AutoML is a glaring example of hype over product. Although the field of AutoML has existed for many years now, Google co-opted the term to refer specifically to its neural architecture search and surrounding suite of products. Neural architecture search essentially creates a dataset with various unique, highly specialized architectures; this search is incredibly computationally intensive and is used to find a singular best model for that specific data. Once that specific model has been found, it is relatively worthless to all the other data except the exact data it was trained on as it has been, at huge computational cost, tuned for that specific data and that specific data only. 2018-08-20 11:21:01.413000+00:00 Read the full story.  

DataHack Radio Episode #8: How Self-Driving Cars Work with’s Brody Huval

Self-driving cars are expected to rule the streets in the next few years. In fact, countries like the USA, China and Japan have already started using them in real-world situations! One of the leaders in this space is Andrew Ng backed, a self-driving car start-up based in California. So how do these autonomous cars work? How difficult is it making one from scratch? What kind of machine learning techniques are used? In this podcast,’s co-founder Brody Huval sheds light on these questions put forward by Kunal, along with other really intriguing facets of autonomous vehicles. It’s a podcast you better not miss! 2018-08-19 23:11:39+05:30 Read the full story.  

India Witnesses First Ever Artificial Intelligence Art Show

Nature Morte presents a group exhibition featuring works created entirely by artificial intelligence in collaboration with Harshit Agrawal, Memo Akten, Jake Elwes, Mario Klingemann, Anna Ridler, Nao Tokui & Tom White. Gradient Descent is the first ever art exhibition in India to include artwork made entirely by artificial intelligence. Curated by 64/1, an art curation and research collective founded by artist Raghava KK and economist Dr. Karthik Kalyanaraman, Gradient Descent, explores the intersection between artificial intelligence and contemporary art. Bringing together artists who address how contemporary art can create a dynamic human-machine relationship, this groundbreaking exhibition provides us with a vision of what art could be in the post-human age. 2018-08-20 12:03:08+00:00 Read the full story.  

SalesForce Open Sources ML Software That Powers Its Einstein AI

Popular cloud computing giant, SalesForce has announced today that it has open-sourced its machine learning tool TransmogrifAI. This ML tool is the core software behind the company’s in-house AI technology called Einstein. With this, SalesForce has a strong intention of tapping AI solutions to fruition in its customer services and sales business. 2018-08-20 00:00:00 Read the full story.  

Artificial intelligence is now directly controlling cooling at Google data centers

In Android 9 Pie, Alphabet’s DeepMind division is responsible for machine learning features like Adaptive Battery and Brightness. One of the first collaborations between the two companies was an AI system tasked with increasing energy efficiency at Google’s data centers. Two years later, an AI has been granted direct control over cooling these servers. According to Google, this is the “first-of-its-kind cloud-based control system.” Every five minutes thousands of sensors throughout the data center issue and send readings to the cloud. Deep neural networks then work to “predict how different combinations of potential actions will affect future energy consumption.” The AI system then identifies which actions will minimize the energy consumption while satisfying a robust set of safety constraints. Those actions are sent back to the data center, where the actions are verified by the local control system and then implemented. This level of automation was something that human operators asked for to implement more granular actions at greater frequency and with fewer mistakes. 2018-08-17 00:00:00 Read the full story.  

The AI-first startup playbook

Iterative Lean Startup principles are so well understood today that an minimum viable product (MVP) is a prerequisite for institutional venture funding, but few startups and investors have extended these principles to their data and AI strategy. They assume that validating their assumptions about data and AI can be done at a future time with people and skills they will recruit later. But the best AI startups we’ve seen figured out as early as possible whether they were collecting the right data, whether there was a market for the AI models they planned to build, and whether the data was being collected appropriately. So we believe firmly that you must try to validate your data and machine learning strategy before your model reaches the minimal algorithmic performance (MAP) required by early customers. Without that validation — the data equivalent of iterative software beta testing — you may find that the model you spend so much time and money building is less valuable than you hoped. 2018-08-18 00:00:00 Read the full story.  

What on earth is data science? The quest for a useful definition : “Data science is the discipline of making data useful.”

Behold my pithiest attempt: “Data science is the discipline of making data useful.” Feel free to flee now or stick around of a tour of its three subfields : Statistics, Machine learning, Data-Mining/Analytics. The term no one really defined. If you poke around in the early history of the term data science, you see two themes coming together. Allow me to paraphrase for your amusement:
  • Big(ger) data means more tinkering with computers.
  • Statisticians can’t code their way out of a paper bag.
And thus, data science is born. The way I first heard the job defined is “A data scientist is a statistician who can code.” I’ll be full of opinions on that in a moment, but first, why don’t we examine data science itself?… 2018-08-18 19:45:00.019000+00:00 Read the full story.  

AI Creating Big Winners in Finance

Artificial intelligence is changing the finance industry, with some early big movers monetizing their investments in back-office AI applications. But as this trend widens, new systemic and security risks may be introduced in the financial system. These are some of the findings of a new World Economic Forum report, The New Physics of Financial Services – How artificial intelligence is transforming the financial ecosystem, prepared in collaboration with Deloitte. “Big financial institutions are taking a page from the AI book of big tech: They develop AI applications and make them available as a ‘service’ through the cloud,” said Jesse McWaters, AI in Financial Services Project Lead at the World Economic Forum. “It is turning what were historically cost centres into new source of profitability, and creating a virtuous cycle of self-learning that accelerates their lead.” 2018-08-16 10:22:10-04:00 Read the full story.  

TensorFlow 2.0 Is Coming; Here’s What You Should Look Forward To

The eagerly-awaited update for the popular machine learning framework TensorFlow was announced earlier this week by Martin Wicke from Google AI. He announced the news on his Google Group, adding that they are planning to release a preview version of TensorFlow 2.0 in late 2018. Since its open-source release in 2015, TensorFlow has become one of the most widely adopted ML frameworks, catering to a broad spectrum of users and use-cases. In this time, TensorFlow has evolved along with rapid developments in computing hardware, machine learning research, and commercial deployment. 2018-08-19 12:55:43-04:00 Read the full story.  

Factory reset: Tech startups raise big bucks to help companies cut waste

Society has become somewhat accustomed to disposable goods, be it cheap garments, budget phones, or plastic packaging. But with Earth facing untold apocalyptic catastrophes in the decades to come, there has been a growing push to do something — anything — to counter the predicted cataclysmic events that await us. A few months back, Seattle became the first major U.S. city to ban single-use disposable straws, while England could become the first… 2018-08-18 00:00:00 Read the full story.  

CFA Institute: 2019 Curriculum Includes Machine Learning, Cryptos

Exam-sitters in the 2019 CFA Program will see additions covering the latest iterations of financial technology, as well as topics like cryptocurrency and machine learning, CFA Institute announced today. The update, spurred by a combination of focus groups and surveys completed by members of the institute, will include 10 new readings and major revisions, as well as improvements to 18 existing readings. The rationale for the additions and reworkings came down to basic economics: supply and demand. “Our goal is to be a fast-follower,” said Stephan Horan, managing director of credentialing for CFA Institute. “That’s what the industry told us that candidates needed to know.” Candidates will face these new topics along with nearly 9,000 pages of curriculum that takes approximately 1,000 hours, on average, to master. Topics with dialed-down or streamlined coverage are credit default swaps and some aspects of portfolio management, said Horan. 2018-08-15 15:43:19-04:00 Read the full story.  

CEO CHAT: Bill Stephenson, AIR Summit

Bill Stephenson spent 20 years at Franklin Templeton Investments, ultimately becoming Global Head of Trading and one of the most recognizable figures in the buy-side trading community. After leaving the firm in 2017, he’s now leading the AIR Summit, a buy-side only event focused on showcasing the companies with the most innovative new solutions to help drive alpha. Traders Magazine editor John D’Antona recently caught up with Stephenson to talk about his new initiative, buy side technology trends, recent market structure developments, and more. 2018-08-15 09:22:10-04:00 Read the full story.  

9 fascinating things I learned while coding up the rules of a board game

I recently decided I was going to take the rules of the board game Forbidden Island and write them up as code. I guess that sounds like a weird thing to just decide to do, doesn’t it? It’s actually one part of a bigger goal I have at the moment of teaching myself some practical machine learning. As part of this journey, I heard a great idea from YouTuber Jabrils to set yourself a significant challenge that you’re interested in, and to work towards surmounting that challenge. For Jabrils, his challenge was getting an AI to control a Forrest Gump character to run around a course in a game. For my challenge, I’ve decided I’d like to build an AI that can play Forbidden Island. (And win!) Obviously, if you’re going to have an AI play a game, you first need a digital version of the game for the AI to play. I knew writing up the rules of the game would be a bit of a distraction, but I decided to give it a crack anyway. I’d been making some good progress with the machine learning course, but it was hard work and I thought this would be a fun detour. It ended up taking quite a bit longer than I anticipated. While I was originally only planning to encode the game rules into code, I was eventually seduced by the idea of writing a program that could play the game. Turning my own automatic thoughts while playing the game into code that could automatically execute was quite the challenge. Here’s the top nine things I learned on this journey:
  1. Kotlin is an awesome programming language
  2. The rules of simple games are actually really complex
  3. The written rules of a game may be ambiguous
  4. Humans don’t consider all the possibilities
  5. Our brains are fantastic at matching patterns and ruling out large swathes of options
  6. Getting statistical significance can be really challenging
  7. Simple rules don’t win games
  8. Games are fascinating
  9. There is a computer made of meat in your head and it’s ridiculously powerful
2018-08-17 17:04:01.221000+00:00 Read the full story.  

How Figure Eight Speeds Up Machine Learning With Video Object Tracking

It generally takes a lot of reps (repetitions) for a human to become really good at something. Playing the violin, swinging at (and hitting) a hard-thrown baseball and dropping back to complete a long pass in football are examples of this. Similarly, it takes a machine a lot of reps to be able to remember a data set and then bring it to the fore when someone needs the information. A lot of people don’t realize this. San Francisco-based startup Figure Eight knows all about this practice and specializes in teaching artificial intelligence engines how to perform optimally, and it does this through video reps. Figure Eight, which describes its product as a “human-in-the-loop machine learning platform,” on Aug. 14 launched its ML-assisted Video Object Tracking solution to accelerate the creation of training data for customers in key industries such as automotive and transportation, consumer goods and retail, media and entertainment, and security and surveillance. 2018-08-14 00:00:00 Read the full story.  

Intel Unveils Data Center Processor Plans Through 2020 : Cooper Lake….

Chip giant Intel’s (NASDAQ:INTC) second-largest business by both revenue and operating profit, and arguably its most important business from a growth perspective, is its data center group, or DCG for short. Last quarter, DCG saw revenue and operating profit grow 27% and 64.8%, respectively. Their new processor, Cooper Lake, will also support the bfloat16 numeric format, which the executive says “is principally used for [machine learning] training kinds of workloads.” Earlier in Shenoy’s presentation, the executive disclosed that the company sold over $1 billion worth of its Xeon processors to customers looking to run artificial intelligence workloads. In a separate presentation, Intel executive Naveen Rao said that the market opportunity for data center processors sold to run artificial intelligence workloads would grow from $2.5 billion in 2017 to between $8 billion and $10 billion by 2022. Although Intel has made it clear that it’s designing chips specifically to run artificial intelligence workloads , the company has said that it’s “reinventing Xeon for [artificial intelligence]” via a combination of hardware and software advancements. 2018-08-20 00:45:11-04:00 Read the full story.  

Qualcomm Snapdragon 670 SoC Adds AI to Smartphones

Today’s topics include the Qualcomm Snapdragon 670 bringing AI to mainstream mobile phones, and Dell EMC targeting AI workloads with integrated systems. 2018-08-15 00:00:00 Read the full story.  

Breaking News founders launch Factal, delivering real-time news and incident alerts to companies

When NBC News shut down its popular Breaking News website and app in late 2016, the team heard not just from general news consumers but also from disappointed users at big companies and organizations around the globe. They had come to rely on the service for 24/7 real-time news and incident updates to protect employees and assets from threats around the globe. Users can set up custom notifications depending on factors including the location of their facilities and assets. Factal then uses machine learning technology and editors to verify and geo-locate information about everything from wildfires to shootings. 2018-08-14 13:00:59-07:00 Read the full  

Salesforce plans to open-source the technology behind its Einstein machine-learning services

Salesforce is open-sourcing the method it has developed for using machine-learning techniques at scale — without mixing valuable customer data — in hopes other companies struggling with data science problems can benefit from its work. The company plans to announce Thursday that TransmogrifAI, which is a key part of the Einstein machine-learning services that it believes are the future of its flagship Sales Cloud and related services, will be ava… 2018-08-16 13:00:09-07:00 Read the full story.  

Why Python Continues to Be the Swiss Army Knife of Programming

Last winter, one of the world’s largest coding bootcamps, Coding Dojo, released an objective analysis of the most in-demand programming languages of 2018. Coding Dojo came to its findings by analyzing the hundreds of thousands of job postings that contained the name of a programming language on job search engine It found—to no one’s surprise—that Java is the most in-demand, followed by Python and JavaScript. 2018-08-16 00:00:00 Read the full story.  

Understanding Probability Theory with Dungeons and Dragons

Probability theory is a rich branch of mathematics that also intersects with philosophy, theology and logic. Probability theory has its roots in the 1600s, when mathematicians Pascal and Fermat began to analyse the mathematics of games of chance. Pascal and Fermat contributed to not only mathematics, but philosophy and theology. Theologians and philosophy majors may recall Pascal’s wager, which in simple terms frames that humans effectively bet with their lives as to whether god exists. We can see therefore, the wide-ranging impact of probability theory. We can consider that probability theory is a vast area to cover, but one that I believe can be both intuitive and easy to grasp, provided helpful examples are given. Probability theory is also an integral part of data science and understanding the basics in this area will provide a strong foundation for more advanced topics, such as regression and Bayesian analysis. 2018-08-19 21:43:29.884000+00:00 Read the full story.  

Numpy With Python For Data Science – Hacker Noon

In Part 1 of the Data science With Python series, we looked at the basic in-built functions for numerical computing in Python. In this part, we will be taking a look at the Numpy library. NumPy is the fundamental package for scientific computing with Python. It contains among other things:
  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities
2018-08-20 10:51:01.656000+00:00 Read the full story.  

Python for data science : Part 4 – Towards Data Science

In Part 3 of the Python for data science series, we looked at the pandas library and it’s most commonly used functions — reading and writing files, indexing, merging, aggregating, filtering etc. In this part, we will continue to deep dive further into the Pandas library and look at how it can be used along with other Python functions for querying dataframes. 2018-08-19 21:42:45.990000+00:00 Read the full story.  

Deep Dive into Math Behind Deep Networks – Towards Data Science

Nowadays, having at our disposal many high-level, specialized libraries and frameworks such as Keras, TensorFlow or PyTorch, we do not need to constantly worry about the size of our weights matrices or remembers formula for the derivative of activation function we decided to use. Often all we need to create a neural network, even one with a very complicated structure, is a few imports and a few lines of code. This saves us hours of searching for bugs and streamlines our work. However, the knowledge of what is happening inside the neural network helps a lot with tasks like architecture selection, hyperparameters tuning or optimisation. 2018-08-17 21:44:15.278000+00:00 Read the full story.  

Report: Top 10 Well-Funded AI Startups to Watch in 2018

Artificial intelligence (AI) continues to redefine how technology is applied to modern use by the mankind. From recruitment resume screening to the complex Quantum Computing applications, AI has come a long way in causing an optimistic change. Sensing the huge disruption AI is capable to make, funding in the AI sector has climbed steadily and impressively over the past decade. In 2017 alone AI industry saw investors pumping nearly $5 billion in US-based AI startups. As trends point out, 2018 will be a promising year for venture investing in AI startups. AI funding is not restricted to Series A, B or C rounds but rather has progressed to supergiant rounds. 2018-08-17 09:20:09+05:30 Read the full story.  

CognitiveScale chosen by Dell to Assist Customer Experiences through AI

CognitiveScale Inc., a provider augmented intelligence and AI software, is being selected by Dell to help transform customer experience and marketing productivity through AI. Dell chose CognitiveScale and its Cortex 5 software to power and transform the core of their customer journeys. By leveraging the power of AI to understand declared, observed, and inferred customer behavior across various channels and devices, CognitiveScale will enable Dell to generate additional insights from customer interactions and create highly personalized experiences. This AI powered system will also deliver prescriptive insights for sales, marketing, and customer service teams to better manage customer engagement. 2018-08-14 00:00:00 Read the full story.  

Three Hot Trends Warren Buffett Is Missing Out On — But You Don’t Have To

You have an investing advantage over Warren Buffett. No, seriously, you really do. Buffett has admitted that he regrets not buying Google parent Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) and years ago. And he’s only been a big buyer of Apple stock in the past few years. Apple gained more than 2,000% in the 10 years before Buffett’s Berkshire Hathaway jumped aboard. The reality is that Buffett doesn’t invest in what he doesn’t understand. That means he can miss out on some of the hottest trends — at least for a while. And therein lies your advantage: gaining an understanding of trends relatively early on and buying the best stocks to profit from those trends. Here are three hot trends that Buffett is missing out on, along with investing ideas for each one.
  1. Artificial intelligence
  2. Cannabis
  3. Gene editing
2018-08-19 00:00:00 Read the full story.  

It Turns Out Amazon’s Alexa Isn’t a Great Way to Buy Stuff

Smart-speaker sales are expected to hit 100 million units this year, and reach 225 million units by the end of 2020. Amazon’s Alexa-enabled devices own two-thirds of the smart-speaker market, while Google has about a 30% share. Yet despite these devices’ proliferation, people aren’t really using them to buy stuff, nor does it seem they really want to. The tech news website The Information reports that out of some 50 million Alexa users, only 1 million have tried to buy something with the device. And of that total, just 100,000 completed a transaction. Amazon disputed those figures, saying that “millions of customers use Alexa to shop.” But it seems that people are more interested in using their devices for listening to music and other entertainment, controlling other connected devices like the lights or the TV, and checking the weather. 2018-08-20 00:00:00 Read the full story.  

Video : AI Use Cases in Capital Markets

Josh Sutton, CEO of startup AI marketplace Agorai, discusses use cases on how artificial intelligence is transforming capital markets, in both the front and back office. 2018-08-16 14:56:53-04:00 Read the full story.  

Is NVIDIA Corporation a Buy?

Investors who follow the technology industry closely have likely heard a lot about the graphics processor maker NVIDIA Corporation (NASDAQ:NVDA) over the past few years. That may be because the company’s shares are up more than 950% over the past three years, and because it’s benefiting from the growth of emerging technologies including artificial intelligence (AI) and autonomous vehicles. 2018-08-20 00:00:00 Read the full story.  

Neurala Announces Brain Builder “AI for Good” Competition for Developers

Following the announcement of its Brain Builder beta program, Neurala, the award-winning artificial intelligence company, today announced a call for submissions for its “AI for Good” competition. Developers who participate in the “AI for Good” contest will have the chance to win a $1,000 first-place cash prize. The second-place finalist will win an EVGA GeForce GTX 1080 Ti SC2, and the third-place winner will receive a DJI phantom 3. Entries will be judged by a panel of industry experts. Neurala’s own applications and commitment to this theme include its work with Motorola Solutions’ first responder body cams to help find missing children, as well as its partnership with the Lindbergh Foundation to help combat animal poaching in Africa. 2018-08-16 16:00:44-04:00 Read the full story.  

How humans can communicate with aliens

Stephen Wolfram is an expert in computer languages. And he has an interesting theory on how we may discover that what we learn from computer and artificial intelligence, could ultimately help us communicate with intelligent alien life. 2018-08-16 00:00:00 Read the full story.  

Tech firms say A.I. can transform health care as we know it. Doctors think they should slow down

As an industry reliant on patient records and beset by outdated technology, health care is widely thought to be a prime target for an artificial intelligence revolution. Many believe the technology will provide a host of benefits to clinical practitioners, speeding up the overall experience and diagnosing illnesses early on to identify potential treatment. 2018-08-17 00:00:00 Read the full story.  

Artificial Intelligence in Medicine Gains Massive Traction with Growing Investment in AI in the Space

Many industries across the globe have been disrupted by the influx of artificial intelligence (AI). And the healthcare industry is no different. The technology has far-reaching implications across the different areas of healthcare, including the discovery and development of better medicines, effective prescription of medicines, accurate monitoring of patient adherence to prescription, proper diagnosis of diseases at an early stage, clinical research studies and applications that support decision-based medical tasks, and others. Given the enormous potential of AI in medicine, the adoption of AI technology by many pharmaceutical and biotechnology companies grew considerably over the past few years, which is leading to the growth of the AI in medicine market. Moreover, the lack of skilled healthcare professionals, the increased funding for the R&D activities concerning the use of AI in medicine, the growing importance of precision medicine fuel the growth of the industry. 2018-08-14 00:00:00 Read the full story.  

Apple is beefing up a team to explore making its own health chips

Apple has a team exploring a custom processor that can make better sense of health information coming off sensors from deep inside its devices, job listings show. The effort hints at Apple’s ability to pump out custom chips on as-needed basis, reflecting a greater level of vertical integration than other technology companies. Building custom chips for narrow functions can help Apple add new features and improve efficiency of its hardware while protecting its intellectual property from would-be imitatotrs. A July 10 job posting from Apple’s Health Sensing hardware team says, “We are looking for sensor ASIC architects to help develop ASICs for new sensors and sensing systems for future Apple products. We have openings for analog as well as digital ASIC architects.” 2018-08-14 00:00:00 Read the full story.  

Google reportedly developing speaker with screen to counter Amazon Echo Show

Google is aiming to challenge Amazon’s Echo Show by releasing its own smart speaker equipped with a screen in time for this year’s holiday season, Nikkei Asian Review reported today. Nikkei Asian Review noted that the new product, based on Google’s Smart Display platform, would round out the Google Home lineup of smart speakers equipped with the voice-enabled Google Assistant artificial intelligence agent. 2018-08-17 20:31:30-07:00 Read the full story.  

Google is now using AI to keep its data centers cool and save energy

Google is practicing what it preaches, handing control of one of the most vital components of its data center operation over to its machine-learning algorithms during the past few months. DeepMind, the Google subsidiary that is responsible for much of its advanced artificial intelligence research, announced Friday that Google has saved 30 percent on its energy bills by improving the efficiency of its cooling systems. “This first-of-its-kind cloud-based control system is now safely delivering energy savings in multiple Google data centers,” Google said in a blog post. 2018-08-17 17:15:10-07:00 Read the full story.  

Google Working On New ‘Coach’ AI Fitness Assistant For Wear OS

Artificial intelligence has always been something that Google has been integrating into a lot of its products. Now, it looks like the company is planning to bring it to Wear OS smartwatches, but this time it will be an AI coach that will assist users with their health and fitness goals. Google Coach is being developed by Google under the codename Project Wooden, according to Android Police. The new AI assistant will be able to provide users with health and fitness data proactively. Google Coach will also be ale to deliver suggestions and recommendations for workouts and track the user’s progress. The assistant is also said to be capable of providing alternative workouts if a user was unable to fulfill a scheduled routine. Google Coach can log the user’s activity and it will use that data to provide suggestions in the future. 2018-08-16 04:51:33-04:00 Read the full story.  

