ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination--New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world. I am the general chair this year, which is light in work but heavy in responsibilities. Markus Weimer also points out the 2016 KDD Cup which has a submission deadline of December 6. KDD Cup datasets have become common reference for many machine learning papers, so this is a good way to get your problem solved well by many people.

The CNTK tutorial is 1 hour during the lunch break of the Optimization workshop while the VW tutorial is 1 hour during the lunch break of the Extreme Multiclass workshop. Consider dropping by either if interested. CNTK is a deep learning system started by the speech people who started the deep learning craze and grown into a more general platform-independent deep learning system. It has various useful features, the most interesting of which is perhaps efficient scalable training. Using GPUs with allreduce and one-bit sgd it achieves both high efficiency and scalability over many more GPUs than could ever fit into a single machine.

If you put water on the stove and heat it up, it will at first just get hotter and hotter. You may then conclude that heating water results only in hotter water. But at some point everything changes – the water starts to boil, turning from hot liquid into steam. Automation, driven by technological progress, has been increasing inexorably for the past several decades. Two schools of economic thinking have for many years been engaged in a debate about the potential effects of automation on jobs, employment and human activity: will new technology spawn mass unemployment, as the robots take jobs away from humans? Or will the jobs robots take over release or unveil – or even create – demand for new human jobs?

The big problem is that reinforcement learning is a difficult technique to characterise. Put simply an RL system learns not by being told how close it is the the desired result, but by receiving rewards based on its behaviour. Of course this is largely how we learn and if it can be made to work efficiently it promises us not just effective AI but new knowledge. For example AlphaGo taught itself to play Go and in the process discovered for itself approaches to Go that humans had ignored.

The bot is just using information that other people have written and is taking it to its logical conclusion. An atheist for instance logically from their perspective can have any standard for morality that the atheist chooses; they can make up their own, because the atheist believes we are the product of random chance and evolution, in which there is no purpose or point. But the google employee is asking what actually is morality, this is a question that can't be answered in an atheist perspective - it's a trick question. The bot is impartial, not an atheist or religious in any way. Morality requires that there be a point to our existence, in which case we would be created by a Creator, not by random chance.

With the spawn of the internet and proliferation of mobile phones, it may seem like technology completely transformed the past two decades. In Alphabet's annual founder's letter, Pichai documented Google's accomplishments, and homed in on the potential of artificial intelligence. Google's AI system AlphaGo recently beat one of the world's best players of the ancient and complex Chinese game of Weiqi, which is better known as Go -- and Pichai was clear that they're not just playing around. "The implications for this victory are, literally, game changing -- and the ultimate winner is humanity," Pichai wrote. "This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels to eventually tackling even bigger challenges like climate change and cancer diagnosis."

Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.

The following interview is one of many included in the report. Brendan Frey is a co-founder of Deep Genomics, a professor at the University of Toronto and a co-founder of its Machine Learning Group, a senior fellow of the Neural Computation program at the Canadian Institute for Advanced Research and a fellow of the Royal Society of Canada. His work focuses on using machine learning to understand the genome and to realize new possibilities in genomic medicine. I completed my Ph.D. with Geoff Hinton in 1997. We co-authored one of the first papers on deep learning, published in Science in 1995.

Google CEO Sundar Pichai shares his vision of the company's future that will focus on machine learning, artificial intelligence, mobile computing and the cloud.

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning. Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R--a cross-platform, zero-cost statistical programming environment--there has never been a better time to start applying machine learning.