The focus of the research is with the application of deep-learning methods to produce an automated algorithm designed to detect diabetic retinopathy from the scanning of images of the eye. The technology is a type of'deep learning'. Deep Learning is a new area of machine learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: 'true' Artificial Intelligence. The algorithm is based on more than 75,000 images, taken from the back of the eye, and relating to a wide range of patients representing several ethnicities. The images included a mix of health patients and those with the condition.

When you first heard of the term convolutional neural networks, you may have thought of something related to neuroscience or biology, and you would be right.

Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning -- as well as ensemble modeling, which uses a combination of approaches techniques, and semi-supervised learning, which combines supervised and unsupervised approaches. While it's not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields. By extracting high-level, complex abstractions as data representations through a hierarchical learning process, deep learning models yield results more quickly than standard machine learning approaches. Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach.

While automakers are still negotiating with local and state governments to let autonomous cars test drive on open streets, one programmer has found a more accessible proving ground to teach AI how to be a motorist: Grand Theft Auto V. It's not the first time folks have used the game to train their self-driving vehicles -- but you can watch this one learn in real-time on Twitch. Programmer Harrison'Sentdex' Kinsley created the AI (or "convolutional neural network"), named it Charles, and set it loose in the game to teach itself through deep learning. While that sounds advanced, so far Charles hasn't quite mastered avoiding collisions with cars, dividers, signs and people. If this AI hit the road today, it would have some real-life police after it quickly -- so long as it didn't hurl itself into the water first (a frequent fate on the livestream). As Kinsley describes in the Twitch description, Charles "learns and takes all actions based on single frames at a time, and bases his decisions on just pixel data. Charles only sees exactly what you see."

Message from the world's best StarCraft players to the world's most advanced AI: bring it on. The space-war computer game is widely regarded as the ultimate challenge for AI programs due to its complexity and rapid pace. Expectations for a match-up between a professional StarCraft player and sophisticated AI ratcheted up last year after an AI program beat a highly ranked human player at Go, one of the world's most difficult board games. At the time, a number of AI experts pointed to StarCraft as the next target for an AI-versus-man showdown. Among them: Demis Hassabis, the founder and CEO of DeepMind, the AI-focused division of Alphabet that created the triumphant Go-playing AI program, AlphaGo.

Last year Intel became a Python distributor, offering its own edition of the language outfitted with Intel's Math Kernel Library (MKL). MKL accelerates data-science-related tasks by using Intel-specific processor extensions to speed up certain operations, a fine fit for a language that has become a staple in machine learning and math-and-stats circles. The Intel Distribution of Python, a repackaging of Continuum Analytics's Anaconda distribution, incorporated MKL support to give Python data science and machine learning packages a boost. Now ActiveState, producers of an enterprise-grade Python, (as well as Ruby, Node.js, and Golang distributions) has brought MKL into its own Python distro. Get a digest of the day's top tech stories in the InfoWorld Daily newsletter.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

The answer to the question of what makes deep learning different from traditional machine learning may have a lot to do with how much data you're working with. "When you start getting into true big data, that's when you can really get into deep learning," said Alfred Essa, vice president of research and data science at New York-based publishing company McGraw-Hill Education. Driven by advances in analytics technologies, deep learning processes became a more widely discussed topic last year. Since then, what constitutes deep learning vs. machine learning has been up for debate. They involve a lot of the same tools and techniques, after all.

As with most recent developments in AI, the web erupted with outlandish storylines. Many described the move as bold despite the fact that (Torch), which is maintained by Ronan Collobert of Facebook AI Research, already offers categorically similar open-source deep learning tools and that Yoshua Bengio's lab has long maintained Theano, the revolutionary software package which pioneered the category in the first place, making deep learning easy for the masses. In an article at Wired, Cade Metz described TensorFlow as Google's "Artificial Intelligence Engine". Even this headline stands out as hyperbolic for an article describing an open-source library for performing linear algebra and taking derivatives. A number of other news outlets marveled that Google made the code open source.

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions. In this work we present our results on learning strategies in Atari games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0 machine learning framework. We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.