There’s a reason Siri, Alexa and AI are imagined as female – sexism

Virtual assistants are increasingly popular and present in our everyday lives: literally with Alexa, Cortana, Holly, and Siri, and fictionally in films Samantha (Her), Joi (Blade Runner 2049) and Marvel’s AIs, FRIDAY (Avengers: Infinity War), and Karen (Spider-Man: Homecoming). These names demonstrate the assumption that virtual assistants, from SatNav to Siri, will be voiced by a woman. This reinforces gender stereotypes, expectations, and assumptions about the future of artificial intelligence. Fictional male voices do exist, of course, but today they are simply far less common. HAL-9000 is the most famous male-voiced Hollywood AI – a malevolent sentient computer released into the public imagination 50 years ago in Stanley Kubrick’s 2001: A Space Odyssey. Male AI used to be more common, specifically in stories where technology becomes evil or beyond our control (like Hal). Female AI on the other hand is, more often than not, envisaged in a submissive servile role. 2018-08-14 17:06:33+01:00 Read the full story.  

Intel acquires Seattle-based deep learning startup Vertex.AI to bolster artificial intelligence efforts

Intel has acquired Vertex.AI, a three-year-old Seattle startup whose tools let developers add deep learning capabilities to their software. Founded in 2015 by Choong Ng, Jeremy Bruestle, and Brian Retford, Vertex and its 7-person team will become part of the Movidius team within Intel’s Artificial Intelligence Products Group. Terms of the deal were not disclosed. “With this acquisition, Intel gained an experienced team and IP to further enable flexible deep learning at the edge,” Intel said in a statement. 2018-08-16 18:32:13-07:00 Read the full story.  

Weekly Selection — Aug 17, 2018 – Towards Data Science

  1. The most important part of a data science project is writing a blog post
  2. Forecasting with Python and Tableau
  3. Better collaborative data science
  4. Don’t make this big machine learning mistake: research vs application
  5. Fine-tuning XGBoost in Python like a boss
  6. A Machine Learning Approach — Building a Hotel Recommendation Engine
  7. Creating custom Fortnite dances with webcam and Deep Learning
  8. Don’t Use Dropout in Convolutional Networks
  9. What App Descriptions Tell Us: Text Data Preprocessing in Python
2018-08-17 12:08:07.922000+00:00 Read the full story.  
Behind Pay Walls/Registration Walls

Amazon’s secretive Cambridge Alexa start-up doubles revenue and headcount

A secretive Cambridge technology start-up acquired by Amazon that helped pioneer the Amazon Echo smart speaker has doubled its headcount and revenues. Evi Technologies, which is owned by Amazon and was responsible for a significant part of the development of its Alexa artificial intelligence technology, doubled its revenues in 2017 to £36m, up from around £18m in 2016. The company also saw its staff numbers increase from 123 to 247 and increased its cash position from around £14m to £20.7m, according to its accounts. 2018-08-19 00:00:00 Read the full story.  

Robots can easily sway children using peer pressure

Children can be easily swayed into changing their opinions by robots, according to new research which raises new questions over the ethics of artificial intelligence. In a series of tests by academics at the University of Plymouth, children aged between seven and nine were more likely to give the same responses as their robot companions, even when it was clear that suggestions made by the robots were wrong. “What our results show is that adults do not conform to what the robots are saying. But when we did the experiment with children, they did,” said Tony Belpaeme, a Professor in Robotics at Plymouth. With children now having far more interaction with digital assistants such as Amazon’s Alexa… 2018-08-16 00:00:00 Read the full story.  

M&S to replace call centre staff with AI that understands human speech

Marks & Spencer is replacing call centre staff with artificial intelligence designed to quickly deal with customer complaints. The company is using software from technology companies Twilio and Google to automate the routing of calls. Previously, people calling M&S would have to speak to a human operator to be transferred to the right department. The new technology will be used in all 640 M&S UK stores by the end of September, as well as its 13 UK call centers. No jobs have been lost in the change, and the business will reassign over 100 employees to in-store roles, M&S said. M&S claims the technology correctly identifies 90pc of queries and can direct a call within seconds. 2018-08-15 00:00:00 Read the full story.  
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AI & Machine Learning News. 13, August 2018

What would you recommend to someone starting out at CloudQuant?

We asked our portfolio managers, product management team and programmers to tell us what they think will help beginners the most here at CloudQuant. We wanted to help everyone in need of a boost to get started on our platform. Everyone in our company uses the CloudQuant website and coding platform in one way or another. We all use our own application, just like the crowd researchers. When we say that our free backtesting tools are “institutional grade” we really mean it. Every algo we run in our trading and investment strategies is proven in the same backtesting engine as the crowd uses. We rely on the scorecards, the reports, and the simulated trades to ensure that our trading is successful. Read the full story  

How A Supercomputer Named Dr. Crusher Perfected Cancer Treatments For 21 Patients

Sequencing tumor genes and then selecting treatments that target certain mutations is routine in the treatment of several tumor types, including breast and lung cancer. But it’s not common practice for treating hematological malignancies like multiple myeloma, even though oncologists who treat those diseases are well aware that each patient’s cancer has unique characteristics that may make it more or less responsive to certain targeted treatments. Oncologist Samir Parekh and his colleagues at the Mount Sinai Icahn School of Medicine in New York wanted to change the treatment paradigm in multiple myeloma. So they developed a system that uses an algorithm to match up multiple myeloma patients with drugs that are already on the market to treat other cancers. They tried the resulting recommendations in a group of 21 patients with treatment-resistant multiple myeloma and got positive results in all but five—a response rate that’s so high Parekh believes the idea could be extended to many other cancers. 2018-08-09 00:00:00 Read the full story.  

Google’s DeepMind AI can accurately detect 50 types of eye disease just by looking at scans

DeepMind published on Monday the results of joint research with Moorfields Eye Hospital, a renowned centre for treating eye conditions in London, in Nature Medicine. The company said its AI was as accurate as expert clinicians when it came to detecting diseases, such as diabetic eye disease and macular degeneration. It could also recommend the best course of action for patients and suggest which needed urgent care.
  • Google’s artificial intelligence company DeepMind has published “really significant” research showing its algorithm can identify around 50 eye diseases by looking at retinal eye scans.
  • DeepMind said its AI was as good as expert clinicians, and that it could help prevent people from losing their sight.
  • DeepMind has been criticised for its practices around medical data, but cofounder Mustafa Suleyman said all the information in this research project was anonymised.
  • The company plans to hand the technology over for free to NHS hospitals for five years, provided it passes the next phase of research.
  • Google’s artificial intelligence company, DeepMind, has developed an AI which can successfully detect more than 50 types of eye disease just by looking at 3D retinal scans.
Read the full story (Business Insider). Artificial intelligence as good as human doctors at spotting early signs of blindness – Read the  story from the Telegraph. DeepMind’s AI can recommend treatment for more than 50 eye diseases with 94% accuracy – Read the story from VentureBeat. CloudQuant Thoughts… After the recent well-documented failure of Watson in the area of Cancer diagnosis (worse than failure – recommending treatment regimes that could be harmful to the patient) it was good to read these stories of ML successes in medicine including one for their competition – Google’s DeepMind. Again, ML is impressive when used more as a leverage to human ability. Honing in on problem areas and making suggestions seems to be the current ideal step for a lot of ML and AI improvements in workflow.  

Ideas on how AI should Improve Our Daily Lives

In most every example of Artificial Intelligence (AI) in business, there is a chat bot or robot or algorithm that attempts to replace a human. We surely will see replacement via AI, but augmentation is also a value approach. In augmentation, a AI process considers various options and provides the human with improved decision options, based on some deeper consideration or at least with more complexity and optionality involved. There are many things we currently do that are doing regularly that are often conducted with limited consideration of options or a poor consideration of complexity. The penalty we experience is in cost, quality of life, and loss of time. Here are some things, that I think, merit an AI process for continued improvement in everyday life.
  • Daily Dynamic Scheduling
  • Achieving Best Pricing on Regular Purchases
  • Deep (and On-going) Searching
  • Cash Management
  • Best Recipe Tonight
2018-08-08 00:00:00 Read the full story. CloudQuant Thoughts… These suggestions continue the idea that ML and AI are going to be best used, initially, as leverage to human thought and decision-making processes. I tried to find a video of Apple from late 80’s early 90’s demonstrating a Search that they had called “AN AVATAR” who would continue to search for new information on the “World Wide Network” on subjects that you found interesting. Having someone shop for you can save you hours of your precious time for a very small cost. If we throw away 40% of our food every year, maybe eating out is not such a bad idea.  

Android Pie: Google Launches New Artificial Intelligence-Powered OS

Google on Tuesday rolled out Android Pie, its highly-anticipated mobile phone operating system. This OS’ major updates focus is on artificial intelligence, which will allow the system to “learn” from the user and customise the Android experience. With the Android Pie update, the search engine giant has promised that the phone would become more and more tailored and customised with the user’s behaviour and usage. “We’ve built Android 9 to learn from you — and work better for you — the more you use it,” said Google in an official statement. 2018-08-07 07:50:20+00:00 Read the full story. CloudQuant Thoughts… It is a pleasure to see companies like Google using AI to improve the user interface. I am always reminded of Palm OS’s three clicks rule (developed from the 80/20 rule) that all information should, wherever possible, be reachable within three clicks maximum. As we continue to develop our CloudQuant web service, we are always trying to think of how to get the most important data to our users in the smallest number of steps. As an example, today we roll out extra features relating to viewing the best and worst trades taken by your backtests. We now sort all backtest days by the absolute PnL and within each date all trades are, by default, sorted by absolute Gross Profit, so now you can quickly access your best and worst days and trades. We have also added keyboard shortcut keys to allow our users to quickly scan through the charts for their trades. See the CloudQuant forum post for more information.  
Below the Fold…

Intel AI and 2,500 Xeons bring ‘The Meg’ mega-shark to the big screen

The Meg, a sci-fi film about a giant prehistoric 75 foot-long shark, debuted last week from Warner Bros. and Gravity Pictures. And Intel said today that its artificial intelligence hardware — namely, about 2,500 Xeon Scalable processors — helped bring the creature to life on the big screen. Scanline VFX used Ziva VFX software and Intel’s Xeon processors to create the creature, known as the megalodon, with lifelike realism (even though these beas… 2018-08-13 00:00:00 Read the full story.  

Delayed impact of fair machine learning

“Delayed impact of fair machine learning” won a best paper award at ICML this year. It’s not an easy read (at least it wasn’t for me), but fortunately it’s possible to appreciate the main results without following all of the proof details. The central question is how to ensure fair treatment across demographic groups in a population when using a score-based machine learning model to decide who gets an opportunity (e.g. is offered a loan) and who doesn’t. Most recently we looked at the equal opportunity and equalized odds models. 2018-08-13 00:00:00 Read the full story.  

K-Means Clustering, Creating a Simple Trading Rule for Smoother Returns

What is K-means clustering? K-means is an iterative refinement algorithm that attempts to put each data point into a group or cluster. The algorithm starts with initial estimates for the K centroids (centers of the mentioned groups) and continues moving the centroids around the data points until it has minimized the total distance between the data points and their nearest centroid. The user will generally specify K which is the number of centroids (groups). The algorithm can be thought of in two repetitive steps:
  1. Data assignment – Each centroid defines one of the clusters. In this step, each data point is assigned to one of the centroids or clusters. Assignment is typically done based on Euclidean distance.
  2. Centroid Update – Centroids are then recomputed or moved. This is done by taking the mean of all the data points assigned to that centroid’s cluster
2018-08-09 09:04:44+00:00 Read the full story.  

Deploying Machine Learning Models is Hard, But It Doesn’t Have to Be

With free, open source tools like Anaconda Distribution, it has never been easier for individual data scientists to analyze data and build machine learning models on their laptops. So why does deriving actual business value from machine learning remain elusive for many organizations? Because while it’s easy for data scientists to build powerful models on their laptops with tools like conda and TensorFlow, business value comes from deploying machine learning models into production. Only in production can a deployed model actually serve the business. And unfortunately, the path to production remains difficult for many companies. 2018-08-09 11:32:55-05:00 Read the full story.  

Why Is Auto-Keras Gaining Such Popularity?

Auto Keras is the new open-source neural network library built for automated machine learning. This is built upon Keras where one with less knowledge about machine learning can make use of this library to build neural networks. This library makes the job easy with the help of automated search for hyperparameter selection and finding the optimized values. Let us dive into how to get started with it: The automated machine learning packages are gaining popularity because of their easy-to-implement technique. A person from a non-AI background or one who has very less machine learning knowledge can build and train neural network within a few lines of code. Let us see how it can be installed. 2018-08-13 08:53:15+00:00 Read the full story.  

A Machine Learning Approach — Building a Hotel Recommendation Engine

All online travel agencies are scrambling to meet the AI driven personalization standard set by Amazon and Netflix. In addition, the world of online travel has become a highly competitive space where brands try to capture our attention (and wallet) with recommending, comparing, matching and sharing. In this post, we aim to create the optimal hotel recommendations for Expedia’s users that are searching for a hotel to book. We will model this problem as a multi-class classification problem and build SVM and decision tree in ensemble method to predict which “hotel cluster” the user is likely to book, given his (or her) search details. 2018-08-13 12:42:11.456000+00:00 Read the full story.  

Robotic Process Automation (RPA) And Artificial Intelligence Don’t Have Many Things In Common

Researchers across the globe are trying to inculcate technologies such as robotics and AI in their workflows to optimise and automate them. In such processes, Robotic Process Automation (RPA) is one of the most popular terminologies and is often sought-after for handling operational tasks with least manual intervention. In other terms, it is a software that automates low-level tasks. However, the two terminologies AI & RPA are used interchangeably. It is important to realise that both RPA and AI facilitate a common goal of Intelligent Automation. While RPA is often picturized as a software robot mimicking human actions, AI is the simulation of human intelligence by machines. In this article, we list down 5 major differences between the RPA & AI. 2018-08-13 08:18:46+00:00 Read the full story.  

Clustering algorithms for customer segmentation – Towards Data Science

In today’s competitive world, it is crucial to understand customer behavior and categorize customers based on their demography and buying behavior. This is a critical aspect of customer segmentation that allows marketers to better tailor their marketing efforts to various audience subsets in terms of promotional, marketing and product development strategies. This article demonstrates the concept of segmentation of a customer data set from an e-commerce site using k-means clustering in python. The data set contains the annual income of ~300 customers and their annual spend on an e-commerce site. We will use the k-means clustering algorithm to derive the optimum number of clusters and understand the underlying customer segments based on the data provided. 2018-08-13 04:44:14.557000+00:00 Read the full story.  

Ultimate guide to handle Big Datasets for Machine Learning using Dask (in Python)

Have you ever tried working with a large dataset on a 4GB RAM machine? It starts heating up while doing simplest of machine learning tasks? This is a common problem data scientists face when working with restricted computational resources. When I started my data science journey using python, I almost immediately realized that the existing libraries have certain limitations when it comes to handling large datasets. Pandas and Numpy are great libraries but they are not always computationally efficient, especially when there are GBs of data to manipulate. So what can you do to get around this obstacle? This is where Dask weaves its magic! It works with Pandas dataframes and Numpy data structures to help you perform data wrangling and model building using large datasets on not-so-powerful machines. Once you start using Dask, you won’t look back. In this article, we will look at what Dask is, how it works, and how you can use it for working on large datasets. We will also take up a dataset and put Dask to good use. Let’s begin! 2018-08-09 08:56:19+05:30 Read the full story.  

TensorFlow Vs Caffe: Which Machine Learning Framework Should You Opt For?

When it comes to TensorFlow vs Caffe, beginners usually lean towards TensorFlow because of its programmatic approach for creation of networks. TensorFlow has surged ahead in popularity largely because of the large adoption by the academic community. Caffe, on the other hand, has been largely panned for its poor documentation and convoluted code. In this article, we cite the pros and cons of both the frameworks and see how they stack up against each other for the beginners. 2018-08-07 10:14:52+00:00 Read the full story.  

Idea of Audit in AI age

Audit has always been a support function for banks that has served as the last line of defence for keeping the organisation safe from external attacks and provide assurance to business & stakeholders about the safely of the business from any malicious attempt to harm the data or assets of the business. With all companies virtually converting into data goldmines, Audit these days transforms itself into an IT function that ensures the ultimate defence of our prized data assets. Imagine Audit as the plumber / janitor who ensures there are no leaks and the supply pipes are safe from any corrosion or any other issues that may have started to infest the system. AI can help with the heavy lifting but…
  • Are these AI libraries plagues with their own risks?
  • Who assessed these libraries for the bias they bring with the data?
  • What type of data set you run through with these libraries?
  • How to manage inherent bias in the data, as it could be Garbage In – Garbage Out case?
2018-08-09 02:32:09 Read the full story.  

Artificial intelligence platform launches to tackle financial crime

As financial crime becomes increasingly sophisticated, a new AI tool has been developed to take on fraudsters, minimize the risks of human error and ensure regulatory compliance. Technology and consulting services company Mindtree has launched an artificial intelligence and machine learning tool designed to help banks improve their ability to detect financial crimes and enhance reconciliation management. The service is powered by a machine-learning platform developed by predictive analytics specialist Tookitaki. The launch comes as banks and other financial institutions come under pressure from the increasing sophistication of financial crimes worldwide and ever-more complex regulations requiring strict operating and reporting standards. Because manually detecting money laundering, dealing with false alarms and fragmented reconciliation processes can be costly and time-consuming, there is a growing need for financial institutions to automate many of these processes. Additional benefits of automation include the reduction of errors and quicker response times to incidents. 2018-08-07 00:00:00 Read the full story.  

Welcome to Kaggle Data Notes : From Russian Tweets to The World Cup That Nearly Came Home

Enjoy these new, intriguing, and overlooked datasets and kernels.
  1. 🤬 From Hate Speech to Russian Troll Tweets (link)
  2. 🇰 Data Science Trends on Kaggle (link)
  3. 👜Fashion AC-GAN with Keras (link)
  4. 📈 (Bio)statistics in R: Part #2 (link)
  5. 🛰 Segmenting Buildings in Satellite Images (link)
  6. ⚽ World Cup 2018: The One That Nearly Came Home (link)
  7. 🇯🇵 The Best “Izakaya” Restaurant in Kyoto (link)
  8. 👹 Dataset: Russian Troll Tweets (link)
  9. 📈 Dataset: Political Propaganda on Facebook (link)
  10. 🤓 Dataset: Predict Pakist…
2018-08-09 00:00:00 Read the full story.  

Weekly Selection — Aug 10, 2018 – Towards Data Science

  • Essential Math for Data Science – Why and How
  • Why Automated Feature Engineering Will Change the Way You Do Machine Learning
  • Drake – Using Natural Language Processing to understand his lyrics
  • Loading Data from OpenStreetMap with Python and the Overpass API
  • Web Scraping TripAdvisor, Text Mining and Sentiment Analysis for Hotel Reviews
  • Introduction to NLP
  • Getting started with graph analysis in Python with pandas and networkx
  • Data Science A-Z from Zero to Kaggle Kernels Master
2018-08-10 16:03:12.982000+00:00 Read the full story.  

A NLP Guide to Text Classification using Conditional Random Fields

The amount of text data being generated in the world is staggering. Google processes more than 40,000 searches EVERY second! According to a Forbes report, every single minute we send 16 million text messages and post 510,00 comments on Facebook. For a layman, it is difficult to even grasp the sheer magnitude of data out there? News sites and other online media alone generate tons of text content on an hourly basis. Analyzing patterns in that data can become daunting if you don’t have the right tools. Here we will discuss one such approach, using entity recognition, called Conditional Random Fields (CRF). This article explains the concept and python implementation of conditional random fields on a self-annotated dataset. This is a really fun concept and I’m sure you’ll enjoy taking this ride with me! 2018-08-13 08:32:53+05:30 Read the full story.  

College Football – Who’s a Fan?

With the college football season about to begin, it is worth a look to see how the teams stack-up, not based on rankings, but based on fan loyalty. Big Data and Data Visualization help us with this and lead to some interesting insights. College football also offers us a great opportunity to look at the Big Data of fandom and the business of college football and even that of universities. Building on my post that looked at NFL fan allegiance by location, I thought looking at some data visualization of college football allegiance would tell us a bit about who roots for which team and a bit more about how Big Data, Data Science, and data visualization are helping us understand complex problems in life and business. 2018-08-12 00:00:00 Read the full story.  

Four key priorities to keep pace in an evolving market place

There’s no escaping the fact we are seeing considerable changes in the way we work. The proliferation of data, rising fraud, digital disruption and changing regulation continue to put pressure on traditional business models, so it’s essential that plans are put in place to successfully move with the times. Our recent research has identified four key priorities for businesses in this evolving market place, all influenced by technology and consume… 2018-08-09 09:14:24 Read the full story.  

Deep Learning in a “Mobile Phone” Environment

Building deep learning models that can execute on mobile runtimes is a very active area of research in the artificial intelligence(AI) space. After all, mobile devices are a significant source of information and host of computations in the modern technology ecosystems. Among the deep learning techniques that have been trying to adapt to the mobile world, none is more relevant than convolutional neural networks(CNNs) given that they are a foundational block to image analysis methods which can unlock the door to many new scenarios for mobile apps. Google has been among the players leading the charge in the mobile deep learning space with research like Federated Learning or frameworks like TensorFlow Lite. Recently, researchers from the Google Brain team published a paper introducing MNasNet, a new method for designing CNN models that can effectively execute on mobile devices. 2018-08-13 11:53:39.783000+00:00 Read the full story.  

Intel Generated $1 Billion In Revenue From Artificial Intelligence Chips In 2017

Sharing their vision at the recently-concluded Data-Centric Innovation Summit, Intel announced that they had made sold $1 billion of artificial intelligence processor chips last year. Intel’s Xeon processor, whose first ancestor was launched 20 years ago. Now, the tech giant announced that more than $1 billion in revenue came from customers running artificial intelligence on Intel Xeon processors in the data centre. “Our investments in optimising Intel Xeon processors and Intel FPGAs for AI are paying off… In total, since 2014, our performance has improved well over 200 times,” said Navin Shenoy, executive vice president and general manager of the Data Center Group at Intel Corporation. 2018-08-09 13:07:34+00:00 Read the full story.  

Comparing Generative Adversarial Network (GAN) to Encoder Decoder Architecture Is Like Comparing Apples To Oranges

Since the deep learning boom has started, numerous researchers have started building many architectures around neural networks. It is often speculated that the neural networks are inspired by neurons and their networks in the brain. Computational algorithms often mimic and copy these biological structures. But there is yet a lot to be discovered about how the brain actually works. Neuroscience is nowhere close to solving the mystery of the brain. That is why artificial intelligence scientists have to come up with many neural network architectures to solve different tasks. Two of the main families of neural network architecture are encoder-decoder architecture and the Generative Adversarial Network (GAN). 2018-08-09 11:32:49+00:00 Read the full story.  

