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AI & Machine Learning News. 17, September 2018

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

Andrew Ng is the most recognizable personality of the modern deep learning world. His machine learning course is cited as the starting point for anyone looking to understand the math behind algorithms. But even the great Andrew Ng looks up to and takes inspiration from other experts.

In this amazing and in-depth video series, he has interviewed some of the most eminent personalities in the world of deep learning (eight heroes, to be precise). The interviews span the length and breadth of deep learning, including topics like backpropogation, GANs, transfer learning, etc. Even artificial intelligence crops up in between conversations. But don’t worry if these terms sound overwhelming, we have listed down the key takeaways from each interview just for you.

The 8 interviewees are Geoffrey Hinton, Ian Goodfellow, Yoshua Bengio, Pieter Abbeel, Yuanquing Lin, Andrej Karpathy, Ruslan Salakhutdinov, Yann LeCun.
2018-09-14 00:58:57+05:30 Read the full story.

CloudQuant Thoughts… If you are already deep into AI and ML you will love this series of interviews!

 

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

 

  • Stealthy enterprise start-up Cresta AI is trying to automate repetitive tasks, starting with sales chats.
  • Sebastian Thrun, the creator of Google’s self-driving car project, is a co-founder of the company.
  • Cresta recently raised a seed round of funding.

Sebastian Thrun, one of the best known entrepreneurs in Silicon Valley, is taking on a new challenge that’s a big shift from his work in autonomous transportation or online education: He’s working to automate sales chats.

Thrun, who founded Google’s research lab X and its autonomous car project, education start-up Udacity, and electric aircraft company KittyHawk, is a co-founder and chairman of a stealthy enterprise company called Cresta AI. He’s the elder statesman of the founding team, which includes Zayd Enam and Tim Shi, who are both in their late 20s.
2018-09-16 00:00:00 Read the full story.

CloudQuant Thoughts… The big names in AI and ML are out in force this week. This story is of little interest except for the fact that it involves Sebastian Thrun!

 

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

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

Today Globant (NYSE :GLOB ), a digitally native technology services company, releases its 2018 Artificial Intelligence Technology Business Guide ‒ a playbook for organizations considering investing in AI, complementing its recently launched book, “Embracing the Power of AI.” The report found that the majority of decision makers (80 percent) believe AI can make an immediate impact on their business by doing things like enhancing routine tasks and sorting through massive sets of data. However, despite this excitement, many are still navigating what investing entails and how ready they are to implement the technology.

The report debunks common AI myths among decision makers to offer a modern perspective for how the technology can be effective for businesses:

  • Myth No.1: AI will replace humans.
  • Myth No.2: AI can’t help my business.
  • Myth No.3: AI’s sophistication leads to magical rewards.
  • Myth No.4: AI is just for tech experts.

2018-09-17 09:11 EST Read the full story.

 

Big Data 50 – Companies Driving Innovation

The wealth of data now available to organizations from internet-scale applications and the growth of the Internet of Things, is fueling the use of data lakes, artificial intelligence (AI), machine learning (ML), and predictive analytics solutions.

These and other technology initiatives for managing and analyzing data were explored in a recent Unisphere Research survey. In the report, analyst Joe McKendrick identified key trends that are shaping the way enterprises leverage their data, as well as the evolving priorities of data managers. Data lakes, a place to store diverse datasets without having to build a model first, are perhaps the most mature technology initiatives seen among enterprises in the survey.
2018-09-13 00:00:00 Read the full story.

 

Data Science Skills: Web scraping using python

One of the first tasks that I was given in my job as a Data Scientist involved Web Scraping. This was a completely alien concept to me at the time, gathering data from websites using code, but is one of the most logical and easily accessible sources of data. After a few attempts, web scraping has become second nature to me and one of the many skills that I use almost daily.

In this tutorial I will go through a simple example of how to scrape a website to gather data on the top 100 companies in 2018 from Fast Track. Automating this process with a web scraper avoids manual data gathering, saves time and also allows you to have all the data on the companies in one structured file.
2018-09-13 00:00:00 Read the full story.

CloudQuant Thoughts… There are real trading opportunities here if you can identify the companies who are leading the charge into AI/ML in their respective industries. Perhaps some news scanning, perhaps utilize a list like the big 50 above or just some good old fashioned web scraping?  Either way, if you can identify the leaders you may have a model you can code up on CloudQuant.com

 

Another Machine Learning Walk-Through and a Challenge

Don’t just read about machine learning — practice it!

