Has the technology lived up to its potential? The first time that AlphaGo revealed its full power, it prompted a visceral reaction . Lee Sedol, the world's greatest player of the ancient Chinese board game Go, had grown visibly agitated at the artificial intelligence's prowess. The hushed crowd in downtown Seoul, South Korea, could barely contain its gasps. It was quickly dawning on Lee, and the tens of millions watching at home, that this AI was different to those that had come before. It wasn't just beating Lee, but it was doing so with an almost human-like aptitude.

The University of Toronto and the affiliated Vector Institute for Artificial Intelligence have announced the recruitment of two rising stars in machine learning research as part of a continued drive to assemble the best AI talent in the world. Chris Maddison and Jakob Foerster will both come to U of T having completed their doctoral research at the University of Oxford. He earned his undergraduate and master's degrees in computer science at U of T – the latter under the supervision of University Professor Emeritus Geoffrey Hinton. A senior research scientist at Google-owned AI firm DeepMind, Maddison will join U of T's departments of computer science and statistical sciences in the Faculty of Arts & Science as an assistant professor next summer. Foerster, a research scientist at Facebook AI Research, will start as an assistant professor in the department of computer and mathematical sciences at U of T Scarborough in fall of 2020.

Fortinet today at the Gartner Security & Risk Management Summit 2018 announced it has infused machine learning algorithms and user-behavioral analytics in its web application firewall to identify nearly 100 percent of all cyberthreats. John Maddison, senior vice president of products and solutions for Fortinet, said version 6.0 of the company's FortiWeb Web Application Firewall (WAF) software employs machine learning algorithms to identify both known and unknown threats. That latter capability is enabled by applying algorithms against the user behavior data being collected to identify anomalies indicative of a new, unknown threat being introduced into the IT environment. Historically, WAFs have relied on application learning (AL) to identify anomalies and known threats. But Maddison said that approach generates too many security alerts, which ultimately leads to a state of alert fatigue that makes it easy for cybersecurity professionals to miss or ignore critical information.

So, you don't work at Google Deep Mind and you don't have access to Nature. You've come to the right place. BetaGo lets you run your own Go engine. It downloads Go games for you, preprocesses them, trains a model on data, for instance a neural network using keras, and serves the trained model to an HTML front end, which you can use to play against your own Go bot. It should start a playable demo in your browser!

We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model perturbed with an exponentially many i.i.d. random variables. We propose a novel algorithm that views this as a combinatorial optimization problem and searches for the extreme state using a standard integer linear programming (ILP) solver, appropriately extended to account for the random perturbation. Our technique, GumbelMIP, leverages linear programming (LP) relaxations to evaluate the qualityof samples and prune large portions of the search space, and can thus scale to large tree-width models beyond the reach of current exact inference methods. Further, when the optimization problem is not solved to optimality, our method yields a novel approximate sampling technique. We empirically demonstrate that our approach parallelizes well, our exact sampler scales better than alternative approaches, and our approximate sampler yields better quality samples than a Gibbs sampler and a low-dimensional perturbation method.