Conversation-as-a-Service: knowledge economies of scale

Chatbots provides the means for empowering everyone, anywhere with better understanding to help them make more informed, contextual decisions. Though much focus has been about the generalist chatbots like Cortana, Alexa, Duplex, Siri and Bixby, a significant paradigm shift will be through specialist chatbots. Each specialist chatbot will contain synthesised knowledge, enabling the individual to cut through information overload quickly and effectively to reach the best-fit outcome. Specialist chatbots can be made available everywhere for everyone through omnichannel deployment. 2018-08-13 10:49:50 Read the full story.  

Kroger inks Ocado grocery delivery deal to battle Amazon threat

What do food, A.I., and flight control have in common? Well, nothing. Unless you’re the U.K. online food retailer Ocado Group. This canny company uses A.I.-programmed flying robots to coordinate shopping basket fills in their depots, which shaves untold hours and operating costs off of the overheads and has seen its stock rocket in value. U.S. supermarket chain Kroger Co (KR.N) struck a deal with British online grocer Ocado (OCDO.L) to ratchet up its delivery business with the construction of robotically operated warehouses, upping the ante in the battle with Inc (AMZN.O) and sending Ocado shares rocketing. The U.S. grocery industry is dominated by Walmart Inc (WMT.N) and Kroger but has been in upheaval since last summer, when Amazon’s $13.7 billion deal for Whole Foods sent supermarkets scrambling to match the online retailer on home delivery. The Kroger deal announced on Thursday is Ocado’s first in the United States and the British company’s fourth major agreement with retailers in six months. A new system of grocery order picking is heading to North American shores courtesy of new deal being drawn up by an online grocer across the pond and one of the biggest retailers in the U.S. While this doesn’t sound like an obvious investment choice for domestic stock lovers, there may well be some upside in the venture. 2018-08-13 00:00:00 Read the full story.  
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AI & Machine Learning News. 06, August 2018

Amazon’s Echo Look fashion assistant lacks critical context

Amazon’s Echo Look is an Alexa fashion assistant that combines human and machine intelligence to tell you how you look in an outfit, keeps track of what’s in your wardrobe, and recommends clothes to buy from Made generally available to the public in recent weeks, the Echo Look debuted in April 2017, but was available by invite only for more than a year — a first for Alexa-enabled devices. Over time, Amazon will team Echo Look with Prime Wardrobe, an Amazon program akin to modern fashion companies like Stitch Fix and Trunk Club that lets users try on clothes and send back what they don’t want to buy. All the meanwhile, Amazon’s facial recognition software Rekognition keeps making headlines for being used by U.S. law enforcement agencies and misidentifying more than two dozen members of Congress as criminals. Let’s examine why it can be a lot of fun to use the Echo Look, why it took Amazon a year to make the device generally available, and why its fashion assistant’s AI is inherently biased. 2018-08-03 00:00:00 Read the full story. CloudQuant Thoughts… Style Check Reasons explains why the AI chose one outfit over another. “It started out highly human and will become more and more machine, but as one might expect with fashion trends constantly changing, there will always be some human engagement in this keeping track of what styles are now in and what’s changing in fashion”. Again we see products where the AI cannot quite get the desired result (Autonomous cars, Autonomous robots) but we can put a human in at the sticking point as a placeholder, launch a product and – as the AI gets better – slowly nibble away at the human element. Watch this AI fashion area to expand rapidly. After all, Amazon have patented a mirror that helps you dress!    

The dawn of AI marketing is here: You can do it too!

Coming into this year, we were surprised to find no AI events for the marketing or growth agenda, so we planned Transform in San Francisco on August 21 and 22, focused exclusively on real business results and applications for marketing, product, and growth engineer executives. We’re excited because Transform has some amazing speakers — and case studies with real results. There’s so much hype around AI that it starts to sound like voodoo, or something only achievable if you’re Google, Amazon, or Microsoft and therefore able to hire data scientists with salaries of starting NFL quarterbacks. But after talking with and studying upstarts like Stitch Fix and Hopper, we realized just about any company can avail themselves of AI technology. Our motto for this event became: “You can do it too!”. 2018-08-03 00:00:00 Read the full story. CloudQuant Thoughts… I can see my elevator pitch for Netflix : “Think Madmen meets AI”  

Bringing Back the Mighty Chiplet

Big things can often fit into small packages, especially if those packages are tightly bound. The concept of a specialized chiplet is as about as old as the microprocessor but due to more demands for ever more processing with power efficiency and high bandwidth the idea is being revived. This reinvigoration of chiplets comes at a time when U.S. research arm DARPA is refreshing investment in novel architectures and approaches to processing growing data volumes. The agency’s Electronics Resurgence Initiative (ERI), is a five-year, $1.5 billion program to overcome Moore’s Law hurdles and develop new microelectronics that can fit into a wide range of government analytics and IT systems projects. Chip giant Intel took part in the recent ERI Summit where industry and academia came together to sift through ideas for the post-Moore’s Law era of system design, including a discussion of how specialized chiplets might be the cure to some of DARPA’s woes. 2018-07-31 00:00:00 Read the full story. CloudQuant Thoughts… We are all used to the idea of offloading our graphical needs to a GPU and now the abilities of these GPUs to do parallelized processing makes them ideal for AI/ML tasks. But we are seeing more and more secondary processors on the mainboards of consumer products. Apple’s T2 chip which made its way into the new MacBooks recently is a perfect example of a Tech firm utilizing an industry standard CPU for the main processing and a secondary custom private Processing Unit for other tasks to stand out from their competition.  

AWS now has $16 billion of unrecognized revenue

Amazon said in its quarterly report that AWS has at least $16 billion in backlog revenue, up from the previous quarter’s $12.4 billion. The average remaining life of those contracts also extended from 3.2 years to 3.5 years in its most recent quarter. It’s the latest sign of AWS signing more long-term contracts, as opposed to customers paying by the hour. Amazon’s cloud service is locking its customers into bigger and longer-term contracts, signaling a deeper commitment from its already market-leading user-base. Amazon disclosed in its latest quarterly report that it had $16 billion in backlog revenue for Amazon Web Services, up from the previous quarter’s $12.4 billion. The average length of those remaining contracts also extended from 3.2 years to 3.5 years in its latest quarter. Backlog revenue is a non-balance sheet item that represents the total value of future contract obligations. Amazon describes it as “commitments in customer contracts for future services that have not yet been recognized.” The remaining balance gets recognized as revenue once the service is billed and delivered. 2018-08-01 00:00:00 Read the full story. CloudQuant Thoughts… It would be interesting to see if they can split this between Web Serving and Data Research uses.  

Customers buy homes – they don’t buy mortgages

Buying a house is one of the biggest commitments most people ever make – both financially and emotionally. And big usually implies complex. But does that mean buying a mortgage also needs to be complex? Why can’t it be made simpler like other kinds of loans? Why should a customer’s experience while availing a mortgage be any different from the experience of seeking a personal or auto loan? What would it take to make it possible to simply buy the house on Amazon and take delivery of the keys next day from a drone? 2018-08-03 00:00:00 Read the full story. CloudQuant Thoughts… Earlier this year, Rocket Mortgage helped Quicken move into the top spot for Mortgages in the US ahead of Wells Fargo. Bank of Queensland in Australia has radically revamped its home loan business, reducing their “Time to Yes” by 99% while also reducing “Total Touch Time” by 85%. Online banking, Tax returns, and Investment means that most of us no longer need to search for old payslips, Rocket Mortgage offer a “Mortgage in less than 8 minutes” but this is not the end for Mortgage disruption. I purchased my first house with an Australian style mortgage which included automatically escalating mortgage payments (how many of us are paid the same at the start of our mortgage as at the end?) and a no penalty early payoff. US mortgages still have a long way to go and there is still plenty of opportunity for tech-driven disruption.  
Below the Fold…  

Facebook’s chief AI scientist says that Silicon Valley needs to work more closely with academia to build the future of artificial intelligence

Facebook’s chief AI scientist, Yann LeCun, says that letting AI experts split their time between academia and industry is helping drive innovation. Writing for Business Insider, the executive and NYU professor argues that the dual-affiliation model Facebook uses boosts individual researchers and the industry at large. A similar model has historically been practiced in other industries, from law to medicine. 2018-08-10 00:00:00 Read the full story.  

Tech, Big Data, and the Capital Markets

“I met some people working on financial technology. They were really cool and interesting people solving hard problems and I wanted to join them. That was how I made my career decision.” In this video from MarketsWiki Education’s World of Opportunity event in New York, Mike Beller, CEO of Thesys Technologies, talks about the electronification of markets and the explosion of trading venues it created. Beller says managing the mass amounts of data generated by this phenomenon is the industry’s biggest challenge. 2018-07-30 14:59:55+00:00 Read the full story.  

Data breaches: app security under threat

In July news broke that a person’s data on a well-known mobile payment service app could be seen publicly (Venmo… it was Venmo!). In case you missed it, a researcher analysed over 200 million publicly available transactions made using the money-sharing app. Her aim was to draw attention to the amount of information that can be gathered using peer-to-peer apps. She was able to access the data through a public application programming interface, even those who had set their setting to private, and build a picture of their lives with surprising accuracy. From burgers to cannabis oil, if you bought it she knew about it. Her bid to out the ways peer-to-peer apps worked was used to highlight that some people place more trust than they should in the default settings of all types of apps. 2018-08-02 00:00:00 Read the full story.  

5 Resources to Inspire Your Next Data Science Project

We are exposed to seemingly endless streams of data science career advice, but there is one topic that doesn’t get quite enough love: side projects. Side projects are awesome for plenty of reasons, but I like how Julie Zhuo puts it in the simple venn diagram below: Side projects serve as a way to apply data science in a less goal-driven environment than you probably experience at work or school. They offer an opportunity to play with data however you want, while learning practical skills at the same time. Aside from being a lot of fun and a great way to learn new skills, side projects also help your chances when applying for jobs. Recruiters and managers love to see projects that show you’re interested in data in a way that goes beyond classes and employment. 2018-08-03 12:54:27.800000+00:00 Read the full story.  

New Apple Policies Threaten Alternative Data

New Terms of Service required of developers marketing their products via the Apple App Store have introduced heightened legal risks to using data collected by iOS apps – an issue that could challenge certain alternative data vendors and the asset managers who use this data. These new policies have been implemented by Apple in the wake of Facebook’s recent privacy scandals. In the past few months Apple has changed its App Store Terms of Service for App Developers to better address new privacy laws like GDPR, which was implemented in May 2018, as well as the growing concerns of individuals about the privacy of their data collected by these apps. In the wake of these changes, Apple has recently started to remove third-party apps from their App Store for violating the new terms. The most concerning changes for the alternative data industry are found in Section 5.1 of the App Store Review Guidelines. These changes can be summarized as follows:
  • App developers must obtain consent from users to collect their data. In addition, users must be informed how and where their data is being used.
  • Data collected for one purpose may not be repurposed without additional user consent.
  • Data collected by apps sold through Apple’s App Store may only be used for two reasons, to improve the app or to support the serving of advertising.
2018-08-06 02:15:09+00:00 Read the full story.  

AutoKeras: The Killer of Google’s AutoML

Will Google’s Auto ML win the AI ML game. Many say yes, but at $20 an hour others are betting on free OpenSource AutoKeras to defeat the Goliath. Do we want the best AI/ML knowledge out in the open or hidden behind a “Wizard of Oz” curtain? 2018-08-05 19:59:12.213000+00:00 Read the full story.  

Understanding Data Science Classification Metrics in Scikit-Learn in Python

In this tutorial, we will walk through a few of the classifications metrics in Python’s scikit-learn and write our own functions from scratch to understand the math behind a few of them. One major area of predictive modeling in data science is classification. Classification consists of trying to predict which class a particular sample from a population comes from. For example, if we are trying to predict if a particular patient will be re-hospitalized, the two possible classes are hospital (positive) and not-hospitalized (negative). The classification model then tries to predict if each patient will be hospitalized or not hospitalized. In other words, classification is simply trying to predict which bucket (predicted positive vs predicted negative) a particular sample from the population should be placed as seen below. 2018-08-05 19:43:11.811000+00:00 Read the full story.  

Drake — Using Natural Language Processing to understand his lyrics

We know for a fact that Drake’s work is popular but why are the majority of his songs such a hit? Is it the production? Is it the marketing? It is probably a combination of factors. However, the aspect I will be focusing on is his lyrics. Drake’s work is expansive and well-documented, so getting text data was not a difficult task. However, figuring out how to analyze it was. But thanks to recent improvements in NLP (Natural Language Processing), analyzing text data is now easier than ever. 2018-08-04 21:54:49.264000+00:00 Read the full story.  

The Best Machine Learning GitHub Repositories & Reddit Threads from July 2018

  GitHub Repositories
  • Image Outpainting
  • Text Classification Models with TensorFlow
  • MatchZoo
  • GANimation
  • GAN Stability
Reddit Discussions
  • Which deep learning papers should I implement to learn?
  • Use of Science at Organizations like Google Brain/FAIR/DeepMind
  • Some Good Books to Gain a Theoretical Understanding
  • Discussion on how AI will Impact Jobs, both Present and in the Future
  • Common Mistakes People make in Data Visualization
2018-08-01 22:36:07+05:30 Read the full story.  

Intake: Taking the Pain out of Data Access – New Anaconda Data Access Layer

Defining and loading data-sets costs time and effort. The data scientist needs to know what data are available, and the characteristics of each data-set, before going to the effort of loading and beginning to analyze some specific data-set. Furthermore, they might need to learn the API of some Python package specific to the target format. The code to do such data loading often makes up the first block of every notebook or script, propagated by copy&paste. 2018-08-02 11:05:44-05:00 Read the full story.  

Self-supervised learning gets us closer to autonomous learning

Self-Supervised Learning is getting attention because it has the potential to solve a significant limitation of supervised machine learning, viz. requiring lots of external training samples or supervisory data consisting of inputs and corresponding outputs. Yann LeCun¹ recently in a Science and Future Magazine interview presented self-supervised learning as a significant challenge of AI for the next decade. 2018-08-06 12:31:01.376000+00:00 Read the full story.  

Implement CRISP Data Science with AWS SageMaker

This article aims to demonstrate the capability and agility of AWS to develop and host both industry-standard machine learning products and research-level algorithms. CRISP (Cross-industry standard process for data mining) is an agile workflow or framework that captures the separation of concerns in Data Science well. Standard CRISP includes above 7 components (Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, Deployment, Monitoring) and 4 phases (Form business questions and collect data, Process data and build model based on it, Package model into data product and deploy, Monitor the model and collect information). It is not uncommon to go back-and-forth in the process. For example, we could be forced to collect certain data to answer a business question, or experiment with various statistical models to lift the accuracy of the model. 2018-08-05 19:59:12.213000+00:00 Read the full story.  

Various Optimisation Techniques and their Impact on Generation of Word Embeddings

We are a machine learning data annotation platform to make it super easy for you to build ML datasets. Just upload data, invite your team and build datasets super quick. Word embeddings are vectorial representations that are assigned to words, that have similar contextual usages. What is the use of word embeddings you might say? Well, if I am talking about Messi and immediately know that the context is football… How is it that happened? Our brains have associative memories and we associate Messi with football… To achieve the same, that is group similar words, we use embeddings. Embeddings, initially started off with one hot encoding approach, where each word in the text is represented using an array whose length is equal to the number of unique words in the vocabulary. 2018-08-06 10:26:01.236000+00:00 Read the full story.  

Policy Networks vs Value Networks in Reinforcement Learning

In Reinforcement Learning, the agents take random decisions in their environment and learns on selecting the right one out of many to achieve their goal and play at a super-human level. Policy and Value Networks are used together in algorithms like Monte Carlo Tree Search to perform Reinforcement Learning. Both the networks are an integral part of a method called Exploration in MCTS algorithm. They are also known as policy iteration & value iteration since they are calculated many times making it an iterative process. Let’s understand why are they so important in Machine Learning and what’s the difference between them? 2018-08-05 08:11:03.748000+00:00 Read the full story.  

How to write your favorite R functions — in Python?

One of the great modern battles of the data science and machine learning is “Python vs. R”. There is no doubt that both have gained enormous ground in recent years to become top choice of programming languages for data science, predictive analytics, and machine learning. In fact, in a recent article from IEEE, Python overtook C++ as the top programming language of 2018 and R has firmly secured its spot in top 10. However, there are some fundamental differences between these two. R was developed primarily as a tool for statistical analysis and quick prototyping of a data analysis problem. Python, on the other hand, was developed as a general purpose modern object-oriented language in the same vein as C++ or Java but with a simpler learning curve and more flexible demeanor. Consequently, R continues to be extremely popular among statisticians, quantitative biologists, physicists, and economists alike whereas Python has has slowly emerged as the top language of choice for day-to-day scripting, automation, backend web-development, analytics, and general machine learning frameworks with extensive support base and open source development community work. 2018-08-04 21:10:56.829000+00:00 Read the full story.  

How Google’s BigQuery ML Is Empowering Data Analysts

In case you were wondering, here’s another sign of the Google Cloud Vs Amazon Web Services war heating up. Google has now brought in the big guns in the analytical data warehousing space with by embedding machine learning capabilities into Google BigQuery. Google BigQuery is an analytics service, low-cost enterprise data warehouse which has now been rebranded as BigQuery ML. One of the key features of BigQuery is that it transforms SQL queries into complex execution plans, dispatching them onto execution nodes to promptly provide insights into the data. BigQuery enables developers to execute SQL as a massively parallel processing query with hundreds of CPU cores and ample disk storage, scanning and aggregating terabytes of data in seconds. BigQuery ML, a capability inside BigQuery enables analysts and data scientists to build and deploy ML models on massive structured or semi-structured datasets. 2018-08-04 07:55:40+00:00 Read the full story.  

Product Overview and Analysis : SigOpt Automated Model Tuning

SigOpt’s optimization solution automates the tuning of any model built with any framework on any infrastructure to maximize the return on machine learning, artificial intelligence and general research investments. Built by experts for experts, this solution embeds an ensemble of Bayesian and global optimization algorithms within a standardized platform that is accessible through a simple REST API. This automated, scalable and comprehensive approach enables teams to tune much earlier and more often, which, in turn, helps transform traditional discrete data projects with mathematical outputs to continuously deployed products that improve business outcomes. 2018-08-03 00:00:00 Read the full story.  

Inside Cloud AutoML: Google’s New Marketing Platform To Drive Cloud Revenue

If there is one big takeaway from the recently-concluded Google Cloud Next 2018 conference, is how the tech behemoth wants to position itself as a strong contender in the hybrid cloud business dominated by Amazon Web Services and Microsoft. This was evident in Google’s embrace of private, hybrid, edge and multi-cloud computing. Another key announcement was about the company expanding Cloud AutoML — the machine learning platform it announced at Google I/O last year — into new areas like Vision, Natural Language and AutoML Translation. This comes at a time when AWS cloud revenue increases 48.9 percent in the second quarter. As the cloud market moves into various cloud architectures, companies are getting more competitive in the face of continuous change and introducing additional artificial intelligence products to draw in more customers. 2018-08-04 07:51:14+00:00 Read the full story.  

Bringing Intelligence to the Edge with Cloud IoT

There are also many benefits to be gained from intelligent, real-time decision-making at the point where these devices connect to the network—what’s known as the “edge.” Manufacturing companies can detect anomalies in high-velocity assembly lines in real time. Retailers can receive alerts as soon as a shelved item is out of stock. Automotive companies can increase safety through intelligent technologies like collision avoidance, traffic routing, and eyes-off-the-road detection systems. But real-time decision-making in IoT systems is still challenging due to cost, form factor limitations, latency, power consumption, and other considerations. We want to change that.2018-08-03 10:37:02+00:00 Read the full story.  

How Machine Learning Is Changing The Software Development Paradigm

Can machine learning be used to accelerate the development of traditional software development lifecycle? As artificial intelligence and other techniques get increasingly deployed as key components of modern software systems, the hybridisation of AI and ML and the resultant software is inevitable. According to a research paper from the University of Gothenburg, AI and ML technologies are increasingly being componentised and can be more easily used and reused, even by non-experts. Recent breakthroughs in software engineering have helped AI capabilities to be effectively reused via RESTful APIs as automated cloud solutions. 2018-08-04 07:45:04+00:00 Read the full story.  

Liquidnet Launches Discovery as Part of VHT Platform

Block trading venue and technology firm Liquidnet just upped the ante on its Virtual High Touch platform. The firm just launched Discovery – the first integration from the OTAS acquisition and is the start of a new generation of trader intelligence tools designed to facilitate more value-added conversations with portfolio managers, elevating their desk’s ability to generate short-term alpha. Virtual High Touch combines advanced data analytics, unique liquidity sourcing tools, advanced algorithms, and real-time decision support. The idea behind VHT is that technology – when delivered in a meaningful, insightful and actionable way – can make the difference in terms of capturing and delivering alpha. Discovery is the culmination of seven years of research and development, leveraging AI and machine learning that draws in massive amounts of market data, distills it into actionable insight tailored to every order on a blotter that is synced with Liquidnet. 2018-07-31 07:07:16-04:00 Read the full story.  

Aera Technology’s Cognitive OS Helps Fine Tune Business Operations

For all the development work that’s been done on enterprise applications that use artificial intelligence to analyze corporate data, the CEO of Aera Technology believes companies aren’t gaining many insights into how well their businesses are performing. “There’s been a lot of work done to help companies process information, but there hasn’t been any work done to help them think,” Fred Laluyaux, president and CEO of Aera Technology told eWEEK at a briefing here at his company’s headquarters. Aera Technology is trying to change that by deploying a cloud-based cognitive operating system that is designed to analyze operational data to show corporate decision makers how they can take actions that improve business performance. 2018-07-31 00:00:00 Read the full story.  

UOB to launch digital-only bank

United Overseas bank is to launch a digital-only bank to cater for the massive and increasing rate of mobile-first consumers in South East Asia. UOB says the new bank will be powered by next-generation artificial intelligence, machine learning, data analytics, user interface design and smartphone capabilities, leveraging inhouse expertise alongside innovations provided by its recent credit assessment joint venture with and its investment and partnership with Personetics. 2018-08-03 10:33:00 Read the full story.  

Aura, Telefónica’s AI, learns the language of people to transform customer engagement

With over 350 million customers in 17 countries, Telefónica is one of the largest telecommunications companies in the world. But the Spanish-based organization wants to do more than connect people with mobile, landline, internet and pay TV services. It wants to make digital life easier for customers. Founded in 1924, Telefónica has transformed into a modern, data-driven company in recent years, with major investments in infrastructure and technology. The upgrades enabled the company to launch Aura, an artificial intelligence-powered digital assistant that “learns the language of people so that they don’t have to learn the language of machines,” says Telefónica. It is available in Spain, Brazil, the United Kingdom, Germany, Argentina and Chile through mobile apps, webs and third-party channels including Facebook Messenger and Google Assistant. 2018-08-01 09:00:03-07:00 Read the full story.  

Deriving value from data: How AI can power smarter credit decisions

The reality of AI creates an opportunity – and a responsibility – for banks to team up with fintech companies in order to stay ahead of emerging competition from disruptors. Banks have put AI on the innovations agenda for years now, but have yet to execute. Meanwhile, the underserved SME financing market, a $2.6tn opportunity, could be snatched up by e-commerce platforms, payment processors, and even telecommunication companies. The time for banks to put AI into action is now before they become displaced by disruptors. Former Cisco chairman and CEO John Chambers famously said that more than 40% of businesses would disappear over the next decade if they fail to execute on an AI strategy. That was back in 2015, so time is of the essence. 2018-08-01 00:00:00 Read the full story.  

