IN THE MODERN FIELD OF ARTIFICIAL INTELLIGENCE, all roads seem to lead to three researchers with ties to Canadian universities. The first, Geoffrey Hinton, a 70-year-old Brit who teaches at the University of Toronto, pioneered the subfield called deep learning that has become synonymous with A.I. The second, a 57-year-old Frenchman named Yann LeCun, worked in Hinton's lab in the 1980s and now teaches at New York University. The third, 54-year-old Yoshua Bengio, was born in Paris, raised in Montreal, and now teaches at the University of Montreal. The three men are close friends and collaborators, so much so that people in the A.I. community call them the Canadian Mafia. In 2013, though, Google recruited Hinton, and Facebook hired LeCun. Both men kept their academic positions and continued teaching, but Bengio, who had built one of the world's best A.I. programs at the University of Montreal, came to be seen as the last academic purist standing. Bengio is not a natural industrialist. He has a humble, almost apologetic, manner, with the slightly stooped bearing of a man who spends a great deal of time in front of computer screens.

IBM is testing a computer against two human debaters in the first public demonstration of artificial intelligence technology it's been working on for more than five years. The company unveiled its Project Debater in San Francisco on Monday. The argumentative computer system is embodied in a 5-foot-tall (1.5 meter) machine shaped like a monolith. Asked to debate in favor of government-subsidized space exploration -- a topic it hadn't studied -- the computer quickly delivered an opening argument, pulling in evidence collected from its repository of newspaper articles and journals. It then listened to a human's counter-argument and gave a 4-minute rebuttal.

IBM pitted a computer against two human debaters in the first public demonstration of artificial intelligence technology it's been working on for more than five years. The company unveiled its Project Debater in San Francisco on Monday, asking it to make a case for government-subsidized space research -- a topic it hadn't studied in advance but championed fiercely with just a few awkward gaps in reasoning. "Subsidizing space exploration is like investing in really good tires," argued the computer system, its female voice embodied in a 5-foot-tall machine shaped like a monolith with TV screens on its sides. Such research would enrich the human mind, inspire young people and be a "very sound investment," it said, making it more important even than good roads, schools or health care. The computer delivered its opening argument by pulling in evidence from its huge internal repository of newspapers, journals and other sources.

IBM pitted a computer against two human debaters in the first public demonstration of artificial intelligence technology it's been working on for more than five years. The company unveiled its Project Debater in San Francisco on Monday, asking it to make a case for government-subsidized space research -- a topic it hadn't studied in advance but championed fiercely with just a few awkward gaps in reasoning. "Subsidizing space exploration is like investing in really good tires," argued the computer system, its female voice embodied in a 5-foot-tall machine shaped like a monolith with TV screens on its sides. Such research would enrich the human mind, inspire young people and be a "very sound investment," it said, making it more important even than good roads, schools or health care. The computer delivered its opening argument by pulling in evidence from its huge internal repository of newspapers, journals and other sources.

An image from a presentaton by Amazon's Ranju Das shows a demonstration of real-time facial recognition and tracking. Das said the video came from a traffic cam in Orlando, where police were in a pilot program of Amazon's Rekognition service. An image from a presentaton by Amazon's Ranju Das shows a demonstration of real-time facial recognition and tracking. Das said the video came from a traffic cam in Orlando, where police were in a pilot program of Amazon's Rekognition service. The city of Orlando, Fla., says it has ended a pilot program in which its police force used Amazon's real-time facial recognition – a system called "Rekognition" that had triggered complaints from rights and privacy groups when its use was revealed earlier this year.

Otherworldly and endearing, Celeste follows a pixel-art character up a mountain, encouraging you through its fiendish, treacherous areas with affirmations and optimism. We said: "A tough game with an uplifting message: don't let the voice inside your head tell you that you can't do things." A much-celebrated and enduringly mysterious dark fantasy game is given a new lick of paint, which further highlights its brilliance. Dark Souls requires courage and perseverance – it's a world where all manner of hideous things are trying to end you – but the rewards are immense. We said: "If a new kind of adventure appeals, one in which quick fingers matter less than brains and human cunning, there's still nothing like Dark Souls."

Ryan Calo's "law and Technology" Viewpoint "Is the Law Ready for Driverless Cars?" (May 2018) explored the implications, as Calo said, of " ... genuinely unforeseeable categories of harm" in potential liability cases where death or injury is caused by a driverless car. He argued that common law would take care of most other legal issues involving artificial intelligence in driverless cars, apart from such "foreseeability." Calo also said the courts have worked out problems like AI before and seemed confident that AI foreseeability will eventually be accommodated. One can agree with this overall judgment but question the time horizon. AI may be quite different from anything the courts have seen or judged before for many reasons, as the technology is indeed designed to someday make its own decisions.

I am only a layman in the neural network space so the ideas and opinions in this column are sure to be refined by comments from more knowledgeable readers. The recent successes of multilayer neural networks have made headlines. Much earlier work on what I imagine to be single-layer networks proved to have limitations. Indeed, the famous book, Perceptrons,a by Turing laureate Marvin Minsky and his colleague Seymour Papert put the kibosh (that's a technical term) on further research in this space for some time. Among the most visible signs of advancement in this arena is the success of the DeepMind AlphaGo multilayer neural network that beat the international grand Go champion, Lee Sedol, four games out of five in March 2016 in Seoul.b

Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.

When webpages influence choice: Effects of visual primes on experts and novices.