Now, researchers from Google DeepMind and Stanford University have updated a theory originally developed to explain how humans and other animals learn. "The evidence seems compelling that the brain has these two kinds of learning systems, and the complementary learning systems theory explains how they complement each other to provide a powerful solution to a key learning problem that faces the brain," explained James McClelland, lead author of the 1995 paper from Stanford University. Components of the neural network architecture that succeeded in achieving human-level performance in a variety of computer games like Space Invaders and Breakout were inspired by complementary learning systems theory. According to DeepMind co-founder Demis Hassabis, "the extended version of the complementary learning systems theory is likely to continue to provide a framework for future research not only in neuroscience but also in the quest to develop Artificial General Intelligence -- our goal at Google DeepMind."

Drawing an accurate sketch of a person's face is an art that is hard for most people to master. But it turns out to be relatively easy for computers. Various programs exist for converting images into line drawings. That often produces a decent start, although these systems can have difficulty with shadows and high contrast. A more promising approach is to use machine-vision algorithms that rely on neural networks to extract features from an image and use these to produce a sketch.

Product shipments of artificial intelligence (AI) systems for vehicles will grow from 7 million in 2015 to 122 million by 2025, which, according to IHS Technology, is a reflection of the automotive industry's growing appetite for AI.

In my opinion, the marriage of the leading professional social network and the world's largest software company demonstrates that we are decidedly at the start of a new era in software, where proprietary data is king, and will start to come bundled together with software. We've seen this rise in the consumer realm, where technology companies are fundamentally aggregating and analyzing user behavior, and providing value back to users (and, of course, advertisers.) There are countless other examples that also demonstrate that consumer technology puts behavioral and user data front and center, in a way that I expect we will start to see from the enterprise as the divide between these two segments starts to collapse. Taken together, this demonstrates that proven machine learning algorithms have both the horsepower and access to granular datasets that are unprecedented.

A tinny voice asks, "Would you like fries with that?" A flashing screen says, "That colour really brings out your eyes." Welcome to our automated future, where more than 40 per cent of Canadian jobs have been replaced by technology. "Our findings show that a significant percentage of Canadian jobs are at a high risk of being replaced by automation over the next 10 to 20 years," Brookfield Institute for Innovation Entrepreneurship executive director Sean Mullin said in a written statement. In a report released Wednesday, the Ryerson University-based institute says that nearly 42 per cent of Canada's workforce is at a high risk of being affected by automation over the next two decades. You can see what the probability of automation in your field is via this interactive chart.

Reading minds is an ability only found in comic book heroes. But new researcher has revealed that computer can now analyse brain scans and work out who a person is thinking about. The AI system can even create a digital portrait of the face in question. Researchers have reconstructed a face after peering into the mind of another by extracting latent face components from neural activity and using machine learning to create digital portraits. Researchers used an innovative form of fMRI pattern analysis to test whether lateral parietal cortex actively represents the contents of memory.

The increasing interest in complex networks research has been a consequence of several intrinsic features of this area, such as the generality of the approach to represent and model virtually any discrete system, and the incorporation of concepts and methods deriving from many areas, from statistical physics to sociology, which are often used in an independent way. Yet, for this same reason, it would be desirable to integrate these various aspects into a more coherent and organic framework, which would imply in several benefits normally allowed by the systematization in science, including the identification of new types of problems and the cross-fertilization between fields. More specifically, the identification of the main areas to which the concepts frequently used in complex networks can be applied paves the way to adopting and applying a larger set of concepts and methods deriving from those respective areas. Among the several areas that have been used in complex networks research, pattern recognition, optimization, linear algebra, and time series analysis seem to play a more basic and recurrent role. In the present manuscript, we propose a systematic way to integrate the concepts from these diverse areas regarding complex networks research. In order to do so, we start by grouping the multidisciplinary concepts into three main groups, namely features, similarity, and network connectivity. Then we show that several of the analysis and modeling approaches to complex networks can be thought as a composition of maps between these three groups, with emphasis on nine main types of mappings, which are presented and illustrated. Such a systematization of principles and approaches also provides an opportunity to review some of the most closely related works in the literature, which is also developed in this article.

Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km$^2$) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand arises from complex urban geography and exhibits complex spatio-temporal patterns, both of which need to captured and exploited. To address these challenges, we propose three methods based on Gaussian mixture models, kernel density estimation, and kernel warping. These methods provide spatio-temporal predictions for Toronto and Melbourne that are significantly more accurate than the current industry practice.

Regression trees are becoming increasingly popular as omnibus predicting tools and as the basis of numerous modern statistical learning ensembles. Part of their popularity is their ability to create a regression prediction without ever specifying a structure for the mean model. However, the method implicitly assumes homogeneous variance across the entire explanatory-variable space. It is unknown how the algorithm behaves when faced with heteroscedastic data. In this study, we assess the performance of the most popular regression-tree algorithm in a single-variable setting under a very simple step-function model for heteroscedasticity. We use simulation to show that the locations of splits, and hence the ability to accurately predict means, are both adversely influenced by the change in variance. We identify the pruning algorithm as the main concern, although the effects on the splitting algorithm may be meaningful in some applications.

In many healthcare settings, intuitive decision rules for risk stratification can help effective hospital resource allocation. This paper introduces a novel variant of decision tree algorithms that produces a chain of decisions, not a general tree. Our algorithm, $\alpha$-Carving Decision Chain (ACDC), sequentially carves out "pure" subsets of the majority class examples. The resulting chain of decision rules yields a pure subset of the minority class examples. Our approach is particularly effective in exploring large and class-imbalanced health datasets. Moreover, ACDC provides an interactive interpretation in conjunction with visual performance metrics such as Receiver Operating Characteristics curve and Lift chart.