Digital Guardian Improves Data Loss Prevention With Behavior Analytics

Digital Guardian will announce on Aug. 6 that it is bringing user and entity behavior analytics (UEBA) capabilities to its Data Protection Platform. The new UEBA capabilities will complement the data loss prevention (DLP) features in Digital Guardian’s platform, enabling organizations to more closely align identity and user behavior with security policy and enforcement. The UEBA feature makes use of machine learning to gain insight into user behavior to identify potential malicious actions. 2018-08-03 00:00:00 Read the full story.  

Mindshare Medical launches AI cancer screening tech that can see data ‘beyond our perception’

You might think diagnosing cancer is easy: Someone either has a cancerous tumor or they don’t. In practice, it’s much harder. Telling the difference between a deadly and a harmelss lump is literally “beyond our perception” with current imaging technology, Ilya Goldberg told GeekWire. That’s where he thinks artificial intelligence can step in. Goldberg is a longtime biologist and machine learning expert, and he is the co-founder and CTO of Mindshare Medical. The startup is developing artificial intelligence tools that can diagnose cancer using imaging data that is invisible to the human eye. 2018-08-02 15:40:51-07:00 Read the full story.  

Interview: How Google Cloud CEO Diane Greene is navigating the tricky world of cloud-based AI

It has been a weird year for Google Cloud CEO Diane Greene. Just as the company’s cloud-computing division has started to hit its stride, signing enterprise deals and reaping the rewards of its tech investments in container technology, employee backlash over artificial-intelligence contracts with the military have kept things interesting during Greene’s third year running the third-place cloud-computing company. While Google is still looking up at Amazon Web Services and Microsoft Azure when it comes to infrastructure cloud computing, it appears to be finding the balance between keeping engineers happy with cloud-native computing tools and courting enterprise company suits with service-level agreements and steak dinners. 2018-07-30 17:34:42-07:00 Read the full story.  

BaFin: Big Data Meets Artificial Intelligence Study

How do technological developments in data processing and analysis impact the financial sector? What are the implications for financial stability, market supervision, firm supervision, and collective consumer protection? The “Big Data meets Artificial Intelligence” report, which BaFin published on 15 June 2018 , helps to answer these questions. 2018-08-06 05:25:38-04:00 Read the full story.  

Scaling Game Simulations with DataFlow – Tetris

Dataflow is a great tool for building out scalable data pipelines, but it can also be useful in different domains, such as scientific computing. One of the ways that I’ve been using Google’s Cloud Dataflow tool recently is for simulating gameplay of different automated players. Years ago I built an automated Tetris player as part of the AI course at Cal Poly. I used a metaheuristic search approach, which required significant training time to learn the best values for the hyperparameters. I was able to code a distributed version of the system to scale up the approach, but it took significant effort to deploy on the 20 machines in the lab. With modern tools, it’s trivial to scale up this code to run on dozens of machines. It’s useful to simulate automated players in games for a number of reasons. One of the most common reasons is test for bugs in a game. You can have bots hammer away at the game until something breaks. Another reason for simulating gameplay is to build bots that can learn and play at a high level. There’s generally three ways of simulating gameplay: Real Time, Turbo and  Headless. 2018-08-04 23:15:47.008000+00:00 Read the full story.  

HSBC joins $20m funding round for financial crime specialist Quantexa

Big data and enterprise intelligence outfit Quantexa has raised $20 million in a Series B funding round led by Dawn Capital and backed by HSBC and Albion Capital. Quantexa’s technology uses real-time entity resolution, network analytics and AI to knit together vast and disparate data sets and derive actionable intelligence to fight financial crime. Founded in 2016, the London-headquartered firm now boasts a team of 90 staffers and has this year scored money laundering prevention deals with HSBC and Deloitte. 2018-08-03 00:01:00 Read the full story.  

AI Weekly: Amazon Echo is basically a Dash button with speakers

Google and Amazon do not care much where you speak with their respective assistant. As long as you’re on team Alexa, Amazon doesn’t really mind that Show Mode appears to cannibalize the Echo Show. Of course, once you’re locked into Google Assistant, you may be more likely to choose YouTube TV over Prime Video, Google Pay over Amazon Pay, and most importantly, Google Express over Amazon’s massive online marketplace. The news is also indicative of a gradual shift toward more visual experiences with AI assistants, which makes sense for a number of reasons. 2018-08-03 00:00:00 Read the full story.  

The top data structures you should know for your next coding interview

Niklaus Wirth, a Swiss computer scientist, wrote a book in 1976 titled Algorithms + Data Structures = Programs. Forty plus years later, that equation still holds true. That’s why software engineering candidates have to demonstrate their understanding of data structures along with their applications. Almost all problems require the candidate to demonstrate a deep understanding of data structures. It doesn’t matter whether you have just graduated (from a university or coding bootcamp), or you have decades of experience. Sometimes interview questions explicitly mention a data structure, for example, “given a binary tree.” Other times it’s implicit, like “we want to track the number of books associated with each author.” 2018-07-30 22:29:48.695000+00:00 Read the full story.  

B.R.AI.N. Index Tracks Disruptive Technologies

As part of the expanding STOXX thematic offering, we are excited to introduce a new index tracking four technologies transforming business globally. The iSTOXX® Developed Markets B.R.AI.N. Index is made up of companies that generate more than 50% of their revenue from biotechnology, robotics, artificial intelligence (AI) and nanotechnology. In covering these four trends, the index aims to give investors access to the economic benefits of modern industrial change. 2018-08-06 05:44:59-04:00 Read the full story.  

Weekly Selection — Aug 3, 2018 – Towards Data Science

  • AutoKeras: The Killer of Google’s AutoML
  • 5 Resources to Inspire Your Next Data Science Project
  • Deploying Keras Deep Learning Models with Flask
  • Graphs & paths: PageRank
  • Interactive Data Visualization with D3.js
  • Brewing up custom ML models on AWS SageMaker
  • Using Uncertainty to Interpret your Model
  • Graphs and ML: Multiple Linear Regression
2018-08-03 12:54:27.800000+00:00 Read the full story.    
Behind a Paywall…

The CEO of a hot Silicon Valley startup who built a product used by more than 15,000 companies reveals the single way you’ll know whether or not people will actually buy your product

Peter Reinhardt, CEO and co-founder of data analytics company Segment, discovered how to find right product fit the hard way: He struggled to find a proper application for his company’s product twice before hitting gold the third time around. In retrospect, he says there was a fundamental difference in early conversations surrounding the current iteration of his product, compared to the examples that turned out to be less successful. 2018-08-05 00:00:00 Read the full story.  
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Open, Close, High, Low

AI & Machine Learning News. 30, July 2018


Comprehensive Hands on Guide to Twitter Sentiment Analysis with dataset & code

Natural Language Processing (NLP) is a hotbed of research in data science these days and one of the most common applications of NLP is sentiment analysis. From opinion polls to creating entire marketing strategies, this domain has completely reshaped the way businesses work, which is why this is an area every data scientist must be familiar with. Thousands of text documents can be processed for sentiment (and other features including named entities, topics, themes, etc.) in seconds, compared to the hours it would take a team of people to manually complete the same task. In this article, we will learn how to solve the Twitter Sentiment Analysis Practice Problem. 2018-07-30 09:08:20+05:30 Read the full story. CloudQuant Thoughts… Of course we already have cleaned Twitter (SMA) and Stocktwits sentiment data available to our users. You can experiment with it today at, utilizing the data to add to or make US Equity trading decisions using our free backtesting system.  

On the eve of a 6-month pilot, details its self-driving car plans

It’s been almost a year since Waymo, the autonomous vehicle division of Google parent Alphabet, became the first company to operate autonomous cars on public roads without drivers behind the wheel. Now intends to follow suit. This month, the Silicon Valley startup will set loose a fleet of self-driving Nissan NV200 vans in Frisco, Texas. They won’t be completely autonomous — a small army of safety drivers and remote operators will ensure rides go off without a hitch. And the vehicles will be contained in a geofenced area. 2018-07-30 00:00:00 Read the full story. CloudQuant Thoughts… Whilst it’s geofenced area is quite small, this is still a major step forward. I particularly enjoyed the cyclist trying to indicate to the driver that he couldn’t see very well because of the sun only to realize that there was no driver.  

Amazon challenges ACLU study on facial recognition tech and police

Amazon’s Matt Wood, a leader on the Amazon Web Services machine learning team, expressed skepticism about an experiment the ACLU conducted using Rekognition to compare headshots of the members of Congress with a database of 25,000 mugshots. The test set Rekognition to make matches with an 80 percent confidence rating, according to Amazon. The ACLU says Rekognition incorrectly matched 28 members of Congress to people pictured in arrest photos. “The false matches were disproportionately of people of color, including six members of the Congressional Black Caucus,” the ACLU said in a blog post. In Wood’s response, he says Amazon recreated the ACLU experiment comparing photos from members of Congress to a database of 850,000 faces with a 99 percent confidence threshold. Amazon says it saw a 0 percent misidentification rate, “despite the fact that we are comparing against a larger corpus of faces.”. Wood also wrote “The default confidence threshold for Rekognition is 80%, which is good for a broad set of general use cases (such as identifying objects, or celebrities on social media), but it’s not the right one for public safety use cases”. 2018-07-27 20:26:58-07:00 Read the full story (at GeekWire). 2018-07-26 00:00:00 Read the full story (at CNNTech). CloudQuant Thoughts… AI is only as good as the data you give it to learn from. It seems a little spurious to complain that it is ineffective when you have given it such a small sample set and then set its confidence rating so low.  Having said that, based on historical precedent it does seem more likely that the results coming out of the Scotland Yard trial (98% false positives) are more trustworthy than those coming from the Chinese government (accurate 99.8% of the time). Who do you believe?  

Everyday Lessons from the Facebook Data Scandal

As the facts come out slowly, there are some realities about what Facebook did and did not do that are highly misguided. There are other realities of the nature of big data capture and digital surveillance in our lives that we must reconsider. Indeed, the notion of privacy and protection are being redefined practically, even if we as a society are not ready for it. Let’s take a closer look at some of the lessons:
  • Big Data Capture and Digital Surveillance are Constant and Permanent
  • Best Intentions Can Lose Out to Temptations of Profit
  • Data Misuse Can Cause Economic Harm to Consumers
  • Data Monetization Often Exploits Consumers
  • Privacy is Undefined
2018-07-24 00:00:00 Read the full story. CloudQuant Thoughts… A very well written piece hitting all the points on how we should all be carefully policing our own personal data.  

UPS Is Thinking About a Future With Autonomous Vehicles

UPS (UPS) is caught in the middle on whether to aggressively pursue autonomous vehicles. “In autonomous, we are kind of in between,” UPS CFO Richard Peretz tells TheStreet. Peretz says UPS’ autonomous driving efforts are being done in test environments, not on public roads. “The driver is an important part of the value proposition for our customers,” Peretz says. In a blog post on its website, UPS acknowledges the inevitable army of self-driving vehicles likely to disrupt many industries. 2018-07-28 08:27:54-04:00 Read the full story. CloudQuant Thoughts… Finally, but they are not alone, there is going to be a lot of disruption surrounding autonomous vehicles…  33 industries that will be disrupted by Autonomous Vehicles.    

Below the Fold…

Over 200 of the Best Machine Learning, NLP, and Python Tutorials — 2018 Edition

The article contains the best tutorial content that I’ve found so far. It’s by no means an exhaustive list of every ML-related tutorial on the web — that would be overwhelming and duplicative. Plus, there is a bunch of mediocre content out there. My goal was to link to the best tutorials I found on the important subtopics within machine learning and NLP. By tutorial, I’m referring to introductory content that is intending to teach a concept succinctly. I’ve avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don’t do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you’re trying to learn a specific niche topic or want to get different perspectives. 2018-07-30 11:43:06.562000+00:00 Read the full story.  

Faster Machine Learning – The Big Deal With GPUs

If you’ve been following data science and machine learning, you’ve probably heard the term GPU. But what exactly is a GPU? And why are they so popular all of a sudden? A Graphics Processing Unit (GPU) is a computer chip that can perform massively parallel computations exceedingly fast. Throughout the 2000s, companies like NVIDIA and AMD invested in GPUs to improve performance for video gaming and 3D modeling. As developers designed increasingly realistic looking games, they needed more powerful hardware to render the game images. Researchers have long experimented with using GPUs for more than just games, but the last 10 years have seen an expansion of GPUs into many data science applications, including deep learning. 2018-07-27 12:13:19-05:00 Read the full story.  

The 3 next steps in conversational AI

Conversational AI is a subfield of artificial intelligence focused on producing natural and seamless conversations between humans and computers. We’ve seen several amazing advances on this front in recent years, with significant improvements in automatic speech recognition (ASR), text to speech (TTS), and intent recognition, as well as the rocketship growth of voice assistant devices like the Amazon Echo and Google Home, with estimates of close to 100 million devices in homes in 2018. But we’re still a long way away from the fluent human-machine conversation promised in science fiction. Here are some key advances we should see over the next decade that could get us closer to that long-term vision. 2018-07-29 00:00:00 Read the full story.  

Top Conferences In Singapore For Data Scientists To Attend

Singapore has grown to have a prominent position in Analytics adoption, especially if we compare its regional peers. It also wins on being a financial and technology hub in south-east Asia. Data science adoption is increasing significantly in the organizations in Singapore. Here we present top conferences on Analytics and Data science that have created a niche over time. 2018-07-27 10:24:01+00:00 Read the full story.  

Predictive analytics is key to insurance sector competitiveness

Access to data is essential to the success of insurance companies—data and analytics play a critical role in sales and distribution, fraud detection and prevention, and underwriting and claims management. They also provide insurance companies with valuable customer insights. One particular group of techniques—predictive analytics, which uses data and statistical algorithms to analyse historical data to forecast behaviour—continues to disrupt the insurance sector. 2018-07-24 00:00:00 Read the full story.  

Financial regulators embrace AI and machine learning

With increasing institutional use of Al and machine learning methods, more and more industry regulators are making use of such software to provide financial stability through services and systemic risk surveillance. Central to the UK Financial Conduct Authority’s (FCA) agenda is their intent to develop “both their technology side – cloud analytics – as well as building out our human side – our data science capability” to allow an “osmotic” expansion across the wider organisation, “effectively enabling us to leverage machine learning,” says head of regtech and advanced analytics at FCA, Nick Cook. Here are four key regulatory areas set for disruption by AI and machine learning:
  • Prevention of financial fraud
  • Anti-Money Laundering (AML) increase in Combating the Financing of Terrorism (CFT) detection
  • Improved techniques for risk assessment and prevention
  • Regulatory reporting
2018-07-30 00:00:00 Read the full story.  

AI Weekly: For self-driving cars, the path to public acceptance is transparency and an abundance of caution

Self-driving cars have transformative potential. They’re poised to reduce traffic fatalities, ease congestion, and even decrease carbon emissions — not to mention cutting down on the amount of city space dedicated to parking. There’s just one problem: Most people are afraid of them. Two studies this week — one by the Brookings Institution and another from the Advocates for Highway and Auto Safety (AHAS) — found that a majority of Americans aren’t convinced of driverless cars’ safety. More than 60 percent of respondents to the Brookings poll said that they were “not inclined” to ride in self-driving cars, and almost 70 percent of those surveyed by the AHAS expressed “concerns” about sharing the road with them. The sentiments echo those expressed in a poll conducted earlier this year by think tank HNTB, which found that 59 percent of people expect self-driving cars to be “no safer” than cars controlled by human drivers. That’s despite the fact that more than 94 percent of car crashes are caused by human error and that in 2016 the top three causes of traffic fatalities were distracted driving, drunk driving, and speeding. 2018-07-27 00:00:00 Read the full story.  

Kixeye enlists players to provide customer support for Battle Pirates

Kixeye launched its Battle Pirates strategy game on Facebook seven years ago, but the game still has a loyal following of 100,000 players. The audience isn’t big enough to serve with Kixeye’s own internal technical support team, but the San Francisco company came up with a novel resource to get the job done: the players themselves. The team created a machine learning and natural language processing system to route support questions to the people who are most likely to be able to answer it. The players are rated on quality of their answers, and the ones who have high ratings will get more questions sent their way. And Getze said the players are compensated in the form of their favorite currency, the virtual currency in the game. Normally, the players have to earn that currency or pay real money for it. But as participants in the Peer2Peer program, they can earn the equivalent of $2 per ticket answered. About 2 percent to 3 percent of the players participate, and that’s all Kixeye needs right now. The players have to pass a test demonstrating their knowledge of the game. 2018-07-27 00:00:00 Read the full story.  

Twitter uses AI to promote more “healthy conversation”

Twitter saw a slight dip in monthly active users during the second quarter of 2018, but exceeded revenue expectations. In June, the company said it was now proactively identifying and “challenging” 9.9 million spam accounts per week. The move is part of Twitter’s ongoing efforts to promote more “healthy conversation” on the platform. Twitter CEO Jack Dorsey said during the earnings call. “We made a major shift this year in shifting more of our model and enforcement towards behavior and conduct on the platform, rather than content. That’s entirely machine learning and deep learning-driven.” 2018-07-27 00:00:00 Read the full story.  

EagleView aerial imagery venture acquiring Australia’s Spookfish, valued at $90M

EagleView Technologies, a privately held aerial imagery and data analytics company headquartered in Bothell, Wash., says it intends to acquire Spookfish, an Australian company that’s in the same business. The deal opens the way for EagleView to deploy Spookfish’s advanced aerial camera technology in North America, and establishes a presence for EagleView in the Australian market. Thanks to the tech upgrade, EagleView will be able to deliver aerial imagery at significantly higher resolution than before, augmented by machine learning processes. The company’s customers include insurance underwriters, tax assessors, city planners, contractors, utilities and others who need accurate information about property conditions or changes. 2018-07-26 22:25:55-07:00 Read the full story.  

Microsoft Is Now a Major Player in Health Care Inc. (AMZN) isn’t the only tech titan dabbling in the health-care industry. Old-guard IT industry leader Microsoft Corp. (MSFT) has grown its health unit into a multi-billion-dollar business, the company’s Chief Medical Officer Simon Kos told CNBC’s “Beyond the Valley” podcast in an episode entitled “Your Health Could Soon Depend on Artificial Intelligence.” The software giant says it employs 1,100 people in health care and claims 168,000 customers in the industry. 2018-07-26 09:26:00-06:00 Read the full story.  

Ignore Tesla CEO Elon Musk’s Artificial Intelligence Driven Apocalypse: AI CEO

Tesla’s (TSLA) Elon Musk should chill about the potential long-term impact of artificial intelligence, says serial entrepreneur Zia Chishti. “Elon has made some interesting statements of late, so I would take his pronouncements with a grain of salt,” Chishti, who is chairman and CEO of surging AI firm Afiniti, tells TheStreet. “AI is just a way to identify patterns in complex fields, it’s not going to nuke the world — there is no chance of that. I think the visions of the impending apocalypse as a result of robot intelligence is fanciful — so I wouldn’t be overly concerned about Elon Musk’s perspective on it.” 2018-07-30 07:00:00-04:00 Read the full story.  

AI Assistant-Equipped Smartphones Will Comprise Half Of New Releases This Year

Artificial intelligence is becoming a major aspect of new smartphones. An industry source says around half of the devices that are launching this year will come equipped with an AI assistant, hinting at the stronger demand for more advanced phone features. Industry consulting firm Strategy Analytics Inc. said via Korea Herald Sunday that about 47.7 percent of smartphones that will be sold on the global market will have some sort of on-device AI assistant. To show just how demand for AI assistant increased, the predicted figure is up from the 36.6 percent last year. 2018-07-29 22:55:57-04:00 Read the full story.  

IBM Watson AI criticised after giving ‘unsafe’ cancer treatment advice

IBM’s Watson supercomputer has come under fire for providing incorrect and ‘unsafe’ healthcare treatment advice to cancer patients. The system is being used in 230 hospitals around the world to help doctors diagnose patients. It does so by using artificial intelligence to analyse their medical data in combination with information from hundreds of medical journals. Since 2015, Watson has given advice on nearly 60,000 patients. A report from health website Stat News states that internal documents shared by IBM Watson’s former deputy health chief Andrew Norden provided strong criticism of the Watson for Oncology system. It stated that the “often inaccurate” suggestions made by the product raise “serious questions about the process for building content and the underlying technology”. For example, Watson reportedly suggested giving the drug Bevacizumab to a 65-year-old man diagnosed with lung cancer, who also seemed to have severe bleeding. One of the side effects of the drug is that can lead to “severe or fatal hemorrhage”. According to the documents reviewed by Stat, a doctor at Florida’s Jupiter Hospital told IBM: “We bought it for marketing and with hopes that you would achieve the vision. We can’t use it for most cases.” 2018-07-27 00:00:00 Read the full story.  


Facebook’s stock dropped by $120 billion this week, but critics are dead wrong for calling it ‘doomed’

Even though Facebook saw its market valuation fall by 20% Thursday, CEO Mark Zuckerberg and company still have plenty to smile about. AP You might have thought from the tone of the coverage following Facebook’s earnings report Wednesday that the company was on the brink of bankruptcy. The results were repeatedly dubbed “disastrous” by reporters and analysts alike. The company was called ” friendless ” in headlines. And following the report , in… 2018-07-27 00:00:00 Read the full story.  

Inventor of Viagra raises funding to treat 7,000 rare diseases

the world’s rarest diseases Dr David Brown, the scientist who developed the blockbuster treatment for erectile dysfunction for Pfizer, is the co-founder of Healx, a UK medical tech startup that uses machine learning to find treatments for 7,000 rare conditions that do not currently have an approved method of treatment. The $10m (£7.6m) initial funding round, led by European venture capital investor Balderton Capital, will allow Healx to develop new technology for what it described as “automated large-scale drug discovery”……. 2018-07-26 00:00:00 Read the full story.  

The top tech executive at $41 billion investment firm Fortress is leaving to start his own data-focused fund

Steve Helber/AP Hylton Socher, the chief technology and information officer of Fortress Investment Group, the $41 billion hedge fund, is leaving the fund to start his own venture, Business Insider has learned. Socher, whose career at Fortress spans a decade, fell down the rabbit hole of big data and other new-wave technologies sweeping Wall Street, he said in an interview. LinkedIn Socher’s firm, which he expects to be up-and-running by the fi… 2018-07-28 00:00:00 Read the full story.  

The ex-army man taking on the big data dark arts

coffee. For the former army officer and special operations expert, it’s been an unlikely journey. He spent years fighting in Iraq and Afghanistan before launching what could be the UK’s next bet in big data and artificial intelligence. Unsurprisingly, Bassett Cross does not come across as your typical tech chief executive. The Silicon Valley uniform of sandals and T-shirts won’t do for the ex-military man, who later worked as an investment banker at JP Morgan. He cuts a disciplined, hard-nosed figure. His new venture – Adarga – uses AI to change the way intelligence agencies and defence companies…. 2018-07-29 00:00:00 Read the full story.  

