Algorithms are now used throughout the public and private sectors, informing decisions on everything from education and employment to criminal justice. But despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societalrisks posed by over-reliance on these systems and work to hold themaccountable. Various industry efforts, including a consortium of Silicon Valley behemoths, are beginning to grapple with the ethics of deploying algorithms that can have unanticipated effects on society. Algorithm developers and product managers need new ways to think about, design, and implement algorithmic systems in publicly accountable ways. Over the past several months, we and some colleagues have been trying to address these goals by crafting a set of principles for accountable algorithms.

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us? The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely won't see machines "exhibit broadly-applicable intelligence comparable to or exceeding that of humans," though it does go on to say that in the coming years, "machines will reach and exceed human performance on more and more tasks."

Artificial intelligence can be an important tool for collecting as well as interpreting business related data. Currently around 45 companies specialize in AI for functions such as recommendations, search, multichannel marketing, merchandising, and conversational commerce. Nothing works better for accumulating and making sense of all the data than machine learning, or AI. When automating aspects of a retail business, AI is able to provide an intelligent as well as a speedy solution, including self-adapting algorithms which can show a company's patterns of behavior which would otherwise be hidden from humans reading the data on their own. From there you have a starting point for predicting patterns of behavior and interaction which drive the most customer interaction, or purchasing.

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."

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.