After spending considerable time and money on courses, books, and videos, I’ve arrived at one conclusion: the most effective way to learn data science is by doing data science projects. Reading, listening, and taking notes is valuable, but it’s not until you work through a problem that concepts solidify from abstractions into tools you feel confident using.

The New York City Taxi Fare prediction challenge, currently running on Kaggle, is a supervised regression machine learning task. Given pickup and dropoff locations, the pickup timestamp, and the passenger count, the objective is to predict the fare of the taxi ride. Like most Kaggle competitions, this problem isn’t 100% reflective of those in industry, but it does present a realistic dataset and task on which we can hone our machine learning skills.
2018-09-10 Read the full story.

CloudQuant Thoughts… A nice detailed walkthrough using a huge dataset (55 million rows of data). Mapping data onto real world maps, and he includes a Jupyter Notebook for you to follow along. Kudos William Koehrsen!

 


Below the fold…

Labeling and Meta-Labeling Returns for ML Prediction

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

 

Linear Regression using Gradient Descent – Towards Data Science

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

 

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

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

 

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

Have you noticed that the recent surge of data scientists have a background in computer science? It’s not a coincidence. These two domains are important in their own right but when merged together, they produce powerful results.

We are thrilled to announce the release of episode 10 of our DataHack Radio podcast with none other than Professor Jeannette M. Wing! She has over 4 decades of experience in academia and the industry, and there is no one better to give a perspective on how computer science has evolved, and how it meshes with the data science world.

I have briefly summarized the key takeaways from this episode below…

  • Professor Jeannette Wing’s Background
  • Using Formal Methods Techniques to Improve Machine Learning Algorithms
  • Research Projects in Academia and Microsoft
  • Difference between Working in Academia v Industry
  • Where are Computer Science and Data Science Heading in the Next 5 Years?

2018-09-17 08:33:42+05:30 Read the full story.

 

The Real Reason behind all the Craze for Deep Learning

Deep learning has created a perfect dichotomy. On the one hand, we have data science practitioners raving about it, and every one and their colleague jumping in to learn and make a career out of this supposedly game-changing technology in analytics. And then there is everyone else wondering what the buzz is all about. With a multitude of analytics technologies projected as the panacea to business’ problems, one wonders what this additional ‘cool thing’ is all about.

For people on the business side of things, there are no easy avenues to get a simple and intuitive understanding. A Google search gets one entangled in the deep layers of neural networks, or gets them bowled over by the math symbols. Online courses on the subject haunt one with a bevy of stats terms. One eventually gives in and ends up taking all of the hype at face value. Here’s an attempt to demystify and democratize the understanding of deep learning (DL), in simple english and in under 5 minutes. I promise not to show you the cliched pictures of human brains, or a spider web of networks.
2018-09-10 Read the full story.

 

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

Last mile problems are the final hurdles to realizing AI’s promised values. Reaping the benefits of AI systems requires more than solid business cases, well-executed AI implementations, and powerful technology stacks. It often requires the collaboration of AI and staffs to provide the right experience to customers. But, companies usually struggle to do this well.

Many analyses highlight how to build AI systems from the lenses of executives and data scientists. Instead, this case study looks at the issues using a personal anecdote and from new perspectives: the ones through the eyes of front line staffs and customers. I discuss various practical solutions, such as 80–20 rules in AI and smoothing hand-offs between machines and humans, to help teams to overcome the last mile hurdles of AI delivery in the real world.
2018-09-09 Read the full story.

 

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

This is a great time to be a data scientist – all the top tech giants are integrating machine learning into their flagship products and the demand for such professionals is at an all-time high. And it’s only going to get better!

Apple has been a major advocate of machine learning, and has packed it’s products with features like FaceID, Augmented Reality, Animoji, Healthcare sensors, etc. While watching Apple’s keynote event yesterday, I couldn’t help but wonder at the new chip technology they have developed that uses the power of machine learning algorithms.
2018-09-13 13:37:32+05:30 Read the full story.