Trade war and iPhone sales: What to watch for in Apple earnings

Apple CEO Tim Cook Drew Angerer/Getty Images Apple has a chance to give investors another reason to keep Apple’s bullish streak going when it reports earnings after markets close on Tuesday . Apple shares are up 15% in the past three months, buoyed by a massive capital return program announced in April. In its fiscal 3rd quarter earnings, which cover the three months ending in June, Apple will reveal whether the iPhones it launched last Septemb… 2018-07-29 00:00:00 Read the full story.  

Amazon AI expert says government should regulate facial recognition

One of Amazon’s leading artificial intelligence experts has suggested that government intervention is needed as a watchdog to monitor the development of facial recognition for the police. The call comes after Amazon’s own facial recognition technology, known as “Rekognition”, was slammed by an American civil rights group for its apparent inaccuracy. Dr Matt Wood, Amazon Web Services general manager of deep learning and artificial intelligenc… 2018-07-29 00:00:00 Read the full story.
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AI & Machine Learning News. 23, July 2018 Delivers a Driver-Assist System That Rivals Super Cruise and Autopilot

When George Hotz first shook the foundation of a massive global industry, he was only 17 years old. His feat: He was the first person to unlock an iPhone, thereby enabling its use across wireless networks other than AT&T. Roughly a decade later, Hotz is on the verge of upending another industry.
First drawn to self-driving technology after conversations with Elon Musk about potential work on Tesla’s Autopilot, Hotz instead chose to challenge the auto industry by forging his own path. The results of his efforts became apparent last week when his startup,, showcased the latest iteration of an advanced driver-assist system unlike anything else on the road.
2018-07-22 00:00:00 Read the full story. CloudQuant Thoughts: Utilizing ML, AI and lots of data gathered through early adopters using its beta model as well as its Panda Dongle and its Chffr app for mobile phones, George Hotz’ company has been able to create an impressive automated driving system that connects to most new Hondas and Toyotas, and some Acuras and GM models. The cars must have lane assist and enhanced cruise control but with just that technology connected to an Android phone using OpenPilot your car can autodrive as well as a Tesla for under $1000. That is pretty impressive for a small firm and it is all thanks to lots and lots of Deep Machine Learning. If you want more information watch this TechCrunch launch discussion from 2016, the back and forth at the end goes into a little more detail as to why he thinks his method is the best way to move automated driving forwards.

How decision Trees work by Brandon Rohrer of Facebook

Decision trees are one of my favorite models. They are simple, and they are powerful. In fact most high performing Kaggle entries are a combination of XGBoost, which is variant of decision tree, and some very clever feature engineering. The concept behind decision trees is refreshingly straightforward. Imagine creating a data set by recording the time you left your house, and noting whether you arrived at work on time. Looking at it, you can see that for the most part, departure times before 8:15 result in punctuality, and departure times after 15 result in tardiness. 2018-07-22 00:00:00 Read the full story. CloudQuant Thoughts: A very nicely put together introduction video. Well worth 15 minutes of your time (or 10 if, like me, you watch all YouTube videos at 1.5x speed!)  

A case for less human bots

In March, Bank of America launched its new in-app AI-powered assistant. Named Erica, the bot presumably takes its name from “America.” Eric surely could have sufficed as well, but giving a customer service bot a female moniker, and voice, has become common practice. Think Alexa, Cortana, and Siri. Even loading up an on-screen webchat is likely to bring up a female-sounding name. To find their male counterparts, move into more technical, knowledge-oriented spaces. Think IBM Watson (in contrast to IBM HR assistant “Myca”) and “legal advisor” bot ROSS. There’s Kensho the financial analyst and Ernest the bank aggregator. 2018-07-22 00:00:00 Read the full story. CloudQuant Thoughts:  Why are most AI driven Bots female? Why are we comfortable having female AI helpers? Something seems off. It just feels wrong. If we ever create a Bot at work we are going to take a more C3PO type approach – one without gender, race, etc.  

Google Wants to Dominate AI in 2018. Here’s Why

Let’s get this out of the way: ever since the 2001 movie A.I. (and actually way before then, too), people have believed that artificial intelligence would soon emerge in the form of a race of robots that imitate human behavior until they become so smart and successful that they ultimately overpower the  human race. We envision artificial intelligence as computers taking on lives of their own and using their brains to pull all sorts of shenanigans. 2018-07-23: Read the full story. CloudQuant Thoughts:  We welcome guest blogger Anna Kucirkova to our site. Thanks for the post!  

stock chartsEventus Systems Appoints First Outside Directors with Three Industry Veterans

Eventus Systems, Inc., a provider of innovative regtech software solutions for the capital markets, today announced that it has appointed three highly acclaimed industry veterans as the first outside directors on its Board. The newly constituted Board will meet for the first time this afternoon. The new Board members are Kim Taylor, former President, Clearing and Post-Trade Services at CME Group; Fred Hatfield, former Commissioner of the U.S. Commodity Futures Trading Commission; and D. Keith Ross, Jr., Executive Chairman of PDQ Enterprises, parent company to broker-dealer and independent Alternative Trading System CODA Markets (formerly PDQ ATS), and former CEO of GETCO LLC. Eventus CEO Travis Schwab said: “We are truly fortunate to add three of the best minds in the industry to our Board as we continue growing our presence in the marketplace and helping our clients solve some of the most vexing challenges in trade surveillance, compliance and risk management. The expansion of our Board with outside directors is a natural step for us as we mature as an organization, but we are especially pleased to benefit from the strategic counsel of these talented, highly experienced professionals – Kim with her outstanding futures, clearing and risk management background; Fred with his derivatives regulatory and energy market expertise, and Keith with his incredible grounding in equities and market structure.” 2018-06-28: Read the full story. CloudQuant Thoughts:  CloudQuant’s parent company uses Eventus technology. We are proud to be an early adopter of their Regtech tools.  

Why your machine-learning team needs better feature-engineering skills

The skill of feature engineering — crafting data features optimized for machine learning — is as old as data science itself. But it’s a skill I’ve noticed is becoming more and more neglected. The high demand for machine learning has produced a large pool of data scientists who have developed expertise in tools and algorithms but lack the experience and industry-specific domain knowledge that feature engineering requires. And they are trying to compensate for that with better tools and algorithms. However, algorithms are now a commodity and don’t generate corporate IP. 2018-07-21 00:00:00 Read the full story. CloudQuant Thoughts:  We have observed this when it comes to stock trading. Simply throwing numbers at an ML algo will not work. Formatting those numbers and normalizing them is better but still will not work. What is needed is knowledge of the “context of the data”. And with model production, having a deep knowledge of the context can save you lots of time and CPU cycles.  

Microsoft adds AI and IoT cautionary language to its earnings

Microsoft reported its Q4 2018 earnings yesterday, with highlights like surpassing $100 billion in revenue for the fiscal year, all three operating groups seeing double-digit year-over-year growth, and as a result the stock soaring past $800 billion in value. All of that meant a smaller tidbit slipped through: three additions and three minor changes made to the earnings release. The Forward-Looking Statements section of the release has had the same boilerplate for years:
  • Statements in this release that are “forward-looking statements” are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could differ materially because of factors such as:
This is then followed by 24 factors. In this past quarter’s release, there were 27 factors. Here are the three new ones:
  1. the development of the internet of things presenting security, privacy, and execution risks;
  2. issues about the use of artificial intelligence in our offerings that may result in competitive harm, legal liability, or reputational harm; and
  3. damage to our reputation or our brands that may harm our business and operating results.
2018-07-20 00:00:00 Read the full story. CloudQuant Thoughts: Interesting additions to Microsofts Forward-Looking Statements.  

How The Tech Community Is Leading The War Against Development Of Lethal Autonomous Weapons

Of late, there have been many arguments against killer robots and lethal autonomous weapons but none have been more potent than the recent news about more than 2,400 technology leaders calling for an open ban on the development of lethal autonomous weapons. One of the biggest voices in this debate has been Tesla’s Elon Musk, who has rallied against the use of killer robots. In a recent conference in Stockholm more than 2,400 individuals and 150 companies from 90 different countries vowed to play no part in the construction, trade, or use of autonomous weapons in a pledge signed on Wednesday at the 2018 International Joint Conference on Artificial Intelligence in Sweden. Max Tegmark, president of the Future of Life Institute and one of the supporters of the ban against development of LAWS, “AI has huge potential to help the world — if we stigmatise and prevent its abuse. AI weapons that autonomously decide to kill people are as disgusting and destabilising as bioweapons, and should be dealt with in the same way”. According to a statement released by the body, the decision to take a human life should never be delegated to a machine since LAWS engaging targets without human intervention – would be dangerously destabilising for every country and individual. 2018-07-20 13:23:37+00:00 Read the full story.

Elon Musk, Google’s DeepMind co-founders and others promise never to make killer robots

Tesla and SpaceX billionaire Elon Musk and all three of the co-founders of Google’s DeepMind are among the thousands of individuals and almost 200 organizations who have publicly committed not to develop, manufacture or use killer robots. “We the undersigned agree that the decision to take a human life should never be delegated to a machine,” reads the pledge published Wednesday and organized by the Boston nonprofit Future of Life, an organization that researches the benefits and risks of artificial intelligence along with other existential issues related to advancing technology. 2018-07-20 00:00:00 Read the full story (CNBC). 2018-07-18 00:00:00 Read the full story (The Verge). CloudQuant Thoughts: In these last two stories the leaders in AI are starting to take the steps that their employees have been calling for in recent months.
Below the Fold…

Top 20 Python AI and Machine Learning Open Source Projects

Getting into Machine Learning and AI is not an easy task. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial that we keep up with the pace of this rapid development. In order to cope with this overwhelming speed of evolution and innovation, a good way to stay updated and knowledgeable on the advances of ML, is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.
  1. TensorFlow
  2. Scikit-learn
  3. Keras
  4. PyTorch
  5. Theano
  6. Gensim
  7. Caffe
  8. Chainer
  9. Statsmodels
  10. Shogun
  11. Pylearn2
  12. NuPIC
  13. Neon
  14. Nilearn
  15. Orange3
  16. Pymc
  17. Deap
  18. Annoy
  19. PyBrain
  20. Fuel
2018-07-23 12:00:00+00:00 Read the full story.  

Anaconda adds GPU-Accelerated Machine Learning Capabilities in Latest Update

Anaconda, a Python data science platform provider, is releasing Anaconda Enterprise 5.2, adding NVIDIA GPU-accelerated, scalable machine learning and more. “The purpose is to help the data scientist be productive and at the same time give the IT folks the control and automation they need to be successful with large scale deployments, machine learning, and artificial intelligence,” said Mathew Lodge, SVP products and marketing, Anaconda. The company worked with NVIDIA to build out support for running the platform at scale on cloud native infrastructure, Lodge explained. 2018-07-17 00:00:00 Read the full story.  

VIDEO: What’s the Difference between Cognitive Computing and AI?

At Data Summit 2018, Hadley Reynolds, co-founder of the Cognitive Computing Consortium, presented a keynote looking at the meaning of AI and cognitive computing. In particular, he considered what each of the terms AI and cognitive computing really mean and how they differ. By 2016, IBM was starting to use the term AI, and in 2017, IBM “really” used the term, and so now everybody is talking about AI, said Reynolds. Given this, he noted, some might wonder why the consortium is still talking about cognitive computing. “This is what we propose from the standpoint of our conversation,” said Reynolds: “That the fundamental differentiation is the extent to which the machine can emulate human thought processes, behaviors, and interactions.” 2018-07-17 00:00:00 Read the full story.  

Why “data for good” lacks precision. – Towards Data Science

Let’s start by asking what do we mean by “data”? I will constrain the scope of our discussion by defining “data” as referring to a project that extracts information from an existing dataset or involves the collection of new/additional data. This often entails data collection, cleaning and/or the application of statistical tools and/or machine learning models. This work can also involve building technical tools for data collection or model deployment. “Data for good” refers to a subset of data projects. “Data for good” is an odd descriptor because it implies that some data is not being used for good or is at least ambivalent in the nature of its application. The subjective nature of the word “good” as a qualifier means that there may be multiple valid definitions used at the same time. I have frequently seen four criteria used to qualify a project as falling under the “data for good” umbrella:
  1. The end recipient of the data product is a non-profit or government agency.
  2. Skilled volunteer/s develop and deliver the data product.
  3. Data tools are provided to the organization/individual for free or at a heavily subsidized amount.
  4. Educational training to improve the data skills of an underserved community
2018-07-22 07:53:09.013000+00:00 Read the full story.  

Plotting decision boundaries in 3D — Logistic regression and XGBoost

Though there’re already quite a few learning resources out there, I believe a nice interactive 3D plot will definitely help the readers gain intuition for ML models. Here I pick two models for analysis: Logistic regression, which is easy to train and deploy, and it’s commonly used in many areas; XGBoost, one of the leading ML algorithms from the gradient boosting tree family (Gradient boosting, LightGBM, etc.). 2018-07-22 19:51:12.836000+00:00 Read the full story.  

Machine Learning: A Micro Primer with a Lawyer’s Perspective

What Is Machine Learning? I am partial towards this definition by Nvidia: “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” The first step to understanding machine learning is understanding what kinds of problems it intends to solve, based on the foregoing definition. It is principally concerned with mapping data to mathematical models — allowing us to make inferences (predictions) about measurable phenomena in the world. From the machine learning model’s predictions, we can then make rational, informed decisions with increased empirical certainty. 2018-07-19 14:00:18+00:00 Read the full story.  

Corvil Taps AI For ’12 Steps of Big Data Grief’

Corvil, which provides data analytics for electronic trading, is using machine learning and artificial intelligence to help clients make sense of huge volumes of data. David Murray, chief business development officer at Corvil, told Markets Media that market participants are looking for help in using data in three areas – client intelligence, measuring execution quality, and venue analysis. They find it difficult to analyse huge amounts of data and determine which signals are most important. Murray told Markets Media: “Clients are struggling with what we call the ’12 steps of Big Data grief’ while Corvil has rich data covering the order lifecycle. The Intelligence Hub has been developed over three years to provide analytics that can be used easily in electronic trading.” 2018-07-19 11:46:31-04:00 Read the full story.  

Why The Best Way To Prepare For AI Is By Studying Past Technological Revolutions

Scientific and technological advancements have always had significant impacts on human lives over the course of history. Artificial intelligence, as a major technological force, has already started exhibiting its impact at a global level. However, this is just the beginning. The impending impact is going to be more deep-rooted, more disruptive and more transformative than we have witnessed in history. Every aspect of human civilisation is going to get affected and eventually transformed — be it academia, corporations, social institutions or individuals. 2018-07-19 12:01:08+00:00 Read the full story.  

Deep Learning and Artificial Intelligence

Artificial intelligence (AI) is in the midst of an undeniable surge in popularity, and enterprises are becoming particularly interested in a form of AI known as deep learning. According to Gartner, AI will likely generate $1.2 trillion in business value for enterprises in 2018, 70 percent more than last year. “AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs),” said John-David Lovelock, research vice president at Gartner. 2018-07-20 09:22:38+00:00 Read the full story.  

Can AI help brewers predict how new beer varieties will taste? Carlsberg says “probably”

Carlsberg now is leading the way in bringing artificial intelligence (AI) to one of the world’s oldest industries. The Beer Fingerprinting Project will help researchers at Carlsberg, the fourth-largest brewing company in the world with 140 beverage brands in 150 countries, use advanced sensors and analytics to more quickly map out and predict flavors. And it’s all aided by a move to the cloud to help speed along the company’s “Sail’22” growth strategy and better contend with increased competitive pressure. 2018-07-16 09:00:54-07:00 Read the full story.  

AI-assisted art moves from pixels to paintbrushes

Artificial intelligence (AI) excels at empirical tasks like mapping the brain’s neurons, predicting Alzheimer’s disease, and tracking sleep patterns. But it’s not half bad at the humanities, either. This week, ten winners of the third annual international RobotArt competition, which tasks contestants with designing artistically inclined AI, were selected from more than 100 submissions entered by 19 teams. Each work of art was voted on publicly and judged by a panel of artists, technologists, and critics on how well the team adhered to the spirit of the competition: “creating something beautiful using a physical brush and robotics and [sharing] what they learned with others.” 2018-07-20 00:00:00 Read the full story.  

Top 10 Free Books And Resources For Learning TensorFlow

TensorFlow, the open source software library developed by the Google Brain team, is a framework for building deep learning neural networks. It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe. In deep learning models, which rely on a lot of data and computing resources, TensorFlow is used significantly. Given its flexible architecture for easy deployment on various platforms such as CPUs, GPUs and TPUs, TensorFlow remains one of the favourite libraries to get into ML. Its huge popularity also means that tech enthusiasts are on a constant lookout to learn more and work more with this library. While there are many tutorials, books, projects, videos, white papers, and other resources available, we bring you these 10 free resources to get started with TensorFlow and get your concepts clear.
  1. Tutorial By TensorFlow (Website)
  2. TensorFlow White Paper (Paper)
  3. Stanford Course On Tensorflow For Deep Learning Research (PPT)
  4. First Contact With TensorFlow Get Started With Deep Learning Programming By Jordi Torres (EBook)
  5. Getting Started With TensorFlow By Giancarlo Zaccone (EBook)
  6. Learning TensorFlow By Itay Lieder, Tom Hope, Yehezkel S. Resheff (Ebook)
  7. TensorFlow Tutorial By Bharath Ramsundar (Slides)
  8. Deep Learning With TensorFlow By Cognitive Class (Online Course)
  9. TensorFlow A System For Large-Scale Machine Learning (Paper)
  10. Free Resources On Github
2018-07-20 08:27:08+00:00 Read the full story.  

IBM’s AI watermarking method protects models from theft and sabotage

What if machine learning models, much like photographs, movies, music, and manuscripts, could be watermarked nearly imperceptibly to denote ownership, stop intellectual property thieves in their tracks, and prevent attackers from compromising their integrity? Thanks to IBM’s new patent-pending process, they can be. Marc Ph. Stoecklin, manager of cognitive cybersecurity intelligence at IBM : “For the first time, we have a [robust] way to prove that someone has stolen a model,” Stoecklin said. “Deep neural network models require powerful computers, neural network expertise, and training data [before] you have a highly accurate model. They’re hard to build, and so they’re prone to being stolen. Anything of value is going to be targeted, including neural networks.” 2018-07-20 00:00:00 Read the full story.  

More than half of hedge funds now using AI technology

Research services provider BarclayHedge found that hedge funds are now leaning towards AI technology for the investment process. Fifty-eight percent of hedge funds are now claiming it has been used for more than three years. Despite 56% of respondents using AI and machine learning to inform investment decisions and 67% using it to generate trading ideas, the survey suggests that hedge funds are not quite ready to allow AI or machine learning to execute trades on their behalf. 2018-07-19 19:18:02+00:00 Read the full story.  

Intro to Machine Learning for Finance (Part 1) — Alpaca Blog

There has been increasing talk in recent years about the application of machine learning for financial modeling and prediction. But is the hype justified? Is machine learning worth investing time and resources into mastering? This series will be covering some of the design decisions and challenges to creating and training neural networks for use in finance, from simple predictive models to the use of ML to create specialised trading indicators and statistics — with example code and models along the way. 2018-07-20 00:00:00 Read the full story.  

Every AI startup is not an AI startup – Hacker Noon

Take 100 startups and ask them “Who is an AI startup?” I am confident the majority will say they are or at least will attach AI to their narrative. Here is the crucial difference — AI systems are becoming more intelligent through time and getting smarter by “consuming” and analyzing more data (It’s is like a kid becoming more intelligent and smart during several years as the kid is studying new things at school). 2018-07-23 11:31:01.604000+00:00 Read the full story.  

3 basic approaches in Bag of Words which are better than Word Embeddings

In the-state-of-art of the NLP field, Embedding is the success way to resolve text related problem and outperform Bag of Words (BoW). Indeed, BoW introduced limitations such as large feature dimension, sparse representation etc. For word embedding, you may check out my previous post. Should we still use BoW? 2018-07-22 13:04:18.552000+00:00 Read the full story.  

Doing Good Data Science

The hard thing about being an ethical data scientist isn’t understanding ethics. It’s the junction between ethical ideas and practice. It’s doing good data science. There has been a lot of healthy discussion about data ethics lately. We want to be clear: that discussion is good, and necessary. But it’s also not the biggest problem we face. We already have good standards for data ethics. The ACM’s code of ethics, which dates back to 1993, is clear, concise, and surprisingly forward-thinking; 25 years later, it’s a great start for anyone thinking about ethics. The American Statistical Association has a good set of ethical guidelines for working with data. So, we’re not working in a vacuum. 2018-07-19 15:28:46+00:00 Read the full story.  

Oracle Study Finds 93% of People Would Trust Orders from a Robot at Work

People are ready to take instructions from robots at work according to a new Oracle study. However, the survey of 1,320 U.S. HR leaders and employees revealed that while people are ready to embrace artificial intelligence (AI) at work, and understand that the benefits go far beyond automating manual processes, organizations are not doing enough to help their employees embrace AI and that will result in reduced productivity, skillset obsolescence and job loss. The study, titled “AI at Work,” identified a large gap between the way people are using AI at home and at work. While 70% of people are using some form of AI in their personal life, only 6% of HR professionals are actively deploying AI and only 24% of employees are currently using some form of AI at work. To determine why there is such a gap in AI adoption when people are clearly ready to embrace AI at work (93% would trust orders from a robot), the study examined HR leader and employee perceptions of the benefits of AI, the obstacles preventing AI adoption and the business consequences of not embracing AI. All respondents agreed that AI will have a positive impact on their organizations and when asked about the biggest benefit of AI, HR leaders and employees both said increased productivity. 2018-07-18 00:00:00 Read the full story.  

The World is Changing Fast. Worry About It Or Profit From It

I discovered something interesting today. 15 years ago, on this exact date, I updated my bank passbook for the last time. For the young ones among us, a bank passbook is a small booklet that banks used to issue to all its account holders. Inside, the passbook would list the amount of cash you have in your bank account. Oh, I remember those days. My mom will bug me to keep my passbook updated to make sure the correct amount of cash was reflected. To do that, we would have to go down to a bank branch, stand a queue, and get a bank teller to update our booklets for us. It would be years later before automated machines took over that menial task. We’re a long way from that today. 2018-07-21 00:00:00 Read the full story.  

Safety over privacy? RealNetworks to offer free facial recognition technology to K-12 schools

RealNetworks, the Seattle company best known for pioneering streaming media in the early days of the web, is deploying a surprising new product today. The company says it will offer a new facial recognition technology, called SAFR, for free to K-12 schools to help upgrade their on-site security systems. SAFR can be used with the same cameras that traditional surveillance systems to recognize students, staff, and people visiting schools. RealNetworks says that in addition to security, the tool can also help with record-keeping and “campus monitoring.” 2018-07-17 06:50:00-07:00 Read the full story.  

