NVIDIA Tesla P100 GPUs and Tesla K80 GPUs will be available on Google Cloud Platform, starting early next year. Delivering the power of our Pascal GPU architecture from the cloud gives businesses another great option for helping to put their data to work and build AI services. On Google Cloud Platform, Tesla P100 GPUs will be available to Google Compute Engine and Google Cloud Machine Learning users around the world. The Tesla P100 delivers high performance and efficiency to power the most computationally demanding applications -- including a 12x increase in neural network training performance compared with a previous-generation offering. The Tesla K80 GPU accelerator delivers exceptional performance, with increased throughput that allows researchers to advance their scientific discoveries and developers to boost their web services.

U of T Engineering researchers Wenzhi Guo (ECE MASc 1T5) and Parham Aarabi (ECE) have designed a new machine learning algorithm that may soon enable your smartphone to give you an honest answer based on logic. The algorithm does not learn from an existing set of examples, but rather takes its data directly from human instructions. This methodology resulted in it outperforming conventional methods of training neural networks by a whopping 160 per cent. What is even more surprising is that the algorithm also outperformed its own training by nine per cent. It learned, for example, to recognize hair in pictures with more reliability than that enabled by the training.

Web logs are probably the most common form of big data out there. That makes it pretty important to know how to extract as much information from them as possible, thanks to machine learning. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. The objective is to find correlations or indicators in the data, which are not necessarily visible to the human eye, in order to resolve a defined problem. When the learning process is over, we look at the performance of the models created by the algorithms.

When it comes to machine learning, every performance gain is worth a bit of celebration. That's particularly true for Google's DeepMind division, which has already proven itself by beating a Go world champion, mimicking human speech and cutting down their server power bills. Now, the team has unveiled new "reinforcement learning" methods to speed up how the AI platform trains itself without being directly taught. First off, DeepMind's learning agent has a better grasp of controlling pixels on the screen. Google notes it's "similar to how a baby might learn to control their hands by moving them and observing the movements."

Robotic process automation (RPA) -- a technology that enables software robots to replicate the actions of human workers for tasks such as data entry -- has the potential to dramatically change the way companies approach many of their key business processes. Georgia Tech researchers have created an artificial skin that could give robots a better sense of touch. It also can greatly boost the efficiency of the legacy applications that support those processes, so it should not be surprising that the technology is seeing a rapid adoption rate. "RPA tools are not as sophisticated as the larger market but play a very important role," said Frances Karamouzis, research vice president and analyst at Gartner. One of the benefits of most of the RPA products on the market is that they are quite heterogeneous across lots of other software applications, she says.

A new artificial intelligence startup called Osaro aims to give industrial robots the same turbocharge that DeepMind Technologies gave Atari-playing computer programs. In December 2013, DeepMind showcased a type of artificial intelligence that had mastered seven Atari 2600 games from scratch in a matter of hours, and could outperform some of the best human players. Google swiftly snapped up the London-based company, and the deep-reinforcement learning technology behind it, for a reported $400 million. Now Osaro, with $3.3 million in investments from the likes of Peter Thiel and Jerry Yang, claims to have taken deep-reinforcement learning to the next level, delivering the same superhuman AI performance but over 100 times as fast. Deep-reinforcement learning arose from deep learning, a method of using multiple layers of neural networks to efficiently process and organize mountains of raw data (see "10 Breakthrough Technologies 2013: Deep Learning").

Our team of 11 researchers conduct advanced research in areas such as reinforcement learning, algorithmic game theory, data science and health informatics, among others. Many will recognize our team's contributions to the varied field of machine intelligence. In 2007, we solved checkers, a long-standing challenge problem for AI researchers, and in 2015, we produced the first AI agent capable of playing an essentially-perfect game of heads-up limit hold'em poker. Through the Arcade Learning Environment, we've encouraged researchers around the world to adopt a new challenge problem focused on Atari 2600 games. Outside of AI challenge problems, we recently launched PFM Scheduling Services, which revolutionizes the way scheduling is done in union environments.

Investors believe it is'inevitable' that artificial intelligence will destroy millions of jobs and that governments are unprepared for such an impact, according to a new survey. Artificial intelligence (AI), or the process by which computers or robots take on tasks that need human intelligence, is one of the key themes of this week's Web Summit in Lisbon. The poll among 224 venture capitalists attending the conference showed 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The poll among 224 venture capitalists attending the Web summit in Lisbon found 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. The survey also found that 83 percent of the investors canvassed expect Britain's exit from the European Union to damage Europe's economy and 77 percent believe it will damage British startups.

Amid the wall-to-wall coverage of the U.S. presidential race, it was easy to miss the Obama administration's release this month of a slim, 48-page report titled "Preparing for the Future of Artificial Intelligence." Yet the subject of the report -- and the changes it foreshadows -- may prove to be as consequential for our society, and our education system, as even the most high-stakes national election. The term "artificial intelligence" means different things to different people, but broadly speaking, it refers to computers and advanced machines that can think, reason and communicate like humans, respond to novel or nuanced situations as a person might, and most critically, learn from experiences as a human would. According to a recent survey, 80 percent of AI researchers believe that computers and advanced machines will eventually achieve levels of artificial intelligence that rival human intelligence. Moreover, half believe that this will happen by the year 2040 -- just one generation from now.

Tom Davenport is the President's Distinguished Professor of Information Technology and Management at Babson College, cofounder of the International Institute for Analytics, fellow at the MIT Initiative on the Digital Economy, and senior advisor to Deloitte Analytics. He teaches analytics/big data in executive programs at Babson, Harvard Business School and School of Public Health, and MIT Sloan School. Davenport pioneered the concept of "competing on analytics" with his best-selling 2006 Harvard Business Review article and 2007 book. His most recent book (with Julia Kirby) is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. He wrote or edited 17 other books and over 100 articles for Harvard Business Review, Sloan Management Review, the Financial Times, and many other publications.