There aren't many ideas that unite trade unionists, the libertarian right, the green movement, and the Silicon Valley tech scene . Is its surprising coalition of bedfellows a sign of an idea whose time has at last resoundingly come – or a symptom of a catch-all, superficial fix in search of a problem? Universal basic income, sometimes called a citizens' income, is the idea that the state should pay every adult citizen a regular, modest income. It is a no-strings payment, so unlike benefits currently available to people of working age, it is not means tested. You get it regardless of whether you have a job, are looking for work, or whether you are even willing to work.

It's a long way from the basic SQL databases and floppy disks he was using 20 years ago, when the New Zealander helped monitor training for professional rugby, cricket and soccer teams in the U.K. But those grassroots hacks recorded valuable information and led to productive outcomes. They helped him evolve into a modern data scientist. Flash forward a few decades, and Riddle is working with Microsoft's Sports Performance Platform, a data-driven predictive analytics platform that harnesses the power of Microsoft's cloud with state of the art machine-learning technologies, artificial intelligence capabilities and data visualization. "Having that creative space and relaxed nature within a high-pressured environment enables forward thinking," Riddle says.

A recent piece from Angelica Mari of ZDNet shows that the number of Brazilian banks that see artificial intelligence (AI) as a strategic priority is higher than many mature markets. About 30 percent of local institutions in the country are seeing AI playing an important role in their innovation plans, according to GFT Technologies' Digital Banking Expert Survey. By comparison, 23 percent of sector firms in the UK and Mexico see AI as crucial in their strategy, while only 17 percent of US banks perceive the technology as an important aspect of their overall plans, the study from the financial services vendor says. Banking giant Bradesco is the highest profile supporter of AI technology in Brazil and has been piloting IBM's Watson for over a year. Earlier this year, the bank has announced its plans to make its Watson-based artificial intelligence system available to end consumers.

For too long the thrust of AI has been to replace humans. For the last 30 years the media has loved to portray AI as meaning'Machines are better than Humans'; whether it's Arnold Schwarzenegger in the Terminator or Alicia Vikander in Ex Machina. We think this has incorrectly framed the adoption of AI within businesses. It has set unrealistic expectations for how AI will perform, as well as created scaremongering about the large scale loss of jobs. A better framing is realizing that Machines and Humans have complementary capabilities.

We present an algorithm for the generation of prime implicates in equational logic, that is, of the most general consequences of formulæ containing equations and disequations between first-order terms. This algorithm is defined by a calculus that is proved to be correct and complete. We then focus on the case where the considered clause set is ground, i.e., contains no variables, and devise a specialized tree data structure that is designed to efficiently detect and delete redundant implicates. The corresponding algorithms are presented along with their termination and correctness proofs. Finally, an experimental evaluation of this prime implicate generation method is conducted in the ground case, including a comparison with state-of-the-art propositional and first-order prime implicate generation tools.

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding a robust policy while taking into account the impact of environment variables. We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy. Experimental results across different domains show that ALOQ can learn more efficiently and robustly than existing methods.

Learning-based representations have become the defacto means to address computer vision tasks. Despite their massive adoption, the amount of work aiming at understanding the internal representations learned by these models is rather limited. Existing methods aimed at model interpretation either require exhaustive manual inspection of visualizations, or link internal network activations with external "possibly useful" annotated concepts. We propose an intermediate scheme in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered by the model, without requiring additional annotations. We interpret the model through average visualizations of these features. Then, at test time, we explain the network prediction by accompanying the predicted class label with supporting heatmap visualizations derived from the identified relevant features. In addition, we propose a method to address the artifacts introduced by strided operations in deconvnet-based visualizations. Our evaluation on the MNIST, ILSVRC 12 and Fashion 144k datasets quantitatively shows that the proposed method is able to identify relevant internal features for the classes of interest while improving the quality of the produced visualizations.

The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected in this task have been proposed. These methods span from classical performance measurements (e.g. escape latency, rodent speed, quadrant preference) to more sophisticated methods of categorisation which classify the animal swimming path into behavioural classes known as strategies. Classification techniques provide additional insight in relation to the actual animal behaviours but still only a limited amount of studies utilise them mainly because they highly depend on machine learning knowledge. We have previously demonstrated that the animals implement various strategies and by classifying whole trajectories can lead to the loss of important information. In this work, we developed a generalised and robust classification methodology which implements majority voting to boost the classification performance and successfully nullify the need of manual tuning. Based on this framework, we built a complete software, capable of performing the full analysis described in this paper. The software provides an easy to use graphical user interface (GUI) through which users can enter their trajectory data, segment and label them and finally generate reports and figures of the results.

Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *omics data) together with current advances in artificial intelligence/machine learning enable to make novel information accessible and quantifiable to a human expert, which is not yet available and not exploited in current medical settings. The grand goal is to reach a level of usable intelligence to understand the data in the context of an application task, thereby making machine decisions transparent, interpretable and explainable. The foundation of such an "augmented pathologist" needs an integrated approach: While machine learning algorithms require many thousands of training examples, a human expert is often confronted with only a few data points. Interestingly, humans can learn from such few examples and are able to instantly interpret complex patterns. Consequently, the grand goal is to combine the possibilities of artificial intelligence with human intelligence and to find a well-suited balance between them to enable what neither of them could do on their own. This can raise the quality of education, diagnosis, prognosis and prediction of cancer and other diseases. In this paper we describe some (incomplete) research issues which we believe should be addressed in an integrated and concerted effort for paving the way towards the augmented pathologist.

Achieving artificial visual reasoning -- the ability to answer image-related questions which require a multi-step, high-level process -- is an important step towards artificial general intelligence. This multi-modal task requires learning a questiondependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are handcrafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-ofthe-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively. Index Terms: Deep Learning, Language and Vision Note: A full paper extending this study is available at http: //arxiv.org/abs/1709.07871, with additional references, experiments, and analysis.