 

Machine Learning Dominates Conversations at Strata Data Conference

Machine learning and AI continue to be the hottest topic at tech conferences around the country and Strata Data was no different. Data professionals converged at the Jacob Javits Center in New York City from September 11 – 13 and the event was humming with the latest talk of how ML and AI will change the future. Byron Banks, VP, product marketing, analytics at SAP, said that while machine learning and automation is predicted to replace certain human tasks, it’s really not emerging to replace us all, instead it’s coming to help users become better at their jobs.

“It’s that idea of harmony,” Banks said. “There’s always an end user group of people involved in working with the machine learning to make it the most effective rather than having the person disappear from the transaction.” David Judge, vice president, SAP Leonardo at SAP, agreed and said it’s not about replacing people, it’s about removing manual, arbitrary tasks. Customers may have machine learning teams and are investing heavily in AI, however, the number of companies managing it effectively are still small, said Will Davis, director of product marketing, Trifacta.
2018-09-14 00:00:00 Read the full story.

 

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

MapR (provider of a Data Platform for AI and Analytics) has announced six new data science service offerings to help customers gain immediate value from machine learning (ML) and artificial intelligence (AI).

According to MapR, the new offerings addresse the fact that AI and ML can be complex, as well as that organizations don’t always have the capacity to execute on AI and ML ideas, and those that do, may not be able to bring those ideas to production.

The six new MapR data science lifecycle service offerings include an AI/ML Hack-a-thon offering in which the MapR Data Science team works with the organization to identify a business use case and prototype a solution to deliver a real ML and AI solution that the organization will continue to improve and maintain over time.
2018-09-11 00:00:00 Read the full story.

 

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

In this post we’ll describe how we used deep learning models to create a hybrid recommender system that leverages both content and collaborative data. This approach tackles the content and collaborative data separately at first, then combines the efforts to produce a system with the best of both worlds.

Using the MovieLens 20M Dataset, we developed an item-to-item (movie-to-movie) recommender system that recommends movies similar to a given input movie. To create the hybrid model, we ensembled the results of an autoencoder which learns content-based movie embeddings from tag data, and a deep entity embedding neural network which learns collaborative-based movie embeddings from ratings data.
2018-09-10 Read the full story.

 

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

Python is the fastest-growing programming language in the world, as it increasingly becomes used in a wide range of developer job roles and data science positions across industries. But how did it become the go-to coding language for so many tasks?

“Python is very popular because of its set of robust libraries that make it such a dynamic and a fast programming language,” said Kristen Sosulski, clinical associate professor of information, operations, and management sciences in the Leonard N. Stern School of Business at New York University, and author of Data Visualization Made Simple. “It’s object-oriented, and it really allows for everything from creating a website, to app development, to creating different types of data models.”
2018-09-13 Read the full story.

 

KPMG acquires a minority stake in fintech startup AdviceRobo

AdviceRobo is a Dutch fintech startup developing technology that predicts financial risk of people and companies taking out loans. To predict risk, the company applies artificial intelligence(AI) on non-financial data including the behavior of potential borrowers. AdviceRobo’s technology enables lenders, such as banks and retailers, to limit the risks of lending and reach a larger group of clients.

“AdviceRobo is definitely a frontrunner in the sector”, says Rob Fijneman, Head of Advisory at KPMG. Fijneman: “We are very pleased that the alliance with AdviceRobo will enable us to add these types of AI-based predictive behavioral models to our services for lenders. AdviceRobo’s models will enable lenders to improve their credit risk models and thus reduce costs, especially in areas where data is the limiting factor.
2018-09-14 00:00:00 Read the full story.

 

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

 

  • Developments of machines and automation software in the workplace could create 58 million new jobs in the next five years, according to a report from the World Economic Forum.
  • The outlook for job creation is more positive today because companies better understand what kind of opportunities are available to them due to developments in technology, according to WEF.
  • There would also be “significant shifts” in the quality, location and format of new roles, the report found.

Machines will overtake humans in terms of performing more tasks at the workplace by 2025 — but there could still be 58 million net new jobs created in the next five years, the World Economic Forum (WEF) said in a report on Monday.
2018-09-17 00:00:00 Read the full story.

 

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

Kx announces a partnership with Anaconda, Inc. to add the kdb+ database system, and related machine learning libraries, to Anaconda’s popular Python and R open source distribution platform. Kdb+ is now much more easily integrated into Python-based projects and technologists outside of the kdb+ community can now take advantage of the power of kdb+ without having to learn how to program in q.

