Get your Minority Report or Skynet references ready, researchers at the University of Cambridge are working on a way to use AI and drone surveillance to spot violent behavior in crowds. The research paper even has a cool sci-fi name: Eye in the Sky. The project uses an inexpensive Parrot AR 2.0 drone to watch crowds of people from above. Then it uses AI to identify people in violent poses (the paper mentions strangling, punching, kicking, shooting and stabbing). It's important to note that the researchers didn't fly a drone around to detect real violence -- The Verge reports it shot its own video clips using volunteers.

The world could fall behind China when it comes to developing artificial intelligence (AI), the former president of the European Commission warned Monday. Jose Manuel Barroso, now non-executive chairman at Goldman Sachs International, told CNBC's Arjun Kharpal that Beijing is getting on with development in AI. "The Chinese are not only making statements, they are investing money," he said at the AI Deep Dive in partnership with Feedzai at Money2020. Barroso added that his sentiment was not negative toward China and that, in the main, Chinese-led science would benefit the world. The Goldman employee said Europe, in particular, "should not be naive" and must defend itself by ensuring that more money is committed to AI development. He said there was evidence that this was happening with European countries that are net contributors to Brussels being asked to provide 100 billion euros ($117 billion) over the next seven years to scientific research.

Monte-Carlo Tree Search (MCTS) has been found to show weaker play than minimax-based search in some tactical game domains. This is partly due to its highly selective search and averaging value backups, which make it susceptible to traps. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTS-minimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising even without making use of domain knowledge such as heuristic evaluation functions. This article continues this line of research for the case where evaluation functions are available. Three different approaches are considered, employing minimax with an evaluation function in the rollout phase of MCTS, as a replacement for the rollout phase, and as a node prior to bias move selection. The latter two approaches are newly proposed. Furthermore, all three hybrids are enhanced with the help of move ordering and k-best pruning for minimax. Results show that the use of enhanced minimax for computing node priors results in the strongest MCTS-minimax hybrid investigated in the three test domains of Othello, Breakthrough, and Catch the Lion. This hybrid, called MCTS-IP-M-k, also outperforms enhanced minimax as a standalone player in Breakthrough, demonstrating that at least in this domain, MCTS and minimax can be combined to an algorithm stronger than its parts. Using enhanced minimax for computing node priors is therefore a promising new technique for integrating domain knowledge into an MCTS framework.

We address the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analytically in the case of two populations, with a uniform bonus-malus on the zones where each population is a majority. We then address the general case of n populations, where the entanglement of populations does not allow a similar analytical solution. We show that an approximate solution with an arbitrarily high level of precision can be computed with linear programming. Finally, we address the inverse problem where the error should not go beyond a certain value and we seek to minimize the discrimination.

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.

This document contains the outcome of the first Human behaviour and machine intelligence (HUMAINT) workshop that took place 5-6 March 2018 in Barcelona, Spain. The workshop was organized in the context of a new research programme at the Centre for Advanced Studies, Joint Research Centre of the European Commission, which focuses on studying the potential impact of artificial intelligence on human behaviour. The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs. The document is made of short position statements and identification of challenges provided by each expert, and incorporates the result of the discussions carried out during the workshop. In the conclusion section, we provide a list of emerging research topics and strategies to be addressed in the near future.

In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective. Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious. For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face. We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment. By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving. We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object. Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.

This problem is faced in many applications such as link prediction, community detection, node classification, and network visualization, among others. Now, it has been shown that the standard shortest path distance and the resistance distance (Klein & Randić, 1993) suffer from important drawbacks in some situations, which sometimes hinders their use as distance measures between nodes in some applications. More precisely, the shortest path distance does not integrate the concept of high connectivity between the two nodes (it only considers the shortest paths, see, e.g., (Fouss et al., 2016)), while the resistance distance provides useless results when dealing with large graphs (the so-called "lost-in-space effect" (von Luxburg, Radl, & Hein, 2010, 2014)). Another drawback of the shortest path distance is that it provides lots of ties when comparing distances, especially on unweighted undirected graphs. In order to avoid the drawbacks of the shortest path and resistance distances, new families of distance measures, interpolating between these two extremes, were recently suggested based on a bag-of-paths (BoP) framework (Françoisse et al., 2017; Kivimäki, Shimbo, & Saerens, 2014; Mantrach et al., 2010). This framework defines a Gibbs-Boltzmann probability distribution over paths on a graph, which focuses on the shortest paths, but spreads also on longer paths and random walks.

Dimensionality reduction is one of the key issues in the design of effective machine learning methods for automatic induction. In this work, we introduce recursive maxima hunting (RMH) for variable selection in classification problems with functional data. In this context, variable selection techniques are especially attractive because they reduce the dimensionality, facilitate the interpretation and can improve the accuracy of the predictive models. The method, which is a recursive extension of maxima hunting (MH), performs variable selection by identifying the maxima of a relevance function, which measures the strength of the correlation of the predictor functional variable with the class label. At each stage, the information associated with the selected variable is removed by subtracting the conditional expectation of the process. The results of an extensive empirical evaluation are used to illustrate that, in the problems investigated, RMH has comparable or higher predictive accuracy than the standard dimensionality reduction techniques, such as PCA and PLS, and state-of-the-art feature selection methods for functional data, such as maxima hunting.

We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what is actually observed, and outputs a completed vector. The discriminator (D) then takes a completed vector and attempts to determine which components were actually observed and which were imputed. To ensure that D forces G to learn the desired distribution, we provide D with some additional information in the form of a hint vector. The hint reveals to D partial information about the missingness of the original sample, which is used by D to focus its attention on the imputation quality of particular components. This hint ensures that G does in fact learn to generate according to the true data distribution. We tested our method on various datasets and found that GAIN significantly outperforms state-of-the-art imputation methods.