Introducing a simple and intuitive Python API for UCI machine learning repository

UCI machine learning dataset repository is something of a legend in the field of machine learning pedagogy. It is a ‘go-to-shop’ for beginners and advanced learners alike. It is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited “papers” in all of computer science. That said, navigating the portal can be bit frustrating and time consuming as there is no simple intuitive API or download link for the data set you are interested in. You have to hop around multiple pages to go to the raw data set page that you are looking for. Also, if you are interested in particular type of ML task (regression or classification for example) and want to download all datasets corresponding to that task, there is no simple command to accomplish such. I am glad to introduce a simple and intuitive API for UCI ML portal, where users can easily look up a data set description, search for a particular data set they are interested, and even download datasets categorized by size or machine learning task. 2018-07-20 13:59:07.032000+00:00 Read the full story.  

Evolution of a salesman: A complete genetic algorithm tutorial for Python

Drawing inspiration from natural selection, genetic algorithms (GA) are a fascinating approach to solving search and optimization problems. While much has been written about GA (see: here and here), little has been done to show a step-by-step implementation of a GA in Python for more sophisticated problems. That’s where this tutorial comes in! Follow along and, by the end, you’ll have a complete understanding of how to deploy a GA from scratch. In this tutorial, we’ll be using a GA to find a solution to the traveling salesman problem (TSP). The TSP is described as follows: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” 2018-07-20 13:59:07.032000+00:00 Read the full story.  

Data Science for Startups: Model Services

In order for data scientists to be effective at a startup, they need to be able to build services that other teams can use, or that products can use directly. For example, instead of just defining a model for predicting user churn, a data scientist should be able to set up an endpoint that provides a real-time prediction for the likelihood of a player to churn. Essentially, the goal is to provide a model as a service, or function call that products can use directly. This type of capability, providing model predictions with sub-millisecond latency, can be categorized as providing models as a service. AWS lambda provides a great way of implementing these capabilities, but does require some set up to get working with common ML libraries. The goal of this post is to show how to use AWS lambda to set up an endpoint that can provide model predictions. 2018-07-20 13:59:07.032000+00:00 Read the full story.  

What it’s like when SoftBank founder Masa Son wants to invest over $100 million into your company

At center, Masayoshi (Masa) Son chief executive officer of SoftBank, attends the annual Allen & Company Sun Valley Conference, July 11, 2018 in Sun Valley, Idaho. Drew Angerer/Getty Images SoftBank’s Vision Fund, a $100 billion behemoth that is upending the insular world of technology investing, doesn’t operate like other venture funds. Sure, they want your pitch, but what SoftBank founder Masa Son is really looking for is futuristic technology … 2018-07-21 00:00:00 Read the full story (Business Insider – Subscription Required).  

A new study shows that tech CEOs are optimistic about the future, even if they still don’t understand millennials

several top tech firms that have been betting on and investing in artificial-intelligence technology. Richard Drew/AP Tech industry CEOs are bullish on the future of their companies, the sector, and artificial intelligence. But they’re worried about the spread of nationalism, cybersecurity — and millennials. Those are some of the key takeaways from a new report by KPMG. After surveying more than 1,000 CEOs from all different sectors and from around the globe, the company zeroed in on the responses of 104 from the tech industry. Compared with their non-tech peers, tech CEOs were more optimistic about their firms’… 2018-07-22 00:00:00 Read the full story (Business Insider – Requires subscription).  
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 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. 16, July 2018

Researchers use AI and accelerometer data to predict heart rate while saving battery life

What do the Apple Watch and Nokia Pulse Ox have in common? They’ve both got pulse oximeter sensors that measure heart rate using photoplethysmography (PPG), the expansion and contraction of capillaries based on changes in blood volume. They’re accurate to a degree, but require a fair amount of electricity because they’re light-based — they emit a signal onto the skin that reflects back to a photodiode. One battery-saving alternative might be accelerometers, a sensor commonly found in smartphones, smartwatches, and activity trackers that measures non-gravitational acceleration. In a paper published on the preprint server, researchers at Philips Health and the University of Bristol describe a machine learning algorithm that can predict heart rate almost exclusively from the sensors, boosting the battery life of the wearable to which they’re attached. 2018-07-13 00:00:00 Read the full story. CloudQuant Thoughts… More great applications of AI. A simple low power Accelerometer plus a little AI can detect if you seem in distress then, and only then, switching on the battery hogging PPG on your smartwatch.  

Alexa alternatives have a secret weapon: Privacy

Earlier this week we learned that worldwide smart speaker sales are expected to increase sixfold within the next couple of years. This mirrors multiple studies that say the majority of U.S. households will have a smart speaker by 2022, powered by current leading intelligent assistants Google Assistant and Alexa. At the same time, tech giants making intelligent assistants seem to want to have it both ways, selling products to both consumers and governments. For example, Microsoft, maker of Cortana, may be supplying facial recognition software to ICE, the government agency tasked with capturing and detaining immigrants who are in the United States illegally. That’s why startups like Snips, which is bringing its own smart speaker to market, center their attention on one primary differentiator: privacy. 2018-07-14 00:00:00 Read the full story. CloudQuant Thoughts… Alexa listening to your every word and emailing clips to random people on your email list. As a child I wanted to build my own Star Trek “computer” to talk to, heck, if they allowed me to program the trigger word myself I probably would buy one of these things (Note: Alexa does allow you to change it to one of 4 pre-selected words, one of which is “computer”!). I have seen amazing setups for home automation. A friend was going to be alone at home for two months and I recommended Alexa controlled light fittings as she already had Alexa’s in her home.. why not, it’s so easy!  

How Can we Ensure that Big Data Does not Make us Prisoners of Technology?

Most of you will have interacted with several algorithms already today. In some cases, more algorithms than people. Algorithms are of course simply sets of rules for solving problems, and existed long before computers. But algorithms are now everywhere in digital services. An algorithm decided the results of your internet searches today. If you used Google Maps to get here, an algorithm proposed your route. Algorithms decided the news you read on your news feed and the ads you saw. Three factors could come together to make an algocracy more than just science fiction:
  1. Big Data, artificial intelligence and machine learning, behavioural science.
  2. The power of Big Data corporations and their central place in providing services that are now essential in our everyday lives raise significant questions about the adequacy of global frameworks for competition and regulation.
  3. We need to anticipate the fundamental questions which Big Data, artificial intelligence and behavioral science present, and make sure that we innovate ethically to shape the answers.
2018-07-13 10:29:48+00:00 Read the full story. CloudQuant Thoughts… Is it fair that Insurers use your eating and drinking records to set the price of your health insurance? Is it fair for credit card companies to limit your credit if you make a payment to a marriage guidance counselor? We should really have dealt with these questions before allowing corporate giants to run rampant with our data. Privacy must be on by default, the release of data must be opt-in only and must include regular clear weekly summaries of who the data was given to and why. With these simple rules in place, we would have avoided the FB Russia issue.  

Facial recognition technology: The need for public regulation and corporate responsibility

All tools can be used for good or ill. Even a broom can be used to sweep the floor or hit someone over the head. The more powerful the tool, the greater the benefit or damage it can cause. The last few months have brought this into stark relief when it comes to computer-assisted facial recognition – the ability of a computer to recognize people’s faces from a photo or through a camera. This technology can catalog your photos, help reunite families or potentially be misused and abused by private companies and public authorities alike. 2018-07-13 00:00:00 Read the full story. CloudQuant Thoughts… So many stories this week on privacy and security around AI and ML. This is an interesting, detailed and thus long BlogPost by Microsoft in reaction to their employees’ response to Microsoft’s Computer Vision technology being used by ICE. But as people freak out about computer vision and the ability of government to watch you wherever you go, they ignore the fact that you do not need complicated AI and ML to achieve this. We all now carry tracking devices in our pockets.  

Algorithms are taking over – and woe betide anyone they class as a ‘deadbeat’

The radical geographer and equality evangelist Danny Dorling tried to explain to me once why an algorithm could be bad for social justice. Imagine if email inboxes became intelligent: your messages would be prioritised on arrival, so if the recipient knew you and often replied to you, you’d go to the top; I said that was fine. That’s how it works already. If they knew you and never replied, you’d go to the bottom, he continued. I said that was fair – it would teach me to stop annoying that person. If you were a stranger, but typically other people replied to you very quickly – let’s say you were Barack Obama – you’d sail right to the top. That seemed reasonable. And if you were a stranger who others usually ignored, you’d fall off the face of the earth. “Well, maybe they should get an allotment and stop emailing people,” I said. “Imagine how angry those people would be,” Dorling said. “They already feel invisible and they [would] become invisible by design.” 2018-07-12 00:00:00 Read the full story. CloudQuant Thought… So many negative articles this week. Are we running before we can even walk?  

Amazon AI predicts users’ musical tastes based on playback duration

Engineers at Amazon have developed a novel way to learn users’ musical tastes and affinities with artificial intelligence: by using song duration as an “implicit recommendation system.” Bo Xiao, a machine learning scientist and lead author on the research, today described the method in a blog post ahead of a presentation at the Interspeech 2018 conference in Hyderabad, India. Distinguishing between two similar songs — for instance, Lionel’s Richie’s “Hello” and Adele’s “Hello — can be a real challenge for voice assistants like Alexa. One way to resolve this is by having the assistant always choose the song that the user is expected to enjoy more, but as Xiao notes, that’s easier said than done. Users don’t often rate songs played back through Alexa and other voice assistants, and playback records don’t necessarily provide insight into musical taste. 2018-07-14 00:00:00 Read the full story. CloudQuant Thoughts… You do not need AI to do this, you can do it with iTunes. It is easy to detect how many times you have pressed skip on a song compared to how many times you have not. It would be very simple for Apple and their ilk to add hard-coded logic to determine what we will like and what we will not like. But do we want to, we seem to be in a world of echo chambers. Listen to different things, go to different places, read different books/opinions. If you do the same as everyone else you are part of the herd. Writing profitable models requires the ability to step back from the herd and observe.  

Japan’s Paidy raises $55m for credit card-free online shopping service

Paidy launched its post-pay credit account for ecommerce in 2014, and now claims more than 1.4 million accounts, bringing online shopping to people who do not have, or do not want to use, credit cards. Once registered, customers make purchases using a mobile phone number and email address with a four digit SMS or voice verification code, before settling a single monthly bill for all their purchases, either at a convenience store, by bank transfer or auto debit. The firm says that its proprietary models and machine learning mean that transactions are underwritten in seconds, with guaranteed payment to merchants – increasing their revenues. 2018-07-12 15:41:00 Read the full story. CloudQuant Thoughts… Many of us do not think about those who are excluded from this brave new world of online shopping and easy fast purchases but there are significant numbers of people in the western world who are unable to gain credit. But where does that leave those that Paidy’s algorithm rejects? To quote from the “Algorithms are taking over article” above… “The Chinese government is working towards assigning its citizens a social score by 2020, an algorithm will rate citizens as a desirable employee, reliable tenant, valuable customer – or a deadbeat, shirker, menace, and waste of time”. If you want to see an entertaining dystopian view of this version of the future I would suggest Black Mirror, Season 3 Episode 1 “Nosedive”.  

Goldman Sachs Targets 25% Upside for Twitter Stock

Shares of Twitter Inc (NYSE:TWTR) are up 2.6% to trade at $44.95, after Goldman Sachs lifted its price target on the social media stock to $55 from $40 — a more than 25% premium to last night’s close at $43.87. The brokerage firm said the company’s efforts to delete fake accounts are “contributing to incremental ad dollars flowing onto the platform.” 2018-07-12 00:00:00 Read the full story. CloudQuant Thoughts… Twitter’s high was $74.73 in December 2013, its low $13.725 in May 2016. Can you write a model to predict these kind of movements, perhaps using News reports, perhaps using Sentiment data. Try now on But I would not put much sway in Goldman Sachs’ predictions because…  

How Goldman Sachs Lost the World Cup

Goldman Sachs’ statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn’t predict the final opposing France and Croatia on Sunday. 2018-07-14 00:00:00 Read the full story. CloudQuant Thoughts… I’m sorry, I watched this disaster unfold, it was hilarious!  
Below the Fold  

Traditional statistical methods often out-perform machine learning methods for time-series forecasts

It is impossible today to sit in a meeting in an analytics environment and discuss a methodological approach to a problem without a machine learning (ML) based solution being suggested. There is merit to this; ML techniques from SVM, CART regression trees, to the suite of neural networks (BNN, RNN, LSTM) offer superior predictive capabilities. When turning this predictive capability to time-series forecasting, it would be natural to think these ML algorithms should be the first choice. Well, perhaps not. A recent paper from 3 forecasting experts at the National Technical University of Athens would suggest otherwise; that when it comes to time-series forecasting, traditional statistical techniques such as ARIMA or ETS may in fact offer superior forecasting performance. 2018-07-09 Read the full story.  

AI drug discovery startup Verge Genomics raises $32 million

Developing new drugs is a challenging enterprise. It costs pharmaceutical companies an average of $2.7 billion to bring medicine to store shelves, according to the Tufts Center for the Study of Drug Development, and as much as 90 percent of treatments in late-stage trials never come to market because they’re deemed ineffective or unsafe. Verge Genomics, run by 29-year-old Alice Zhang, is trying to address these problems by making drug discovery faster and cheaper. “Drug companies are looking at one gene at a time. That works for certain diseases, but more complex ones can be caused by hundreds of genes,” she said. “They [also] aren’t typically using human data until they get into clinical trials. We use that data from day one … [those are] some of the ways we’re decreasing the [drug] failure rate.” 2018-07-16 00:00:00 Read the full story (at Venture Beat). 2018-07-16 00:00:00 Read the full story (at Business Insider).  

A Gentle Introduction to Credit Risk Modeling with Data Science — Part 2

In our last post, we started using Data Science for Credit Risk Modeling by analyzing loan data from Lending Club. We’ve raised some possible indications that the loan grades assigned by Lending Club are not as optimal as possible. Over the next posts, our objective will be using Machine Learning to beat those loan grades. 2018-07-15 17:37:51.531000+00:00 Read the full story.  

How Cellular Features Improve ML Accuracy In Phenotyping

Research and discoveries in cell biology have come a long way. Improvements in biological equipments, especially in the area of microscopy, have risen to a cutting-edge level. The precision in obtaining images on a microscopic scale is astonishing. These advances have now presented a challenge of obtaining a vast amount of image data in crispy-clear quality. Although machine learning (ML) has resolved this problem with quick efficacy by using automation, it fails to utilise information from microscopic elements such as cells and tissues. ML only considers the properties or features surrounding the data. It does not dig in deep about the cellular features that determine or influence the extrinsic (environmental) factors have on humans. 2018-07-16 05:44:17+00:00 Read the full story.  

Top 10 Kaggle Data Notes of the last week…

  1. S&P 500 Simple Forecasting with Prophet  (Facebook’s library for time series forecasting)
  2. arXiv Data Analysis: Computation and Language Papers
  3. Dysonian SETI with Machine Learning
  4. Content-based Recommender Using Text Mining
  5. Predicting Cast in TV’s Frasier Based on Dialog
  6. Shift in Votes Between Political Parties
  7.  Data Science Glossary on Kaggle
  8. Kaggle Dataset #1: Stanford Cars Dataset
  9. Kaggle Dataset #2: Columbia Object Image Library
  10. Kaggle Dataset #3: CelebFaces Attributes Dataset
2018-07-12 00:00:00 Read the full story.  

Particle Swarm Optimisation in Machine Learning – Towards Data Science

“Gradient Descent will not make you an expert at Machine Learning” Most of the articles you would have come across must have talked about Gradient Descent whether it is a Simple Linear Regression or Neural Networks. In this article I will introduce a technique i.e. Particle Swarm Optimisation (PSO) to you. No doubt that Gradient Descent is a good optimisation technique which works great for convex functions & low dimensional space but you can expect bizarrely good results with PSO. 2018-07-15 14:51:52.071000+00:00 Read the full story.  

New dog, old tricks: Fintec data management in the cloud

Every cloud has a silver lining, at least that’s what our elders drummed into us, an early example of expectation management and how to deal with life’s challenges. Other teaching proxies included looking after the family dog, a somewhat more practical and physical learning experience, involving across the board responsibilities of duty of care, maintenance and welfare for another living being. Cloud computing for data management offers similar canine inspired opportunities for curating what can turn out to be a beast or a docile pet in equal measure. So let’s run through the many considerations of responsible cloud ownership and their interpretation 2018-07-16 00:00:00 Read the full story.  

AI has arrived – don’t leave data strategy behind

Over the last century computers have sped up exponentially, according to Moore’s law, whilst their production costs have halved every 18 months. That pace is increasing even more with quantum computers that are already running at 50 qubits. By comparison, a 100 qubit computer could theoretically be more powerful than all the supercomputers in the world combined. That’s a lot of computing power. Meanwhile, the amount of data produced in the world is advancing at a staggering pace. Today we are generating 16.3 zetabytes of data per year and that number is set to grow to 163 zetabytes by 2025, according to IDC. That’s a 10-fold increase in just seven years. For this reason, Gartner bills AI as the most disruptive technology to emerge over the next decade, because it has the power to process vast pools of data and turn them into critical insights that enhance lives. Although AI has been around since the 1960s, progress has been slow – mainly due to the lack of data and poor computer power. Both are now available in abundance. 2018-07-13 00:00:00 Read the full story.  

Oval Money to link automated savings app to Twitter postings

Oval Money, the automated savings platform, has enabled users to double their monthly savings by tapping into their social media habits. The average UK user was putting away just £63 per month on the platform when it launched 12 months ago. In just a year, the figure has leapt to £130, equivalent to an annual jump from £756 to £1,560. The firm has attributed the increase in part to users linking the app with their Facebook activity, so they made automated savings every time they posted. 2018-07-16 10:13:00 Read the full story.  

Fidelity’s Welo: Forget Tech; AI to Have Greater Impact on Industrials

Technology companies may believe that they have cornered the market for artificial intelligence, but investors may want to set their sights toward industrial companies to get some AI exposure. …Incorporating AI into industrial companies’ processes will offset higher inflation, notably wage inflation. After all, downtime is extremely costly for industrial companies, and if AI-enabled sensors and software can run some processes more efficiently, it could help companies minimize this issue. The fund manager pointed to a gas turbine as one example. If a company can use AI to anticipate a potential failure, that could bring a massive benefit to that business. 2018-07-12 12:06:00-06:00 Read the full story.  

Intel Adds Structured ASICs to Product Lineup With eASIC Acquisition

Intel over the past several years has adapted to an increasingly data-centric world by diversifying its product lines beyond CPUs for PCs and servers to offer a broader range of computing systems including programmable chips, ASICs and, soon discrete GPUs. “Customers designing for high-performance, power-constrained applications in market segments like wireless, networking and the internet of things (IoT) sometimes begin deployments with FPGAs for fast time-to-market and flexibility,” McNamara wrote in a post on the company blog. “They then migrate to devices called structured ASICs, which can be used to optimize performance and power-efficiency. A structured ASIC is an intermediary technology between FPGAs and ASICs. … The addition of eASIC will help us meet customers’ diverse needs of time-to-market, features, performance, cost, power and product life cycles.” 2018-07-12 00:00:00 Read the full story.  

Banks stand to reap $512 billion revenue boost from ‘intelligent automation’

With banks the world over exploring the business case for AI and robotic automation, a new report estimates that hundreds of billions of dollars in additional revenue may be up for grabs as the focus moves away from costs savings to income generation. To date, automation technologies, such as RPA (Robotic Process Automation), have been implemented by the financial services industry to drive down costs and create efficiencies. But a new front is opening, with the deployment of machine learning tools for customer-facing interactions seen as new weapon in the armoury of banks facing the threat of competition from Big Tech players like Amazon and Alphabet. 2018-07-12 09:32:00 Read the full story.  

BattleFin Announces Pending Launch of Alt Data Assessment & Testing Platform

The Ensemble platform allows hedge funds and investment firms to test alternative data sets that they found at the BattleFin Alternative Data Discovery Days or that are part of the BattleFin Alternative Data Accelerator. Standardized NDAs, Testing Agreements, & Compliance checks will be completed once. One interesting feature of the Ensemble platform is that fundamental asset managers looking to add alternative data to their research process will be able to leverage BattleFin’s data scientist community by scoping out projects to track and predict specific Key Performance Indicators using specific alternative data sets. 2018-07-12 08:36:10+00:00 Read the full story.  

Finra Taps AI to Stop ‘Mini Manipulation’

Artificial intelligence may help market manipulators skirt the rules, but the same technology is helping regulators detect the once-hidden behavior. “The machine is learning the activity and identifying it for us as well as the changes in activity so that we can stay slightly ahead of it,” said Gene DeMaio, senior vice president, market regulation at Financial Industry Regulatory Authority, during a recent call hosted by the STA Foundation, the educational arm of the Security Traders Association. “As we train the program a little bit more as time goes by, we are getting better and better exceptions.” The technology has helped the self-regulatory organization to identify potential instances of “mini-manipulation,” which is also known as cross-asset manipulation that uses equities and options. Mini-manipulation occurs when a trader holds an options position and attempts to move the underlying equity to change the price of the equity. 2018-07-13 21:40:39-04:00 Read the full story.  

AI doctor app Babylon fails to diagnose heart attack, complaint alleges

An artificial intelligence app that claims to be able to diagnose medical conditions better than human doctors fails to properly identify heart attacks, it has been claimed. Babylon, which lets people book virtual appointments with GPs and receive prescriptions through its app, also operates a tool that uses artificial intelligence to automatically diagnose health problems. Customers can type their symptoms and receive a diagnosis through the app. 2018-07-13 00:00:00 Read the full story (PAYWALL)  

Forget the sex, the hot new book about Google is an important reminder of what Sergey and Larry are really after

A new book has generated headlines over Sergey Brin’s ‘playboy’ period during Google’s early days, but there’s another big takeaway in the book that readers in the tech industry should not ignore. The book explores a deep-rooted and intense desire that’s driven Google’s founders all these years. Brin and cofounder Larry Page stumbled onto the magic search business but that was never their main interest. From the start, Google was always intended to be an AI company. They’re now closer to that vision than they have ever been, and what comes next could make search look like a footnote in Google’s history. 2018-07-14 00:00:00 Read the full story (PAYWALL).  