Kdb+ is the world’s fastest time-series database at the forefront of high-performance streaming, real-time and historical analytics. Its unified, elegant q language includes first-class tables, functions and time-series features. Its tiny footprint efficiently scales vertically and horizontally.
2018-09-12 00:00:00 Read the full story.

 

Kx technology selected by Canadian Securities Administrators for advanced market surveillance

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

 

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

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

 

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

After an interesting year for Google Cloud’s artificial intelligence group, Andrew Moore, dean of computer science at Pittsburgh’s Carnegie Mellon University, will become head of the division at the end of the year, with current leader Fei Fei Li returning to Stanford in a move that Google said was all part of the original plan.

Moore, a former Google employee, will rejoin the company at the end of the current semester at Carnegie Mellon, Google Cloud CEO Diane Greene announced in a blog post. “We are incredibly fortunate to have Andrew’s leadership at this point in our development as we define how we will expand bringing AI and ML technologies and solutions to developers and organizations all over the world,” she wrote.
2018-09-10 15:57:48-07:00 Read the full story.

 

Nvidia Aims Tesla T4 GPUs at AI Inferencing in Data Centers

Nvidia officials are looking to press their advantage in the fast-growing artificial intelligence space with the introduction of the company’s new Tesla T4 GPUs and a new platform and software aimed at the inference side of the AI equation. At the vendor’s GTC technology conference in Tokyo this week, Nvidia CEO Jensen Huang showed off the new GPU, which is based on Nvidia’s Turing architecture that was introduced last month. At the time, the first of the Turing GPUs unveiled were aimed primarily at gamers. At the GTC Japan event, Huang turned his attention to the data center, including hyperscale environments.

Along with the Tesla T4 GPUs, the CEO announced the TensorRT software to help drive the development of voice, video, image and recommendation services and the TensorRT Hyperscale Inference Platform, powered by the T4 GPUs and aimed at enhancing inference tasks in such industries as automotive, manufacturing robotics and health care.
2018-09-13 00:00:00 Read the full story.

 

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

Siri Suggestions will surface recommended actions using more than 100 different factors such as time of day, location, or even the Wi-Fi network you’re on to determine whether or not a shortcut suggestion surfaces on your smartphone lock screen or Apple Watch face. A standalone Shortcuts app also lets users create custom commands.

Whereas before with SiriKit you could connect with a limited number of apps for use cases like sending messages or hailing a ride from Uber, Shortcuts appears to be more flexible than similar products from Alexa and Google Assistant, and is capable of connecting Siri with apps that enable Shortcuts APIs.
2018-09-14 00:00:00 Read the full story.

 

Fintech tackles op risk

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

 

Cooking With Robots: MIT Students Teach AI To Make Pizza

Have you ever used artificial intelligence to make pizza? A group of MIT students tried it, and they are quite pleased with how it came out. The pizza included the AI-made “wale[sic] walnut ranch dressing” as a topping. In the sea of reports hinting at the dangers of artificial intelligence, the Massachusetts Institute of Technology group found a way to show that robots and humans could have a bright, peaceful future together. On Monday, MIT student Pinar Yanardag and her colleagues launched a new project called “How to Generate (Almost Anything.) Each week they will release a new product, such as art, perfume or food, that was created through the work of MIT students and their AI-powered robot. “By augmenting human capabilities and pushing the boundaries of creativity, can AI inspire us to create things that wouldn’t have existed otherwise? A dress designed with a crazy hat, a pizza made with shrimp & jam or a scent that has never been smelled before?”

In the first chapter of their project, the MIT students wanted to teach their AI to make pizza. To do this, the robot’s neural network had to process hundreds of artisan pizza recipes and come up with new topping combinations that would go nicely together. “In general, AI models are very good at connecting different pieces of information together – that’s why there is usually a surprise factor in anything that an AI generates,” Yanardag said. “In our pizza experiment, we saw something similar where AI combined ingredients like shrimp and Italian sausage with jam, which it picked up from a dessert pizza.”
2018-09-14 09:40:37-04:00 Read the full story.

 

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

Artificially intelligent (AI) systems are as diverse as they come from an architectural standpoint, but there’s one component they all share in common: datasets. The trouble is, large sample sizes are often a corollary of accuracy (a state-of-the-art diagnostic system by Google’s DeepMind subsidiary required 15,000 scans from 7,500 patients), and some datasets are harder to find than others.

