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Using deductive reasoning, the bot identifies friend or foe to ensure victory over humans in certain online games.

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods...

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause...

Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games...

Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.

AI and Machine Learning are starting to make the Web an even cooler place. Creative, never-before features in websites that are 100% AI-driven is about to be a significant force. Recently, I did a live-stream coding session with Jason Lengstorf’s Learning with Jason, where we detected faces in the…

AI and Machine Learning are starting to make the Web an even cooler place. Creative, never-before features in websites that are 100% AI-driven is about to be a significant force. This article describes some of the cool new possibilities for using JavaScript to train machine learning algorithms in…

Sports enthusiasts who want to follow the games and events carefully cannot stay away from Kodi that gives access to live streaming of sports events, some of which are even free. Round the clock and throughout the world, sports events are taking place, cutting across the time zones, which makes it…

Model quickly generates brain scan templates that represent a given patient population.

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

In this crash course on GANs, we explore where they fit into the pantheon of generative models, how they've changed over time, and what the future has in store for this area of machine learning.

While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI.

How can we make sure new technologies stay centred on human wellbeing?

Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has enabled applications with performance levels approaching those of trained professionals in tasks...

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for...

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is...

Bots are playing an increasingly important role in the creation of knowledge in Wikipedia. In many cases, editors and bots form tightly knit teams. Humans develop bots, argue for their approval, and maintain them, performing tasks such as monitoring activity, merging similar bots, splitting complex...

We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning task, and the other is a simple regularization term such as L1-norm for sparsity. We develop a new online algorithm, the...

Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learning, all based on the fundamental learning-theoretic technique of uniform convergence. While it is well-known that many...

We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. Specifically, we are given a set of labeled examples (x,y) drawn from a distribution D on Rd+1 such that the marginal distribution on the unlabeled points x is arbitrary and the labels...