This week YouTube has updated its mobile app for both iPhone and Android to provide users with a way better Home experience, with a cleaner format and thumbnails for recommended videos. But it's not just a simple redesign that makes Home special, YouTube has curated the videos it will now recommend for you based on what it calls a deep neural network technology. This means the recommendation system is built to find patterns when it comes to the content you watch regularly and learn from them in the future. Which means that (in theory) it'll always be serving up the best content, and changing its mind as you change your interests. YouTube claims that those who have already tried the new recommendation system love the mix of new content from new faces and fresh content from creators they already know and love. You can try it for yourself now, as the update should be rolling out to iPhone and Android users now.

RE•WORK will host it's annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. Professor Bengio is also Head of the Machine Learning Laboratory, Co-director of the CIFAR Neural Computation & Adaptive Perception program, and editor of many prestigious machine learning publications. Daniel's work has been reported on in The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine.

"What is the ape to a man? And so shall man be to the Übermensch." Years ago, as a teenager in France, I used to visit a family friend, Monsieur de la Place. He lived with his wife in a high-rise in a small town outside of Paris. An old man, one side of his body had been paralyzed by a stroke. He was very educated and profound, and became a mentor of sorts to me for a time.

Dignity Health, announced results from a randomized controlled study which demonstrated that the use of digital health technology improved asthma control. The study, "Effectiveness of Population Health Management Using the Propeller Health Asthma Platform: A Randomized Clinical Trial," was recently published in the Journal of Allergy and Clinical Immunology: In Practice. The Propeller Health Asthma Platform utilizes sensors, mobile applications, and analytics to monitor short-acting?-agonist

Global logistics provider DHL believes worldwide supply chains are beginning to undergo a fundamental transformation as more "artificial intelligence" is deployed to handle both the domestic and international movement of goods According to research conducted in support of its recent 2016 Logistics Trend Radar, DHL thinks the impact of data-driven and autonomous supply chains provides an opportunity for "previously unimaginable levels of optimization" in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade, despite high set-up costs deterring early adoption within the logistics industry. Matthias Heutger, senior vice president for strategy, marketing & innovation at DHL, said in a statement that 15 of the 26 "key trends" identified in the company's annual trend radar report "are likely to make an impact in under five years" and thus bear careful watching by the global logistics industry. While the "Internet of Things" or "IoT" will also play a large role in more "intelligent supply chains" as well – a trend DHL noted in its trend report last year – security concerns regarding hacking, among other issues, is slowing down its adoption. IoT offers the potential to connect virtually anything to the Internet and accelerate data-driven logistics, DHL stressed; estimating that by 2020, more than 50 billion objects will be connected to the Internet, presenting an "immense" 1.9 trillion opportunity in logistics, by its reckoning. "Only a few logistics [IoT] applications with substantial business impact have materialized so far," DHL noted in its report. "This is largely due to a shortage of standards in the industry, security concerns, and the fact that recent IoT innovations have mainly been developed for the consumer market.

Subscribe to Datacenter today and access the 200 Leading National Advertisers Report, ranking the nation's 200 biggest ad spenders and more. With all the buzz about artificial intelligence and the launch of the Facebook bot marketplace, I thought it might be helpful to look at the potential implications for marketers over the next 12 months and provide a relatively jargon-free look at some of the near-term opportunities and tips on how to start to prepare for them. At a high level, it's about using AI to power more direct interactions between brands and people, focusing on faster, smarter responses to their questions and needs. Yes, Facebook has released the bot store and messenger maps will become the primary channel for digital customer service and inquires. Facebook is not the only one -- Microsoft has a bot API in Skype and other popular messaging apps such as Kik have also released functionality.

The trucking industry has a driver problem. The job pays well and doesn't require a college degree, but the long hours and lonely stretches on the road make it a tough way of life. That's why the long haul trucking market sees an annual labor turnover rate of more than 90 percent, according to Bob Costello, chief economist at the American Trucking Association. The industry estimates it will need to hire a total of 890,000 new truck drivers over the next decade. The driver shortage is a problem for everyone, because trucking's crucial to the American economy.

Data preprocessing is an umbrella term that covers an array of operations data scientists will use to get their data into a form more appropriate for what they want to do with it. For example, before performing sentiment analysis of twitter data, you may want to strip out any html tags, white spaces, expand abbreviations and split the tweets into lists of the words they contain. When analyzing spatial data you may scale it so that it is unit-independent, that is, so that your algorithm doesn't care whether the original measurements were in miles or centimeters. However, preprocessing data does not occur in a vacuum. This is just to say that preprocessing is a means to an end and there are no hard and fast rules: there are standard practices, as we shall see, and you can develop an intuition for what will work but, in the end, preprocessing is generally part of a results-oriented pipeline and its performance needs to be judged in context.

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective.

Machine translation (MT) systems such as Google Translate have become part of our daily life. But how do they work? In this talk, I'll explain how these systems are built. In the first part of my talk, I'll present a general overview of the field and the key ideas driving modern MT systems. In the second part, I'll dig deeper into the statistical techniques used to estimate translation models from data, and discuss some of the current hot topics in the field."