22 books Wall Streeters think everyone should read this summer

Everyone needs a good beach read. We asked three Wall Streeters for the books they are having trouble putting down as the weather warms up. Along with titles about finance and successful figures, we also received recommendations for thrillers, novels, and historic non-fiction that make the list of perfect page-turners.
  • ‘Sapiens: A Brief History of Humankind’ by Yuval Noah Harari
  • ‘Bad Blood’ by John Carreyrou
  • ‘Extreme Ownership – How U.S. Navy SEALS Lead and Win’ By Jocko Willink and Leif Babin
  • ‘A Life Well Played’ by Arnold Palmer
  • ‘Lords of Finance: The Bankers Who Broke The World’ by Liaquat Ahamed
  • ‘How Not to Be Wrong: The Power of Mathematical Thinking’ by Jordan Ellenberg
  • ‘Elon Musk: Telsa, SpaceX and the Quest for a Fantastical Future’ by Ashlee Vance
  • ‘Hillbilly Elegy: A memoir of a family and culture in crisis’ by JD Vance
  • ‘Spymaster’ by Brad Thor
  • ‘The Orphan Master’s Son’ by Adam Johnson
  • ‘King Leopold’s Ghost’ by Adam Hochschild
  • ‘Prediction Machines: The Simple Economics of Artificial Intelligence’ by Ajay Agrawal, Joshua Gans, Avi Goldfarb
  • ‘The Cuban Affair’ by Nelson DeMille
  • ‘Principles’ by Ray Dalio
  • ‘Radical Candor’ by Kim Scott
  • ‘Medium Raw: A Bloody Valentine to the World of Food and the People Who Cook’By Anthony Bourdain
  • ‘The Other Woman’ by Daniel Silva
  • ‘The Hard thing about Hard Things’ by Ben Horowitz
  • ‘The Perfect Weapon’ by David E. Sanger
  • ‘The Underground Railroad’ by Colson Whitehead
  • ‘The Republic of Pirates’ by Colin Woodard
  • ‘The Soul of America’ by Jon Meacham
2018-07-16 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 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. 09, July 2018

Teaching Cars To Drive — Highway Path Planning – Udacity – C++

Term 3 of the Udacity Self-Driving Car Engineer Nanodegree. C++ Planning a path that is both safe and efficient is one of the hardest problems in the development of an autonomous vehicle. In fact, this step, known as Path Planning, is still an active area of research. The reason why Path Planning is such a complex task is because it involves all components of a self-driving vehicle, ranging from the low level actuators, the sensors which are fused to create a “snapshot” of the world, along with the localization and prediction modules to understand precisely where we are and what actions different entities in our world (other vehicles, humans, animals, etc) are more likely to take in the next few seconds. One other obvious component is a trajectory generator that can compute candidate trajectories to be evaluated by the planner. 2018-07-08 07:40:50.206000+00:00 Read the full story. CloudQuant Thoughts… These online courses are getting better and better all the time. Who would have thought that one could “learn to program a car to drive to drive itself”, let alone learn how to do it “online.  

Artificial Intelligence choosing the best bits from Wimbledon

A Watson powered artificial intelligence system is monitoring emotional outburst from top tennis players at Wimbledon to help it decide which clips make it into highlights packages. 2018-07-05 00:00:00 Read the full story. CloudQuant Thoughts… Two thoughts for this one… 1) How much did this cost and b) Is significantly better than these 22 lines of python code? “Bigger isn’t always better” is a lesson I thought IBM had learned long ago.  

HSBC Predicts Six Future Banking Jobs

HSBC predicts surprising banking jobs of the future, including ‘Mixed Reality Experience Designer’ and ‘Algorithm Mechanic’ Bank currently recruiting more than 1,000 roles to support its digital evolution globally A new report offers a glimpse into the future of a career in banking, predicting six surprising new types of jobs and how the digital revolution will evolve the role of people in the workforce. 2018-07-06 Read the full story (MarketsMedia). 2018-07-06 Read the full story (FinExtra). CloudQuant Thoughts… And they are all AI/ML/Data Scientist jobs at their heart with a few extra skills tacked on the side!  

Apple’s Carlos Guestrin cautions AI leaders to think very carefully about how they use their data

The rise of machine learning has been one of the most exciting developments in modern technology, but according to Carlos Guestrin, one of the oldest principles of computing still applies: garbage in, garbage out. 2018-07-02 15:00:42-07:00 Read the full story. CloudQuant Thoughts… If you watch no other part watch the Magic Sudoku Solver demonstration using AR /AI /ML.  

Artificial Intelligence And Machine Learning In Medicine: Hope Or Hype?

Applications of artificial intelligence and machine learning to health care and research have been exploding over the last year—and sometimes backfiring, as researchers negotiate the learning curve. There is great hope, however, that these technologies will reduce drug discovery times and ultimately enable precision medicine. Is a computational approach the awaited disruption that will speed time and lower costs of research and development? Or will human biology outsmart our best efforts to decode it algorithmically? 2018-07-07 15:59:21-04:00 Read the full story. CloudQuant Thoughts… This is a very entertaining post for two reasons.. 1) The panel conversation is very interesting. What is Watson good at? Taking in large amounts of data and working out the patterns that are easily discernable. Why may Watson struggle with medical applications? Because there is no encyclopedia, we don’t know what causes most medical diseases. 2) The obviously machine-generated transcript of the start of the presentation includes the fabulous line “So when people think about Johnson Johnson they think of them people think about baby capful spurts of Cool J has.” (The actual line was.. “So, when people think about Johnson’n’Johnson they think about baby care products but of course J&J has…..”. I guess we are not quite there yet!  

Inside China’s Dystopian Dreams: A.I., Shame and Lots of Cameras

In the Chinese city of Zhengzhou, a police officer wearing facial recognition glasses spotted a heroin smuggler at a train station. In Qingdao, a city famous for its German colonial heritage, cameras powered by artificial intelligence helped the police snatch two dozen criminal suspects in the midst of a big annual beer festival. In Wuhu, a fugitive murder suspect was identified by a camera as he bought food from a street vendor. With millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people. It wants to assemble a vast and unprecedented national surveillance system, with crucial help from its thriving technology industry. 2018-07-08 00:00:00 Read the full story. CloudQuant Thoughts… This is the dystopian future playing out in front of us. You cannot move without the state watching you. I’m freaked out when my Nest controller knows that I am walking up to it!  

Sonos, Like Fitbit Before it, Is Going Public Just as Competition Heats Up

If the smart speaker market looks much the same in three years’ time as it does today, Sonos should be in good shape. But that’s hardly a given, of course. And the numbers just shared in the connected audio hardware maker’s IPO filing suggest it has a limited margin of error as tech giants continue launching rival products. 2018-07-07 09:00:00-04:00 Read the full story. CloudQuant Thoughts… Is IoT the next industry to bubble its way to the top, why not create your own list of IoT firms and backtest them on to see how they have performed.  

The rise of ‘pseudo-AI’: how tech firms quietly use humans to do bots’ work

It’s hard to build a service powered by artificial intelligence. So hard, in fact, that some startups have worked out it’s cheaper and easier to get humans to behave like robots than it is to get machines to behave like humans. “Using a human to do the job lets you skip over a load of technical and business development challenges. It doesn’t scale, obviously, but it allows you to build something and skip the hard part early on,” said Gregory Koberger, CEO of ReadMe, who says he has come across a lot of “pseudo-AIs. It’s essentially prototyping the AI with human beings”. This practice was brought to the fore this week in a Wall Street Journal article highlighting the hundreds of third-party app developers that Google allows to access people’s inboxes. In the case of the San Jose-based company Edison Software, artificial intelligence engineers went through the personal email messages of hundreds of users – with their identities redacted – to improve a “smart replies” feature. 2018-07-06 00:00:00 Read the full story. CloudQuant Thoughts… Sometimes, as shown in the PBS NOVA Rise of the Robots program, you have a step in a process that is just beyond the capabilities of the current technology. In that instance, you make it an outside step. It helps you to define where you fall short and then take chunks out of just that part of the problem.  

Google’s DeepMind taught AI teamwork by playing Quake III Arena

Google’s DeepMind today shared the results of research and experiments in which multiple AI systems were trained to play Capture the Flag on Quake III Arena, a multiplayer first-person shooter game. An AI trained in the process is now better than most human players in the game, regardless of whether it’s playing with a human or machine teammate. The AI, named For the Win (FTW), played nearly 450,000 games of Quake III Arena to gain its dominance over human players and establish its understanding of how to effectively work with other machines and humans. DeepMind refers to the practice of training multiple independently operating agents to take collective action as multi-agent learning. 2018-07-04 00:00:00 Read the full story. CloudQuant Thoughts… The rate of improvement shown in this video is simply staggering.  

Data Privacy, Women in Data Science with Carla Gentry – Podcast

Carla Gentry is one of most popular social media influencers in the data science field. She has over 300,000 followers on LinkedIn and 48k followers on Twitter. Her experience in this field is unparalleled and we are grateful for leaders like her who consistently give back to the community. 2018-07-09 08:17:56+05:30 Read the full story. CloudQuant Thoughts: “Her advice to aspiring female data scientists was to the point – stand your ground, be confident in yourself, find mentors, keep going and keep learning. You will find the perfect fit for you as long as you continue to believe in yourself and your abilities.” Spot on! We completely agree and want to see more women in Data Science and Algorithmic Development.  
Below the fold…

User experience enhanced by artificial intelligence

I had a couple of good user experiences over the past couple of days, they may seem like small beer to some people but to me they point to a bright future… One was I wrote an email in Gmail that mentioned “… I have attached a PDF with the latest summary…”. I then hit send but forgot to attach the PDF. The response from Gmail was to prompt me to attach the PDF. This may seem trivial but it saved the recipient from having to write asking for the PDF and then saved me having to write another mail with the PDF attached. The second good UX experience was, I traveled in Spain last week and used a Monzo debit card as it saves on fees and offers a good exchange rate. Looking at the Monzo app this morning it has a message saying “Your trip to Spain – you spent £475.69 over 5 days”. 2018-07-09 11:13:35 Read the full story.  

Smart Future of Commuting

It’s now time for commuter transport to be revolutionised by technology! Afterall, pretty much everyone nowadays is mobile-savvy. Most people are constantly connected to their smartphones which are embedded with all of the necessary software for location tracking, GPS, bluetooth, voice control and more. So, here’s a glimpse into the smart future of commuting. And the best thing, it’s all entirely possible now. “Alexa, how’s my commute today?” 2018-07-05 09:18:13 Read the full story.  

The Six Sister Cities of Silicon Valley

Silicon Valley has achieved an almost mythical status in both modern American popular culture as well as the annals of world economic history. Located south of San Francisco, the name “Silicon Valley” was coined in the early 1970s due to the volume of silicon chip innovators and manufacturers in the area. Today, the valley is synonymous with technology innovation and venture capital: two mighty forces of change. The great majority of American economic growth over the past 5 decades has been as a direct or indirect result of an omnipresent technology explosion. However, the over-simplification of Silicon Valley as a physical location belies a misunderstanding of the depth and breadth of contributions to this magnificent and ubiquitous technology upheaval. When using the moniker “Silicon Valley,” we should recognize that, as the number of technology centers across the U.S. increases in numbers, we are actually referring to a mindset as much as a place and to an entrepreneurial approach to life as much as a set of adjacent zip codes.
  • Boston, Massachusetts
  • Austin, Texas
  • Huntsville, Alabama
  • Pittsburgh, Pennsylvania
  • Raleigh, Durham, Chapel Hill, North Carolina (RESEARCH TRIANGLE PARK – RPT)
  • Seattle, Washington
2018-07-05 00:00:00 Read the full story.  

How Google Brain’s New RNN Analyses And Generates Sketch Drawings

had a stellar improvement in the past few decades. RNN has progressed from just being a possible theoretical concept to a standard element in neural network applications, which are now being used in machine learning areas such as handwriting recognition, language learning, speech recognition among others. In this article, we will discuss a new type of RNN developed by Google Brain, their artificial intelligence team, which analyses human sketches and presents them on a vector format. 2018-07-09 11:59:05+00:00 Read the full story.  

FXCM Algo Summit 2018

Last month, 5 quant trading experts joined FXCM for an educational event in London. Approximately 200 attendees attended lectures and workshops on topics ranging from machine learning to alpha generation to cryptocurrencies. The speakers conducted both interactive workshops and presentations. You can access the recordings of each presentation below.
  • Artificial Intelligence and Algorithmic Trading by Yves Hilpisch
  • How to Create a Quantitative Trading System Based on Various Algos by Stéphane Ifrah
  • Cryptocurrency Workshop by Stephane Ifrah
  • Backtest an EMA Strategy Using REST API by Charles Graves
  • Trading Strategies That Are Designed Not Fitted by Rob Carver
  • Trend-Following Strategies for Tail-Risk Hedging and Alpha Generation by Artur Sepp
  • From Trading Strategy to Industry Professional: How to Break into the Investment Management Business by Andreas Clenow
  • An Interactive Q&A Session on Trading System Design by Rob Carver
2018-07-05 11:56:39+00:00 Read the full story.  

Using FPGA superpowers to speed up cloud workloads with InAccel 🚀

There is nothing new under the Sun, the saying goes, and that also holds true for Field Programmable Gate Arrays. FPGAs though are back with a rage, with major cloud vendors offering FPGA-as-a-service and chip manufacturers renewing their interest in the space. Among other facts, Intel acquired Altera for $16B and is now shipping the new Intel Xeon CPUs with an integrated FPGA. 2018-07-09 11:46:01.448000+00:00 Read the full story.  

Baidu Releases New AI Chip In Its Annual Event ‘Create 2018’

China’s largest internet search giant Baidu released a new AI chip called ‘Kunlun’ in its second annual developer conference Baidu Create 2018. This marks the company’s latest venture into AI on top of FPGAs, cameras and other related technologies. With the chip, Baidu aims to provides an exclusive hardware for machine learning applications. 2018-07-05 11:10:15+00:00 Read the full story.  

RPA Company Automation Anywhere Raises $250 Million In Series A Funding

Automation Anywhere, a noted company in the robotic process automation (RPA) field announced that they had completed their Series A financing round of $250 million led by New Enterprise Associates, Goldman Sachs Growth Equity; with participation from General Atlantic and World Innovation Lab. This investment has brought the company’s post-money valuation to $1.8 billion. 2018-07-04 06:45:51+00:00 Read the full story.  

AI in digital banking sales: win the 200 billion dollar race

Currently, front office is king in Banking AI applications, according to the findings of a recent survey of the 30 of the world’s biggest banks by the Financial Times. Some 17 of the 18 banks that provided detailed answers are already using AI in front office, ranging from Citi’s Facebook messenger chatbot to UBS’s use of Amazon’s virtual assistant Alexa for customer service. Front office is also where banks see the biggest potential for AI-related savings. But the survey results also show that broad is best: eight banks are using AI in front office, middle office, back office and data analytics. The other 10 are using it in three of the four areas. 2018-07-09 09:46:18 Read the full story.  

Udacity Or Coursera: Which MOOC Is The Best For Artificial Intelligence And Machine Learning Upskilling?

It is a question that rankles every data science and machine learning enthusiast who wants to upskill and take up MOOC specialisations to land a data science job. Most self-taught machine learning and data science practitioners have to tread the self-learning path with a bunch of online courses, especially through Coursera and Udacity Nanodegree programme. These courses also serve as preparatory material for graduate courses. While there is no definite consensus on which course is better, it all depends on individual goals and whether students are geared towards a theoretical learning environment or prefer a more job-oriented, hands-on approach. Most of the time, learners fall back on ratings and reviews to gauge the effectiveness of the program and whether it is worth the ROI. Both the platforms are helmed by industry stalwarts – Udacity was founded by Google X founder and ex-Stanford professor Sebastian Thrun, and Coursera is helmed by Andrew Ng. 2018-07-03 11:18:28+00:00 Read the full story.  

Goldman Sachs once again changes its World Cup predictions — and is now forecasting a Belgium-England final

Goldman Sachs has changed it’s mind about the World Cup final – again. “With Brazil now out of the World Cup, Belgium is at the top of our probability table,” Goldman’s analysts wrote. Belgium beat Brazil 2-1 in Sochi on Friday night, and are now the most likely team to take home the trophy, with a 32.6% chance of winning, according to Goldman. 2018-07-09 12:16:44+00:00 Read the full story.  

How uses cloud data to drive global good with Azure

In the late 90’s, 12-year-old Craig Kielburger knew he wanted to change the world. Craig saw things he knew weren’t right —abuse, inequality, and cynicism—and decided to make a difference. That compassion grew, and today, Craig and his brother Marc’s nonprofit organization, WE, is dedicated to “making doing good, do-able.” Although its offices are based primarily in North America and the United Kingdom, WE’s efforts and impacts have a global rea… 2018-07-06 15:18:00+00:00 Read the full story.  

How tech companies are successfully disrupting terrorist social media activity

One difficulty the social media companies face is that, if a terrorist group is blocked from one platform, it might simply move to a different one. In response to this, GIFCT members have created a shared industry database of “hashes”. A hash is a unique digital fingerprint that can be used to track digital activity. When pro-terrorist content is removed by one GIFCT member, its hash is shared with the other participating companies to enable them to block the content on their own platforms. 2018-07-04 18:30:02+10:00 Read the full story.  

Grappling with Google Duplex: What happens when our AI assistants suddenly seem more human

When I first saw Google’s new Duplex technology, it was simultaneously thrilling and horrifying. It’s the kind of technological evolution that you initially see as furthering humanity and having the potential to spawn countless business opportunities. However, as its potential sinks in, you get a creepy vibe as you realize how bad actors might exploit it with malicious intent. As it turns out, what Google Duplex will do may not be quite as promising and scary as its first demo suggested. Nonetheless, this article will explore four privacy and security concerns we should consider as future AI-based personal assistants continue to advance. 2018-07-06 19:30:11-07:00 Read the full story.  

Customizing Plots with Python Matplotlib – Towards Data Science

A central part of Data Science and Data Analysis is how you visualize the data. How you make use of visualizations tools has an important role in defining how you communicate insights. In this article, I want to walk you through my framework for going from visualizing raw data to having a beautiful plot that is not just eye-catching but emphases the core insights you want to convey. 2018-07-09 01:35:35.627000+00:00 Read the full story.  

Do match-days boost the FIFA World Cup song? – Towards Data Science

Music is something that is part of football’s culture. Official FIFA and local theme songs unite the armies behind all the countries that participate in this year’s World Cup. Aside from enhancing the atmosphere during the game, the event gives artists exceptional world coverage and notoriety. We can all remember hits like Shakira’s “Waka Waka” and Pitbull’s “We Are One”, though these were released respectively 8 and 4 years ago. Because of the enormous reach the event has and music being unifying amongst supporters I wanted to know if we can track down the results of this World Cup’s anthem and see if these would increase on game days — and if this would be the case, does this differ per country? 2018-07-08 18:33:05.234000+00:00 Read the full story.  

Modern Business Intelligence: Leading the Way for Big Data Success

Due to the escalating growth in unstructured data creation, many enterprises are realizing traditional approaches to data management are not enough. As a result, these organizations are exploring options such as looking to data scientists to complement the tasks traditionally assigned to business analysts. This book will look closely at the emerging trends in big data and the pressing need for analytics solutions that emphasize more user-friendly approaches, such as more sophisticated visualization techniques. There are significant changes brewing that can potentially and irreversibly disrupt the traditional analytics. PDF DOWLOAD – LOGIN REQUIRED. 2018-07-06 00:00:00 Read the full story.  

Treating fraudsters to a taste of AI medicine

Fraud is an issue that remains constant in the financial services sector. It creates trust problems between consumers and FIs, it can discourage consumer uptake of new services and it’s expensive for everyone involved. Over £1bn has been stolen from bank customers through credit and debit card fraud in the past 12 months and one in 10 people in the UK have cancelled their credit or debit card in the past year because of attempted fraud. Such is the potential of AI that companies such as MasterCard and Worldpay have already started using AI to detect fraudulent transaction patterns and prevent card fraud. PayPal has joined their ranks too, with the online provider using its home-grown artificial intelligence engine to detect suspicious activity, and more importantly to separate false alarms from true fraud. 2018-07-05 14:10:45 Read the full story.  

Infographic : Robots, Disease And Sea Levels

A new strain of flu could crop up at any time and decimate the global population. Climate change could make our planet unlivable. Robots of our design rise up and turn against us. While these ideas may seem far-fetched, considering humanity’s potential end will help us prevent it, or at least be prepared. Whether we reach our end in the next pandemic flu or are lost to automated weapons of our own design, humanity’s future could be bleak. To learn more about humanity’s ominous future, check out this infographic. 2018-07-08 17:08:21-04:00 Read the full story.  

Apple’s Shortcuts will flip the switch on Siri’s potential

Apple’s strengths have always been the device ecosystem and the apps that run on them. With Shortcuts, both play a major role in how Siri will prove to be a truly useful assistant and not just a digital voice to talk to. 2018-07-08 00:00:00 Read the full story.  

Facial Recognition Shows Promise as Next Step in Corporate Security

Two minutes after the glass door of the Capital Gazette newspaper in Annapolis, Maryland, was blasted by a shotgun round, and five newspaper employees were killed, police had the alleged shooter in custody. But they had a problem when they took him in for questioning. He had no identification, he would not talk to police and his fingerprints returned no results from any database. Police were stumped, but then investigators thought to put the mystery gunman’s photo into the state’s new facial recognition system. In a matter of minutes, the alleged shooter was identified as Jarrod Ramos, a man who had previously sued the paper for its coverage of his arrest and conviction on stalking charges a few years earlier. 2018-07-06 00:00:00 Read the full story.  

UBS puts digital clone of chief investment officer in branch

UBS has teamed up with IBM and avatar biometrics outfit FaceMe to build two “animated digital assistants”, which will now be tested over three months. The first, dubbed Fin, is a non-human looking figure that will appear on screens and talk, in German, to clients that visit the branch, helping them to solve basic issues such as updating a new credit card or billing address, on the spot. The second, is Daniel Kalt, named after the regional chief investment officer, Switzerland at UBS Global Wealth Management. Kalt was photogrammetrically captured using more than 100 digital SLR cameras in a special lighting setup to build an accurate avatar. 2018-07-04 14:28:00 Read the full story. See the video at UBS’s own site.  

BofA Merrill introduces biometric log-ins for corporate mobile users

Bank of America Merrill Lynch has added fingerprint and facial recognition technology alongside an embedded token to enable quick access to payment approvals for corporate users of its CashPro mobile application. With CashPro Mobile’s integrated token feature, users no longer have to jump between the mobile app or token app or carry their physical token. The secure biometric authentication simplifies log-ins while providing stronger security than passwords provide today. The app currently has more than 475,000 active users among the bank’s commercial, large corporate, and business banking clients and its popularity among users is growing. 2018-07-02 15:53:00 Read the full story.  

Tech company develops algorithm to greenlight movie scripts

Hollywood’s movie industry could gain another cog in the machine, if one tech company has anything to do with it. US startup ScriptBook hopes to change the way films get greenlit – or approved for production – by removing the human decision-making from the process altogether. Nadira Azermai, who founded ScriptBook, sees her company as a possible solution to the nosediving profits besieging Hollywood. ScriptBook uses artificial intelligence to algorithmically decide which screenplays to pursue and which to reject in order to make the most profit and even achieve awards success. Presenting ScriptBook at the Karlovy Vary Intl. Film Festival, Azermai said that when analysing the screenplays for Sony’s releases between 2015 and 2017, it correctly spotted 22 of the 32 movies that were box-office failures from a total of 62 movies. Or, as Azermai summed it up according to a report in Variety: “If Sony had used our system they could have eliminated 22 movies that failed financially.” 2018-07-06 00:00:00 Read the full story.
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AI & Machine Learning News. 02, July 2018

Playing Card Detection

Generating a dataset of playing cards to train a neural net. The notebook creating_playing_cards_dataset.ipynb is a guide through the creation of a dataset of playing cards. The cards are labeled with their name (ex: “2c” for “2 of spades”, “Kh” for King for hearts) and with the bounding boxes delimiting their printed corners. This dataset can be used for the training of a neural net intended to detect/localize playing cards. It was used on the project Playing card detection with YOLO v3 2018-07-02 09:03:06+05:30 Read the Reddit Post. CloudQuant Thoughts… Not really a news post, just a post on Reddit but excellent nonetheless. Thought I would take the opportunity to give it some exposure and pull the Reddit Post, Video and Github all together in one place.  

