Los Angeles Rams running back Todd Gurley reportedly has been diagnosed with arthritis in his left knee, according to Jeff Howe of theathletic.com. Gurley was used very sparingly in the 2018 playoffs, especially after the divisional round game against Dallas. The diagnosis could explain the reasoning for his lack of carries during the Rams' playoff run, which ended against the Patriots in the Super Bowl. His knee issues started earlier in the season, when he was forced to miss the final two games against Arizona and San Francisco. Back in December, the Rams said it was just soreness and inflammation.

Training an artificial intelligence (AI) algorithm requires data--lots of data. But staying GDPR-compliant while acquiring that data can be almost impossible. Here's the problem: To make a decision about someone--e.g., that they like the color blue and should be targeted with blue advertisements--an AI algorithm combines their personal data with other data inside its big black box, and spits out the answer. To get the data the AI needs, GDPR requires companies to get consent to use that personal data, tell that person exactly what it's being used for, and guarantee it won't be used for anything else. But companies have no idea what's happening inside that black box, so true consent becomes a myth. Article 22 of GDPR complicates the issue by giving consumers the right to not have an automated process make a decision about them that has legal affects or otherwise "significantly effects them."

In abstract argumentation, multiple argumentation semantics have been proposed that allow to select sets of jointly acceptable arguments from a given argumentation framework, i.e. based only on the attack relation between arguments. The existence of multiple argumentation semantics raises the question which of these semantics predicts best how humans evaluate arguments. Previous empirical cognitive studies that have tested how humans evaluate sets of arguments depending on the attack relation between them have been limited to a small set of very simple argumentation frameworks, so that some semantics studied in the literature could not be meaningfully distinguished by these studies. In this paper we report on an empirical cognitive study that overcomes these limitations by taking into consideration twelve argumentation frameworks of three to eight arguments each. These argumentation frameworks were mostly more complex than the argumentation frameworks considered in previous studies. All twelve argumentation framework were systematically instantiated with natural language arguments based on a certain fictional scenario, and participants were shown both the natural language arguments and a graphical depiction of the attack relation between them. Our data shows that grounded and CF2 semantics were the best predictors of human argument evaluation. A detailed analysis revealed that part of the participants chose a cognitively simpler strategy that is predicted very well by grounded semantics, while another part of the participants chose a cognitively more demanding strategy that is mostly predicted well by CF2 semantics.

One of the challenges of using artificial intelligence solutions in the enterprise is that the technology operates in what is commonly referred to as a black box. Often, artificial intelligence (AI) applications employ neural networks that produce results using algorithms with a complexity level that only computers can make sense of. In other instances, AI vendors will not reveal how their AI works. In either case, when conventional AI produces a decision, human end users don't know how it arrived at its conclusions. This black box can pose a significant obstacle.

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.

Most work on interpretability in machine learning has focused on designing either inherently interpretable models, that typically trade-off interpretability for accuracy, or post-hoc explanation systems, that lack guarantees about their explanation quality. We propose an alternative to these approaches by directly regularizing a black-box model for interpretability at training time. Our approach explicitly connects three key aspects of interpretable machine learning: the model's innate explainability, the explanation system used at test time, and the metrics that measure explanation quality. Our regularization results in substantial (up to orders of magnitude) improvement in terms of explanation fidelity and stability metrics across a range of datasets, models, and black-box explanation systems. Remarkably, our regularizers also slightly improve predictive accuracy on average across the nine datasets we consider. Further, we show that the benefits of our novel regularizers on explanation quality provably generalize to unseen test points.

In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem. We generate NL explanations comprising of the evidence to support the answer to a question asked to an image using two sources of information: (a) annotations of entities in an image (e.g., object labels, region descriptions, relation phrases) generated from the scene graph of the image, and (b) the attention map generated by a VQA model when answering the question. We show how combining the visual attention map with the NL representation of relevant scene graph entities, carefully selected using a language model, can give reasonable textual explanations without the need of any additional collected data (explanation captions, etc). We run our algorithms on the Visual Genome (VG) dataset and conduct internal user-studies to demonstrate the efficacy of our approach over a strong baseline. We have also released a live web demo showcasing our VQA and textual explanation generation using scene graphs and visual attention.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

Artificial intelligence (AI) is expanding beyond academics and the web giants, who have immense access to data and computing power and deep pockets to fund research projects. There is a lot of hype, but companies are being urged to embrace AI. Two studies published recently have emphasised the importance of businesses adopting AI to remain competitive. The Let's get real about AI study from OC&C consultants reported that spending on AI has been huge: $219bn was spent by businesses on AI globally in 2018, equivalent to about 7% of the total enterprise IT spend. AI spend in the US was $91bn in 2018, and $12bn in the UK.