Researchers from Nvidia, the Mayo Clinic, and the MGH and BWH Center for Clinical Data Science believe they’ve come up with a solution to the problem: a neural network that itself generates training data — specifically, synthetic three-dimensional magnetic resonance images (MRIs) of brains with cancerous tumors. It’s described it in a paper (“Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks”) being presented today at the Medical Image Computing & Computer Assisted Intervention conference in Granada, Spain.
2018-09-16 00:00:00 Read the full story.

 

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

Artificial intelligence tools will never be widely used if it takes decades of expertise to put them into action, which is why cloud companies have been working hard to make them easier to use and more accessible. Microsoft took another step in that direction Thursday with the acquisition of San Francisco-based Lobe.

Founded by Mike Matas, Adam Menges, and Markus Beissinger in 2015, Lobe created visual tools that can build deep-learning models with a drag-and-drop user interface, rather than lines of code. “We look forward to continuing the great work by Lobe in putting AI development into the hands of non-engineers and non-experts,” Scott said in his post.
2018-09-13 17:38:19-07:00 Read the full story.

 

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

Base10 Partners today announced the launch of a $137 million fund to invest in early-stage startups that will use AI to change industries by empowering workers instead of automating them out of jobs.

The prime directive of the debut fund will be to back companies in industries like real estate, construction, waste management, and logistics — what managing partner Adeyemi Ajao calls “automation for the real economy” and “solving problems for 99 percent of people.”
2018-09-17 00:00:00 Read the full story.

 

Cisco Builds UCS C480 ML Rack Server to Accelerate Deep Learning

Cisco Systems on Sept. 10 unveiled its UCS C480 ML rack server, a system designed to accelerate deep learning workloads powered not only by two Intel “Skylake” Scalable Processors but also eight Tesla V100 GPUs from Nvidia that are connected by the chip maker’s NVLink interconnect. The system comes with as many as 24 disk drives offering as much as 182TB of storage and up to six NVMe drives. Plus, it can support up to four 100 Gigabit Ethernet switches.

The tightly integrated offering will use artificial intelligence and subsets like machine learning and deep learning to address the challenges facing enterprises as they try to drive competitive advantages from the massive amounts of data they’re generating.
2018-09-11 00:00:00 Read the full story.

 

Simple Method of Creating Animated Graphs – Towards Data Science

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

 

Basic concepts of neural networks – Towards Data Science

After presenting, in two previous posts, the factors that have contributed to unleashing the potential of Artificial Intelligence and related technologies as Deep Learning, now is time to start to review the basic concepts of neural networks.

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

 

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

If you are a doctor in the U.S., chances are you spend a lot of time doing paperwork — sometimes double the amount of time you spend seeing patients. “Physicians today spend an inordinate amount of time, a tremendous amount of time, looking at a screen because they are required to capture documentation for a visit with a patient,” said Harjinder Sandhu, the CEO and founder of Saykara.

Saykara is trying to change that with its AI-powered voice assistant, and the company just raised $5 million to help scale its technology. Another nugget of information the company revealed: Physicians using SayKara cut down their paperwork time by 70 percent.
2018-09-13 16:15:50-07:00 Read the full story.

 

Adding emotional intelligence to artificial intelligence

While no one can deny AI is having an enormous effect on the finance industry, there are still limitations to the technology, especially in terms of its ability to react to emotional cues. Your customer’s emotions are an extremely important part of a business relationship, and the ability to read and comprehend these signals plays a huge part in tailoring the customer experience.

Chatbots can’t easily detect a shift in tone or tension in a conversation and aren’t able to quickly appease a customer, and this is where the emotional intelligence is needed. For example, while a robo-advisor is great for an inexpensive and basic service, the issue comes when you have a more challenging or unique financial situation. A human advisor is best served here as they have the ability to account for personal needs or complex and sensitive circumstances, for example debt or divorce. This is where human employees are still required, to maintain customer relationships and avoid frustrating customers – or causing them to take their business elsewhere.
2018-09-12 00:00:00 Read the full story.

 

Intake: Caching Data on First Read Makes Future Analysis Faster

Intake provides easy access data sources from remote/cloud storage. However, for large files, the cost of downloading files every time data is read can be extremely high. To overcome this obstacle, we have developed a “download once, read many times” caching strategy to store and manage data sources on the local file system.