Understanding and Building an Object Detection Model from Scratch in Python

When we’re shown an image, our brain instantly recognizes the objects contained in it. On the other hand, it takes a lot of time and training data for a machine to identify these objects. But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive. In this article, we will understand what object detection is and look at a few different approaches one can take to solve problems in this space. Then we will deep dive into building our own object detection system in Python. By the end of the article, you will have enough knowledge to take on different object detection challenges on your own! 2018-06-28 08:03:26+05:30 Read the full story. CloudQuant Thoughts… This article goes into a little more detail on AI for computer vision if you are interested.  

The Science Behind OpenAI Five – One of the Greatest Breakthroughs in the History of AI

Bill Gates called it a “huge milestone in advancing artificial intelligence” and the entire tech world was amazed last week when a bots developed by OpenAI were able to beat humans in a game of Dota 2 (Defence of the Ancients 2 – Valve Software). The accomplishment represents a major breakthrough in the history of artificial intelligence comparable with AlphaGo defeating Lee Sedol in 2016. OpenAI system leveraged state of the art deep learning research to that showed that AI is actually capable to deal with the messiness and complexity of physical environments in the real world. 2018-07-02 12:19:37.735000+00:00 Read the full story. CloudQuant Thoughts… “Quantitively, the experience collected by the OpenAI Five system was estimated something around the 180 years per day. To accomplish that, the OpenAI team used approximately 128,000 CPU cores on the Google Cloud platform.” This is staggering, it’s not “comparable with AlphaGo”, the step-change it made is comparable to the step-change made by AlphaGo. ie we just took a huge step forwards.  

AI is changing this industry. Now what?

I spent last week listening to experts in artificial intelligence talk about what AI can and will bring to the markets and the broader world. What is patently clear is that AI is here now and is only going to expand. It is firmly in the hedge fund space today, as funds look for new ways to generate alpha. A May report from Future Perfect Machine named Bridgewater Associates, Renaissance Technologies, DE Shaw, Two Sigma, Winton Capital Management, Schonfeld Strategic Advisors, PDT Partners, Man Group and Citadel as big AI users and cited a Barclays PLX survey that said “62 percent of hedge funds now use some type of AI process in collecting information, finding best execution, identifying market momentum and scanning information sources for signals and obscure patterns.” Speaking at the WealthTech conference Morgan Slade, CEO at CloudQuant, said AI is being applied to only 50 or so of the 1500 alternative data sets available today. In the next few years, those alternative data sets may top 6,000, he said. Researchers who can mine that data fastest using AI may have the next edge. 2018-07-02 00:00:00 Read the full story. CloudQuant Thoughts… A manual trader once described the US Equity market as being like a ring of salivating wolves waiting for a single chunk of meat to be dropped in the center of the circle, you have to be first to the meat. Well in the 10+ years I have been in this industry I have seen that race to the center increase in speed dramatically. Opportunities fade and new opportunities arise. You have to find new ways of discovering Alpha, of finding an edge. Alternative data-sets combined with AI are definitely the cutting edge today.  

Relative Coin Rank as Investment Strategy

We’ve seen the rise and fall of many crypto coins over the last few years. Sometimes new projects seem to come out of nowhere, while other times a given coin rises steadily through the ranks until it gets noticed by the mainstream. With more than 1600 coins and tokens on the market today, things are getting awfully crowded, and it is difficult to learn enough know whether any given coin is worthy of inclusion in our portfolios. 2018-06-30 19:07:04.355000+00:00 Read the full story. CloudQuant Thoughts... An interesting idea. Can you adapt it for US Equities? Give it a try at If you are successful we will back you with full force.  

Morgan Stanley Hires University of Pennsylvania Artificial Intelligence Expert

Morgan Stanley, which aims to expand its use of artificial intelligence, has hired Michael Kearns to help guide the effort. Kearns is a computer science professor at the University of Pennsylvania and has years of experience at Steve Cohen’s former hedge fund and other Wall Street firms. He will lead Morgan Stanley’s AI research and offer advice on deploying the technology for projects across the company, the New York-based firm said in a memo to employees Tuesday. 2018-06-28 11:02:07-04:00 Read the full story.

AI2 taps University of Washington researcher to lead $125M ‘common sense AI’ initiative

The Allen Institute for Artificial Intelligence in Seattle is making an ambitious bid to give AI common sense, a major factor in taking the technology beyond its current limitations. Now the institute has hired a new leader for the project: University of Washington Professor Yejin Choi. Choi will take the helm of Project Alexandria, the “common sense AI” initiative backed by $125 million from Paul Allen, the Microsoft co-founder and founder of the institute. Her addition comes amid a fierce talent war over top AI researchers and engineers. Just in the past few months, Facebook poached a top AI2 researcher for its growing Seattle office. AI2, in turn, hired a key AI leader from Amazon’s Alexa division. 2018-06-29 16:45:36-07:00 Read the full story. CloudQuant Thoughts… Both of these stories highlight the major issues in AI, that industry demand is outstripping educational supply, resulting in the private sector draining the educational sector of its best and brightest. This does not bode well for the future, we need the best in education to bring forth the best in the new generation of AI and ML engineers. Otherwise the supply will dry up.  

TORA Recognized as Trading System of the Year at Global Investor Awards 2018

TORA announced that it was awarded Trading System of the Year at the Global Investor Awards in London. TORA added significant product enhancements to help investors adhere to MiFID II and other new global regulations, including: A groundbreaking new AI-based tool within the TORA OEMS that analyses how effective trading decisions are at minimizing slippage versus a benchmark and that uses machine learning to provide pre-trade estimates to help traders improve execution quality. This real-time feedback mechanism was added as a direct response to the new, tougher, MIFID II best execution requirements. An AI-based AlgoWheel. This allows traders to see a slippage ranking of normalized broker algos and if required, automates the execution of low-touch orders to free more time to focus on high-touch orders. 2018-06-29 00:00:00 Read the full story. CloudQuant Thoughts… AI and ML are affecting every aspect of trading from the data to the decision to the trade.  

Lessons in artificial intelligence from Nest, Audi and a cockerel

Optimizing for the wrong Metric… The most important thing to get right about AI is what you are trying to optimize. For all the hype that exists around the smart home, self-driving vehicles, fear about losing jobs, I have shut off the “auto-scheduling” on my Nest, despite being a machine learning expert. I have an Audi that I find impossible to drive smoothly. And I grew up on a farm with a Cockerel that was relentless in his attacks on my legs. What do all these things have in common? Systems optimizing the wrong metric! 2018-06-30 22:30:57-07:00 Read the full story. CloudQuant Thoughts… A very entertaining and thought-provoking article. As someone who developed a number of successful auto-trading systems prior to the AI revolution, the best idea was rarely the one you set out to research.   An Extra CloudQuant Thought… We also note the number of stories “below the fold” where Microsoft is being mentioned. This is obviously a direct result of their change of direction under Satya Nadella.  
Below the Fold  

Improving Marketing Attribution With Machine Learning (Interview with Max Sklar of Foursquare)

As part of our AI For Growth executive education series, we interview top executives at leading global companies who have successfully applied AI to grow their enterprises. Today, we sit down with Max Sklar, Head of Machine Learning Attribution at Foursquare. User attribution, especially between offline and online worlds, is a persistent challenge for marketers. Using novel machine learning techniques on top of Foursquare’s incredible data trove of consumers’ physical behavior, Max enables enterprise customers to identify when online campaigns have driven offline revenue and convert real-world foot traffic into long-term digital customers and social media fans. 2018-06-25 19:26:04+00:00 Read the full story.  

The Top GitHub Repositories & Reddit Threads Every Data Scientist should know (June 2018)

  • Facebook’s DensePose – identifies more than 5000 nodes in the human body (for context, other approaches operate with 10 or 20 joints).
  • NLP Progress – This repository has been created especially to track the progress in the NLP field
  • MLflow – Databricks open source a solution to all ML framework challenges. that manages the entire machine learning lifecycle
  • Salesforce’s decaNLP – sentiment analysis model that can also do semantic parsing and question answering at the same time
  • Reinforcement Learning Notebooks – Reinforcement learning is becoming popular by the day and so is the open source community for it
  • Playing Card Detection with YOLOv3 – thread has a lot of useful information on how the technique was created
  • OpenAI Five – a group of 5 neural networks designed and developed to beat human opponents in the popular Dota 2 game
  • What ML Hypothesis are you Curious About but are Hoping Someone Else will Research it? – This discussion is like a wish list of what data scientists and machine learning practitioners want to see
  • Setup that Data Scientists use for Machine Learning – Read this thread to find out what other data scientists use for building their ML
  • Practical Use Cases for Reinforcement Learning – people already working in this field give their take on where they see RL penetrating in the near future
2018-07-02 09:03:06+05:30 Read the full story.  

Cloudera partners with MetiStream to launch Ember, an analytics platform for health care providers

Machine learning and analytics provider Cloudera is teaming up with health care analytics company MetiStream to launch a new health-focused, machine learning-powered medical records solution for hospital systems and outpatient clinics. Today, the firms jointly announced Ember, a product that “accelerates the time to patient insight” from handwritten clinical notes and other medical data. 2018-07-02 00:00:00 Read the full story.  

Top 5 Mistakes of Greenhorn Data Scientists – Towards Data Science

You prepared well to finally become a Data Scientist. You participated in Kaggle competitions and you binge watched Coursera lectures. You feel prepared, but the work as a real-life Data Scientist will prove vastly different from what you might expect. 2018-06-30 17:15:15.404000+00:00 Read the full story.  

Here Are Free AI Learning Resources For Beginners

Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google’s Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms. 2018-07-01 06:24:01+00:00 Read the full story.  

The AI bubble won’t burst anytime soon, but change is on the horizon

During an AI podcast a couple months ago, someone asked me if AI would be the next big tech bubble to burst. This question led me to think about what AI is today and where it’s headed. What is AI, really? It’s a next-generation network and database tool that’s short for “artificial intelligence.” AI just sounds sexier. In fact, AI today is not really what people think it is. AI theories and algorithms have been around for decades. 2018-07-01 00:00:00 Read the full story.  

Google Home’s language expansion leaves Alexa behind

With the recent launch of Spanish language integration for Google Home, Google is poised to gain even more ground against Amazon’s Alexa-powered devices. The addition of Spanish language functionality expands Google’s share in the U.S. digital assistant market and into Mexico and Spain. Alexa currently supports only three languages, including English, German, and Japanese, while Google Home is set to offer over 30 languages before 2019. As Alexa falls behind, many wonder why it has taken so long to make digital assistants multilingual. 2018-06-30 00:00:00 Read the full story.  

Microsoft improves facial recognition to perform well across all skin tones

That improvement addresses recent concerns that commercially available facial recognition technologies more accurately recognized gender of people with lighter skin tones than darker skin tones, and that they performed best on males with lighter skin and worst on females with darker skin. With the new improvements, Microsoft said it was able to reduce the error rates for men and women with darker skin by up to 20 times. For all women, the company said the error rates were reduced by nine times. Overall, the company said that, with these improvements, they were able to significantly reduce accuracy differences across the demographics. 2018-06-26 02:14:03-07:00 Read the full story.  

IBM Addresses AI Bias with Massive Image Archive

Bias isn’t only about a person’s predetermined views affecting his or her opinion on a particular topic. It’s also an important factor in how accurate information from a query using artificial intelligence turns out. Because of that belief, and because an AI system is only as good as the data upon which it is trained, IBM revealed June 27 that it will soon make available to the global research community:
  • A dataset of 1 million images to improve facial analysis system training. This archive will be five times larger than the largest face image dataset available today, and it is specifically designed to reduce sample selection bias.
  • A dataset of 36,000 facial images–equally distributed across various attributes– that algorithm designers can use to evaluate bias in their own facial analysis systems. This will specifically help algorithm designers to identify and address bias in their facial analysis systems. The first step in addressing bias is to know there is a bias, and that is what this dataset will enable.
The facial attribute and identity training dataset is annotated with attributes and identity, using geo-tags from Flickr images to balance data from multiple countries and active learning tools to reduce sample selection bias, the company said. 2018-06-27 00:00:00 Read the full story.  

Ridge Regression Vs Lasso: 2 Popular ML Regularisation Techniques

In the case of ML, both ridge regression and Lasso find their respective advantages. Ridge regression does not completely eliminate (bring to zero) the coefficients in the model whereas lasso does this along with automatic variable selection for the model. This is where it gains the upper hand. While this is preferable, it should be noted that the assumptions considered in linear regression might differ sometimes. Both these techniques tackle overfitting, which is generally present in a realistic statistical model. It all depends on the computing power and data available to perform these techniques on a statistical software. Ridge regression is faster compared to lasso but then again lasso has the advantage of completely reducing unnecessary parameters in the model. 2018-06-28 05:44:43+00:00 Read the full story.  

RippleMatch uses AI to help students line up work after college

For most young adults, finding work out of school isn’t exactly a walk in the park. Two-thirds of recent college graduates struggle to launch their careers in the first few years, according to There Is Life After College author Jeffrey Selingo, and as many as 49 percent of them don’t land a job related to their field of study. That’s why Yale graduates Eric Ho and Andrew Myers created RippleMatch, a machine learning-powered recruitment tool for college students. It recently raised $3 million in a funding round led by Accomplice and Bullpen Capital. 2018-06-29 00:00:00 Read the full story.  

Niantic Opens AR Platform To Third-Party Developers, Shows Off Experimental Capabilities

Niantic Labs, the developer behind “Pokémon Go,” has announced that it is planning to open its augmented reality platform to third-party developers. The company also shared its vision for the future of its AR platform, which includes advancements in machine learning and computer vision. Niantic has acquired the computer vision and machine learning company Matrix Mill and established a new office in London. Niantic plans to coordinate with Matrix Mill and Escher Reality to continue the advancement and development of its Real World Platform 2018-06-29 05:46:14-04:00 Read the full story.  

How The Trade Desk Uses AI to Find ‘Perfect’ Impressions for Advertisers

Here’s a thought: Why not run digital advertising campaigns in a similar fashion to the stock market, using programmatic methods and new-gen IT? How about letting a few knowledgeable humans together with machine learning and analytics engines figure out where the perfect impressions are–on any device–that present buying opportunities relevant to us today? That’s exactly what global ad tech provider The Trade Desk does on a 24/7 basis. 2018-06-27 00:00:00 Read the full story.  

IT Science Case Study: Detecting Advanced Cyber Threats

IBC’s decision to use BluVector Cortex resulted in significant success. In 2017, IBC faced a new zero-day ransomware threat known as Jaff. Before news about the new zero-day malware broke publicly, IBC’s threat team observed more than 2,000 instances of Jaff in just a week. Thanks to the BluVector Cortex platform, using its Machine Learning Engine (MLE) to sort through the millions of files on the network, the threat team detected Jaff before it even had a name. With that knowledge, the team then used its containment software to halt the further spread of the malware. 2018-06-29 00:00:00 Read the full story.  

30 Free Resources for Machine Learning, Deep Learning, NLP & AI

Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next. This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). 2018-06-30 00:00:00 Read the full story.  

How Artificial Intelligence Is Disrupting the ETF Industry

The robots are here. Artificial intelligence (AI) continues to build its presence across global industries, so its progression into the asset management sector is only natural. AI is already disrupting the ETF industry and it is having spillover effects into other investment vehicles. Millions of market signals, news articles, and social media posts are processed by currently operating funds to produce thousands of hypothetical test portfolios which are further distilled down into daily trade recommendations. There has been an increase in the number of usually tight-lipped hedge fund managers admitting to using AI after these AI ETFs launched. Others will eventually catch on that more cost-efficient asset management structures exist through the use of AI. For all its advances, it’s unlikely that AI will immediately replace human managers and analysts. But what it will do is to allow them to more efficiently manage the overwhelming amounts of market data. 2018-07-02 08:30:04-04:00 Read the full story.  

Microsoft Goes Silo-Busting for Enterprise Cloud Analytics

Gathering insights from information placed on Microsoft’s cloud-based storage and big data analytics platforms is about to get easier for the company’s enterprise customers. On June 27, Microsoft unveiled new cloud capabilities that further lower the barriers to big data analytics. They include Azure Data Lake Storage Gen2, a service that brings “together the notion of a Hadoop compatible file system with a scale-out object cloud storage platform,” namely Azure Blob Storage. 2018-06-27 00:00:00 Read the full story.  

Microsoft’s Move To Launch ‘Research Open Data’ Is A Revolutionary Way To Compete With Google And AWS

Dubbed as an excellent open data effort by one of the leading cloud providers, Microsoft is striving hard to gain developer and community trust by embracing the open data movement with “Research Open Data”. They plan for it to be an excellent collection of free datasets to push state-of-the-art research in areas such as natural language processing, computer vision, and domain-specific sciences. The datasets are available in several categories like Biology, Computer science, Engineering, Information science, Mathematics, Physics, Social Sciences. 2018-06-30 12:28:53+00:00 Read the full story.  

How Analytics-Driven Store Clustering Can Drive Sales And Profits In Retail

Personalisation is the new mantra for retailers today. However, that should not be limited only for the customers but also be adopted as a strategy towards growing store sales. ‘One-size-fits-all’ approach would no longer work for achieving strong store-level growth and it is important to customise a strategy for each store. 2018-06-29 04:56:10+00:00 Read the full story.  

Microsoft’s Enterprise IoT Push Moves to the Intelligent Edge

Microsoft’s internet of things (IoT) product strategy for enterprises just took a major turn. Azure IoT Edge is generally available, the Redmond, Wash., software maker announced on June 27, bringing with it the quality and support assurances required for production IoT deployments that enable the intelligent edge at scale. The cloud-based service provides tools that enterprises can use to deploy, secure and run artificial intelligence (AI) and data analytics workloads on edge IoT devices and systems. 2018-06-28 00:00:00 Read the full story. Raises $35 Million In Series B Funding From Dell And TGP Growth, a noted provider of enterprise artificial intelligence applications, announced in an official statement that they have raised $35 million in a Series B funding round, bringing their fundraising total to $51 million. The company will use the new funds to expand their suite of applications, which help key industries predict the future and make better business decisions. 2018-06-27 10:35:03+00:00 Read the full story.  

Investors to use less sell-side research, more AI

Institutional investors plan to use less sell-side research in the coming years, relying more on proprietary in-house work that takes advantage of AI, according to a study commissioned by Thomson Reuters. 2018-06-29 00:01:00 Read the full story.  

More States Opting To ‘Robo-Grade’ Student Essays By Computer

Here’s a little pop quiz. Multiple-choice tests are useful because:
  • A: They’re cheap to score.
  • B: They can be scored quickly.
  • C: They score without human bias.
  • D: All of the above.
It would take a computer about a nano-second to mark “D” as the correct answer. That’s easy. But now, machines are also grading students’ essays. Computers are scoring long-form answers on anything from the fall of the Roman Empire, to the pros and cons of government regulations. 2018-06-30 00:00:00 Read the full story.  

Artificial Nociceptors For AI, A Novel Approach To Comprehend Danger

The developments in today’s artificial intelligence applications are humongous and have reached a top-notch status in research. Robots, for example once had limited applications in niche areas, but are now being used in various business and commercial areas. AI systems such as robots require near-perfect sensory components to collect and analyze data for accurate functioning. Recent trends in AI have also focused on innovations in hardware, apart from the methodology. Be it in the form of insanely smaller integrated chips or powerful and fast processors, the pace in bringing all these elements on a practical viewpoint for AI has been magnificent. One unique biological component which has slowly been enticing AI researchers towards hardware implementation is nociceptor. 2018-06-28 11:16:24+00:00 Read the full story.  

Deep Learning Tensorflow Benchmark: GeForce Nvidia 1060 6GB Vs Intel i5 4210U

The use of GPUs in the 3D gaming realm has given rise to a high-definition gaming experience for gamers all over the world. Now, these mighty devices are being used in the world of deep learning to produce robust results — exactly 100 times faster than a CPU. The reason why GPU is so powerful is because the number of cores inside it are three to five times more than the number of cores in a CPU, all of whom work parallelly while computing. In this article, we shall be comparing two components of the hardware world — a CPU, an Intel i5 4210U vs a GPU, a GeForce Nvidia 1060 6GB. With the help of one basic high-dimensional matrix multiplication, the famous MNIST dataset, we shall compare the computation power and speed of these devices. 2018-06-26 12:13:20+00:00 Read the full story.  

Doubting Nvidia’s (NASDAQ:NVDA) Future Is Dumb

Doubting Nvidia (NVDA) is a dumb move. Shares of California-based Nvidia have slumped slightly in recent trading, down about 7% for the week. The stock has traded in the red in 11 of the last 16 sessions. About 60% of Nvidia’s revenue is owed to PC gaming and cryptocurrency mining graphics processing units (GPUs), Davuluri said. But don’t think Nvidia is any one-trick pony when it comes to the chips. The mix of product and profit will increase, Davuluri said. “[Gaming and AI] will come closer to intersecting as a portion of sales,” the analyst predicted. “There’s definitely a mix shift within the organic growth itself.” As for datacenter revenue, try this on for size: sales rocketed 132.8% year-over-year in 2017 for the unit. 2018-07-02 06:45:00-04:00 Read the full story.  

How AI could improve the quality of end-of-life care

The means to predict mortality using artificial intelligence could be a transformative factor in the future of palliative health care. While this topic may seem a bit morbid, AI has the potential to help medical care providers and doctors significantly improve the delivery of patient care in hospice situations. 2018-06-29 00:00:00 Read the full story.  

Another Robotics ETF is Here

There is momentum for investment vehicles with exposure to the artificial intelligence (AI) and robotics theme. Issuers of exchange-traded funds (ETFs) are racing to meet that demand, as highlighted by the debut of another AI and robotics ETF. On Thursday, the iShares Robotics and Artificial Intelligence ETF (IRBO) debuted. 2018-06-29 07:10:00-06:00 Read the full story.  

How science fiction can predict the future and help tech innovators make better decisions

Organizations may do better in planning for the future by thinking less like business leaders and more like science-fiction writers. That’s the idea behind the Seattle startup Scout, a subscription website, and community that develops near-term, what-if scenarios based on deep analysis of current technologies and trends. If using science fiction and analysis in this way sounds untried, Scout co-founder Berit Anderson said it reflects the approach of well-known tech industry figures such as Elon Musk and Vint Cerf. “I really saw how strategic foresight and science fiction impacted and shaped their worldview,” Anderson said. “Not just how they think about the future, but also (how) a lot of science fiction creates kind of a blueprint for some of the world’s top technologists and CEOs.” 2018-06-25 13:13:21-07:00 Read the full story.  

Weekly Selection — Jun 29, 2018 – Towards Data Science

  • How Bayesian statistics convinced me to hit the gym
  • Getting started with reading Deep Learning Research papers: The Why and the How
  • An Introductory Example of Bayesian Optimization in Python with Hyperopt
  • Deep Learning on the Edge
  • The 10 coolest papers from CVPR 2018
  • The Data Science Bubble
  • Data Science for Startups: Deep Learning
  • Towards Rapid Discovery of Viable Pipelines
  • Analyse a Soccer game using Tensorflow Object Detection and OpenCV
  • Winning the War Against Imbalanced Data
2018-06-29 15:30:49.135000+00:00 Read the full story.  
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