Artificial intelligence needs our help. The best AIs are quickly mastering skills from lip-reading to video games, but only by learning through repeated failure. As robots take on riskier domains, like healthcare and driving, this is no longer an acceptable approach. Fortunately, a new study suggests that with the right human oversight, it might be possible to ditch the failures. To try to train an AI without it making a mistake, Owain Evans at the University of Oxford and his colleagues started with the simple two-dimensional table tennis video game Pong. Normally, a Pong-playing agent will let the ball fly past its paddle a few hundred times before realising that isn't a very good way of increasing its score.

Cortana and Alexa just went from being rivals to being besties. Microsoft and Amazon's respective digital assistants are teaming up to work together later this year, the companies surprisingly announced today. That means you'll be able to tap into Alexa's smarts via Cortana on Windows 10 PCs and (further down the line) Microsoft's mobile Cortana apps, or access Cortana via Amazon's Echo devices and Alexa-enabled phones like the HTC U11 and Huawei Mate 9. You'll need to specifically summon the assist, however, by saying "Cortana, open Alexa" or "Alexa, open Cortana." The timing might seem weird with Cortana-powered devices like the Harmon Kardon Invoke speaker launching this fall. But with both digital assistants owning a firm niche--PCs and Office software for Microsoft, smart speakers for Amazon--the collaboration helps them extend their reach without stepping on each other's toes too much.

Richard Anderson, the tall, handsome actor best known for costarring simultaneously in the popular 1970s television shows "The Six Million Dollar Man" and "The Bionic Woman," has died at age 91. Anderson died of natural causes on Thursday, family spokesman Jonathan Taylor told The Associated Press. "The Six Million Dollar Man" brought a new wave of supernatural heroes to television. Based on the novel "Cyborg" by Martin Caidin, it starred Lee Majors as U.S. astronaut Steve Austin, who is severely injured in a crash. The government saves his life by rebuilding his body with atom-powered artificial limbs and other parts, giving him superhuman strength, speed and other powers.

Just as drivers observe the rules of the road, most pedestrians follow certain social codes when navigating a hallway or a crowded thoroughfare: Keep to the right, pass on the left, maintain a respectable berth, and be ready to weave or change course to avoid oncoming obstacles while keeping up a steady walking pace. Now engineers at MIT have designed an autonomous robot with "socially aware navigation," that can keep pace with foot traffic while observing these general codes of pedestrian conduct. In drive tests performed inside MIT's Stata Center, the robot, which resembles a knee-high kiosk on wheels, successfully avoided collisions while keeping up with the average flow of pedestrians. The researchers have detailed their robotic design in a paper that they will present at the IEEE Conference on Intelligent Robots and Systems in September. "Socially aware navigation is a central capability for mobile robots operating in environments that require frequent interactions with pedestrians," says Yu Fan "Steven" Chen, who led the work as a former MIT graduate student and is the lead author of the study.

Imagine if doctors could determine, many years in advance, who is likely to develop dementia. Such prognostic capabilities would give patients and their families time to plan and manage treatment and care. Thanks to artificial intelligence research conducted at McGill University, this kind of predictive power could soon be available to clinicians everywhere. Scientists from the Douglas Mental Health University Institute's Translational Neuroimaging Laboratory at McGill used artificial intelligence techniques and big data to develop an algorithm capable of recognizing the signatures of dementia two years before its onset, using a single amyloid PET scan of the brain of patients at risk of developing Alzheimer's disease. Their findings appear in a new study published in the journal Neurobiology of Aging.

It requires more than just taking online courses or being able to implement papers to get a job in the modern AI industry. After speaking with over 50 top Applied AI teams all over the Bay Area and New York, who come to Insight to find Applied AI practitioners, we have distilled our conversations into a set of actionable items outlined below. If you want to make yourself competitive and break into AI, not only do you have to understand the fundamentals of ML and statistics, but you must push yourself to restructure your ML workflow and leverage best software engineering practices. This means you need to be comfortable with system design, ML module implementation, software testing, integration with data infrastructure, and model serving. Frequent advice for people trying to break into ML or deep learning roles is to pick up the required skills by taking online courses which provide some of the basic elements (e.g.

We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent's explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. This significantly reduces variance in the gradient updates and removes the need for a variance reduction baseline. We show empirical results on two control domains where MAC performs as well as or better than other policy gradient approaches, and on five Atari games, where MAC is competitive with state-of-the-art policy search algorithms.

In this work, we address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training in a straightforward manner. We demonstrate the new method on visual datasets as well as on financial data. In order to evaluate the latter, we developed a hypothetical trading strategy whose performance is affected by the performance of the disentanglement, namely, it trades better when the factors are better separated.

This paper presents convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with misspecified settings where a test integrand is less smooth than a Sobolev RKHS based on which a quadrature rule is constructed. We provide convergence guarantees based on two different assumptions on a quadrature rule: one on quadrature weights, and the other on design points. More precisely, we show that convergence rates can be derived (i) if the sum of absolute weights remains constant (or does not increase quickly), or (ii) if the minimum distance between distance design points does not decrease very quickly. As a consequence of the latter result, we derive a rate of convergence for Bayesian quadrature in misspecified settings. We reveal a condition on design points to make Bayesian quadrature robust to misspecification, and show that, under this condition, it may adaptively achieve the optimal rate of convergence in the Sobolev space of a lesser order (i.e., of the unknown smoothness of a test integrand), under a slightly stronger regularity condition on the integrand.

Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations. This work considers the problem of robust PCA as a nonconvex optimization problem on the manifold of low-rank matrices, and proposes two algorithms (for two versions of retractions) based on manifold optimization. It is shown that, with a proper designed initialization, the proposed algorithms are guaranteed to converge to the underlying low-rank matrix linearly. Compared with a previous work based on the Burer-Monterio decomposition of low-rank matrices, the proposed algorithms reduce the dependence on the conditional number of the underlying low-rank matrix theoretically. Simulations and real data examples confirm the competitive performance of our method.