How It Works : Intake catalogs are composed of data sources, which contain attributes describing how to load data files. To enable caching, catalog authors can now specify caching parameters that will be used to download data file(s), store them in a configured local directory, and return their location on local disk.
2018-09-10 11:37:37-05:00 Read the full story.

 

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

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

 

5 Takeaways From Mark Zuckerberg’s Security Manifesto

Facebook Inc. (FB) co-founder and CEO Mark Zuckerberg has pledged to write a series of lengthy blog posts explaining how the social network is working to reduce the amount of divisive messages, propaganda and fake news engulfing its website. On Wednesday, Zuckerberg published his first post in the series, a roughly 3,270 word insight into how Facebook is progressing towards its goal to protect its website from election interference.

Humans and Machines Blocking Fake Accounts : Zuckerberg revealed that Facebook hired more than 10,000 extra people this year and has been building systems based on advancements in machine learning to block millions of fake accounts every day. While admitting that “these systems will never be perfect,” he added that Facebook’s automated technology and 20,000-plus workforce are beginning to have a positive impact.
2018-09-13 04:35:00-06:00 Read the full story.

 

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

Google today announced the integration of Sound Search into Now Playing to vastly improve its ability to recognize songs on Android phones. Now Playing, which was introduced last year, uses on-device machine learning to recognize tens of thousands of popular songs. Sound Search, which predates Now Playing but was incorporated into the tool today, can identify tens of millions of songs.

Sound Search operates on servers and is available via a long press of the Google Assistant button on Android phones or in the Google Search app. You can also add a shortcut to your home screen to quickly identify a song.
2018-09-14 00:00:00 Read the full story.

 

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

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

 

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

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

 

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

The Association of Computing Machinery (ACM) has released an update to its Code of Ethics and Professional Conduct geared at computing professionals. The update was done “to address the significant advances in computing technology and the degree [to which] these technologies are integrated into our daily lives,” explained ACM members Catherine Flick and Michael Kirkpatrick, writing in Reddit.

This marks the first update to the Code, which the ACM maintains “expresses the conscience of the profession,” since 1992. The goal is to ensure it “reflects the experiences, values and aspirations of computing professionals around the world,’’ Flick and Kirkpatrick said. The Code was written to guide computing professionals’ ethical conduct and includes anyone using computing technology “in an impactful way.” It also serves as a basis for remediation when violations occur. The Code contains principles developed as statements of responsibility in the belief that “the public good is always the primary consideration.”
2018-09-10 14:21:39+00:00 Read the full story.

 

Weekly Selection — Sep 14, 2018 – Towards Data Science

 

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

2018-09-14 12:48:38.174000+00:00 Read the full story.

 

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

NEW YORK, September 12, 2018 – GFT, global IT engineering and technology specialists for the financial services industry for over 30 years – and Google Cloud services partner, has announced the launch of their GFT Streaming Enterprise Analytics Platform (SEAP) solution powered by Google Cloud. GFT introduced the solution in July at Google Cloud Next ’18 in San Francisco, alongside other industry innovators.

SEAP provides real-time data ingestion and processing to support the consumption of actionable insights. With the ability to ingest any type of unstructured data, in a streaming format, the platform enables inflow analytics through use of AI and machine learning technology. Powered by Google Cloud technology, the on-premise design utilizes the latest big data streaming technology and can migrate readily for a cloud implementation.
2018-09-12 00:00:00 Read the full story.

 


Behind Paywall or Data Farmers….

2018 NEXT-GENERATION DATA DEPLOYMENT STRATEGIES REPORT

Download the “Next-Generation Data Deployment Strategies” report to learn about the current state of machine learning, data lakes, Hadoop, Spark, object storage and more! Don’t miss out on these new insights about the hottest technology trends today.
2018-09-13 00:00:00 Read the full story.
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Citigroup has found a new way to offer hedge funds obscure data that can give them an edge — and it’s part of a $2 billion investing gold rush

Alternative data sets aren’t so alternative anymore. At least according to Citigroup, which last week started giving clients access to data collected and analyzed by Thinknum, a four-year old startup which provides insights into a company’s health that aren’t readily available from conventional sources like financial reports and economic indicators.

The banking giant becomes one of the first on Wall Street to gi…
2018-09-15 00:00:00 Read the full story.
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