Collaborative Filtering (CF) is one of the most commonly used recommendation methods. CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users. In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse. Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics. However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.). These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise. For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions. In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality. Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms.

Roderick Bloem TU Graz Austria A prominent problem in artificial intelligence and machine learning is the safe exploration of an environment. In particular, reinforcement learning is a wellknown technique to determine optimal policies for complicated dynamic systems, but suffers from the fact that such policies may induce harmful behavior. We present the concept of a shield that forces decision-making to provably adhere to safety requirements with high probability. Our method exploits the inherent uncertainties in scenarios given by Markov decision processes. We present a method to compute probabilities of decision making regarding temporal logic constraints. We use that information to realize a shield that--when applied to a reinforcement learning algorithm--ensures (near-)optimal behavior both for the safety constraints and for the actual learning objective. In our experiments, we show on the arcade game PAC-MAN that the learning efficiency increases as the learning needs orders of magnitude fewer episodes. We show tradeoffs between sufficient progress in exploration of the environment and ensuring strict safety.

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and propose a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We demonstrate that this approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.

We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size of the resulting Horn formula in the worst case. We recall a well-known polynomial-time algorithm for learning Horn formulas with membership and equivalence queries and modify it to obtain a polynomial-time probably approximately correct algorithm for learning the Horn envelope of an arbitrary domain. Keywords: PAC learning, attribute exploration, FCA, formal concept 2010 MSC: 68T27, 06B99 1. Introduction The learnability of concepts from oracle queries has received significant attention in learning theory. The most common types of oracles investigated in the literature are membership and equivalence oracles, and for these types of oracles various results have been obtained showing learnability in polynomial time. One of the most prominent examples is the fact that Horn formulas can be learnt in polynomial time with access to membership and equivalence oracles [1]. In the realm of formal concept analysis [2], a different learning method has been established almost simultaneously with the standard query learning setting. The theory of formal concept analysis emerged as a subfield of mathematical order theory, more precisely of lattice theory, and it studies lattices as hierarchies of concepts. Since its emergence in the early 1980s, it has evolved into a rich theory with a wide range of applications. An important technique of formal concept analysis is the attribute exploration algorithm. A Horn envelope of a theory is a Horn formula whose set of models includes all the models of the theory and is as specific as possible [3].

It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks to deal with decision-making scenarios by incorporating the notion of maximum expected utility in influence diagrams. The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action. This way, we are not proposing a new type of expected utility hypothesis. On the contrary, we are keeping it under its classical definition. We are only incorporating it as an extension of a probabilistic graphical model in a compact graphical representation called an influence diagram in which the utility function depends on the probabilistic influences of the quantum-like Bayesian network. Our findings suggest that the proposed quantum-like influence digram can indeed take advantage of the quantum interference effects of quantum-like Bayesian Networks to maximise the utility of a cooperative behaviour in detriment of a fully rational defect behaviour under the prisoner's dilemma game.

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues; other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.

The best trained soldiers can't fulfill their mission empty-handed. Data scientists have their own weapons -- machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that's why we interviewed data science practitioners -- gurus, really --regarding the useful tools they choose for theirprojects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics).

You are lucky to be in the Artificial Intelligence (AI) enabled network, working on or studying machine learning, data science, business intelligence, or any other AI domains that are buzz words in the cutting-edge technology industry. While AI makes work easy and error free, it is, however, true that automation is a threat and slowly takes away human jobs. Taking the two aspects of the same coin together, the demand for AI talent continues to grow at an accelerating pace. AI and automation are all set to impact nearly all the industries. AI is increasingly being deployed for popular services like customer service chatbots in medicine, retail and banking industries.

Behind Theresa May and her cabinet in your photo (Cabinet crisis, 10 July) is a big painting of Countess Ada Lovelace, mathematical genius and probable inventor of the computer. Good to see Lovelace hung in the Cabinet Office and a sign that she is at last being given the recognition that she and other hitherto forgotten women of science deserve. It had been repainted in silver Hammerite and then left out in the rain, resulting in what could only be described as an interesting paint finish. It was affectionately known as The Silver Blister. Is that terrific, dignified dark-blue masthead really here to stay? Stephen Friar Painswick, Gloucestershire