WIRE)--GNS Healthcare (GNS), a leading precision medicine company that applies causal machine learning technology to massive and diverse data streams to better match drugs and other health interventions to individual patients, today announced that Celgene Corporation has entered into a service and license arrangement for the rights to operate the GNS Healthcare REFS (Reverse Engineering and Forward Simulation) causal machine learning and simulation platform for applications across drug discovery, clinical development, and commercialization and market access. In addition, several GNS causal modeling experts will be brought in-house at Celgene sites to operate the platform. GNS also announced that Celgene has made a second equity investment in GNS. "Companies that embrace data-driven frameworks and culture such as Celgene are gaining a competitive advantage to rapidly generate insights that are simply not possible with any other analytics methodology." This service and license arrangement with embedded GNS employees is a linking of people, process and technology.

Smart phone apps that enable users to easily track their diets have become widespread in the last decade. This has created an opportunity to discover new insights into obesity and weight loss by analyzing the eating habits of the users of such apps. In this paper, we present diet2vec: an approach to modeling latent structure in a massive database of electronic diet journals. Through an iterative contract-and-expand process, our model learns real-valued embeddings of users' diets, as well as embeddings for individual foods and meals. We demonstrate the effectiveness of our approach on a real dataset of 55K users of the popular diet-tracking app LoseIt\footnote{http://www.loseit.com/}. To the best of our knowledge, this is the largest fine-grained diet tracking study in the history of nutrition and obesity research. Our results suggest that diet2vec finds interpretable results at all levels, discovering intuitive representations of foods, meals, and diets.

We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration versus exploitation tradeoffs. In a multiarmed bandit model, players can pick among many arms, and each play of an arm generates an i.i.d. reward from an unknown distribution. The objective is to design a policy that maximizes the expected reward over a time horizon for a single player setting and the sum of expected rewards for the multiplayer setting. In the multiplayer setting, arms may give different rewards to different players. There is no separate channel for coordination among the players. Any attempt at communication is costly and adds to regret. We propose two decentralizable policies, $\tt E^3$ ($\tt E$-$\tt cubed$) and $\tt E^3$-$\tt TS$, that can be used in both single player and multiplayer settings. These policies are shown to yield expected regret that grows at most as O($\log^{1+\epsilon} T$). It is well known that $\log T$ is the lower bound on the rate of growth of regret even in a centralized case. The proposed algorithms improve on prior work where regret grew at O($\log^2 T$). More fundamentally, these policies address the question of additional cost incurred in decentralized online learning, suggesting that there is at most an $\epsilon$-factor cost in terms of order of regret. This solves a problem of relevance in many domains and had been open for a while.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.

There are many Python machine learning resources freely available online. Go from zero to Python machine learning hero in 7 steps! The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best?

A strange vision is gaining traction in the developed world: a vision of a future economy so dramatically changed by technology that it is beyond recognition. This is not the apocalyptic prophecy of a real-world Skynet, turning computerised gadgets and artificial intelligence systems against the human population, but a growing fear of automation replacing people in an increasingly robotised world of work. While it is plain to see how machines have improved humanity's lot in the past despite putting their overlords out of work, some argue that this time, it is different. No doubt, sophisticated IT and AI are likely to consign more jobs to the bin of history than ever before. But some elements of this new techno-pessimism are worryingly short-sighted, and they must be weeded out before they gain more popularity in business and political circles.

Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits. Intel introduces industry's most comprehensive data center compute portfolio for AI: the new Intel Nervana platform. Intel aims to deliver up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU solutions. Intel reinforces commitment to an open AI ecosystem through an array of developer tools built for ease of use and cross-compatibility, laying the foundation for greater innovation. Intel announces AI strategy to drive breakthrough performance, democratize access and maximize societal benefits.

Next time you stop for gas at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated gas pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated gas-delivering robot appeared in 1964 at a station in Westminster, Colorado. Short Stop convenience store owner John Roscoe bought an electric box that let a clerk inside activate any of the pumps outside. Self-serve pumps didn't catch on until the 1970s, when pump-makers added automation that let customers pay at the pump, and over the next 30 years, stations across the nation installed these task-specific robots and fired attendants. By the 2000s, the gas attendant job had all but disappeared.

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.

Not a day goes by when I don't hear another artificial intelligence horror case. If evoking more of a modern and less of a killer machine image is desired, the protagonist in Ex Machina (although no less scary) is selected. The audience is really interested now. Even more critical -- the end of the human race is beckoning! Going back to work is less motivating when you know you'll be replaced by your Roomba in a few years' time.