Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in Artificial Intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and of difficult solution nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence.

Deep learning and machine learning tailored toward a specific Next to convex optimization, contributed were hot topics, and the workshop application. It is now recognized that papers addressed the problems included papers from across the globe formal languages, and their symbolic of symbolic stochastic planning on deep reinforcement learning agents underpinnings, can enable descriptive and shortest path problems.

This article discusses verification and validation (V&V) of autonomous systems, a concept that will prove to be difficult for systems that were designed to execute decision initiative. V&V of such systems should include evaluations of the trustworthiness of the system based on transparency inputs and scenario-based training. Transparency facets should be used to establish shared awareness and shared intent between the designer, tester, and user of the system. The transparency facets will allow the human to understand the goals, social intent, contextual awareness, task limitations, analytical underpinnings, and team-based orientation of the system in an attempt to verify its trustworthiness. Scenario-based training can then be used to validate that programming in a variety of situations that test the behavioral repertoire of the system. This novel method should be used to analyze behavioral adherence to a set of governing principles coded into the system.

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About this course: You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.

Part of the elemental appeal of zombie fiction is the permission it provides to imagine which household item, when pressed, you might use to stove in the face of a lunging, undead version of Mrs Brown from No 37. In the glare of such an apocalypse, familiar domestic items such as tea towels, cafetieres and loo brushes must be reappraised, their value now dependent on their ability to cause brain damage rather than efficiently dry a plate, deliver coffee, or clean the glum residue from a toilet bowl. Do you reach for the bread knife (rasping, noble), or the biro (intimate, cruel)? The 17-year-old film Battle Royale further elevated the premise. In the film a busload of high school students are gassed and delivered to a remote island.

We regularly encounter complex activities consisting of basic skills— both conscious and subconscious. Adequately performing these complex activities involves mastering the individual basic skills and having the ability to seamlessly integrate them together. Games are one such example of a complex activity that is difficult to break down into the basic skills required, but engagement in games relies on designers introducing challenges proportionate to a player's skill. Procedurally generated levels cause additional problems since it is hard to estimate level difficulty for a particular player. This proposal suggests a framework for determining the skills necessary to successfully complete a game, creating AI-based bots with those skills to reflect players with the same skills, and identifying and generating optimal orderings of levels to promote learning each skill of a game. The proposed framework will be implemented in three citizen science games— Paradox , Foldit , and Nanocrafter — and one computer science educational game called GrACE .

Co-creative agents, or artificially intelligent computer agents that can collaborate creatively in real-time with human partners, have proven successful in being both creatively engaging and fun to interact with. Prior research in museum experience design also indicates that due to their incorporation of embodied interaction, creative narrative construction, and personal identity, co-creative agents have potential to drive pre-learning experiences that motivate participants to learn more about technology in museum settings. However, many co-creative agents fall short in effectively communicating technology-related educational outcomes. My work aims to explore how museum experiences involving co-creative agents can be designed and evaluated such that they both foster creative engagement and facilitate pre-learning experiences, using two interactive installation projects (LuminAI and TuneTable) as technical probes.

We consider the problem of using observational data to learn treatment assignment policies that satisfy certain constraints specified by a practitioner, such as budget, fairness, or functional form constraints. This problem has previously been studied in economics, statistics, and computer science, and several regret-consistent methods have been proposed. However, several key analytical components are missing, including a characterization of optimal methods for policy learning, and sharp bounds for minimax regret. In this paper, we derive lower bounds for the minimax regret of policy learning under constraints, and propose a method that attains this bound asymptotically up to a constant factor. Whenever the class of policies under consideration has a bounded Vapnik-Chervonenkis dimension, we show that the problem of minimax-regret policy learning can be asymptotically reduced to first efficiently evaluating how much each candidate policy improves over a randomized baseline, and then maximizing this value estimate. Our analysis relies on uniform generalizations of classical semiparametric efficiency results for average treatment effect estimation, paired with sharp concentration bounds for weighted empirical risk minimization that may be of independent interest.

Love them or hate them, computers are becoming more ingrained in 21st century medical care. And in an increasingly data-driven industry, medical education hasn't kept pace. Physicians might curse their computers for sucking time away from patients or turning them into "data entry clerks," but computers aren't to blame, according to two health policy researchers from Harvard Medical School. As algorithms gradually outperform the human mind, clinicians need to place more emphasis on data science to get the most out of advanced analytics and machine learning that could have a significant impact on medical care care. "Today's medical education system is ill-prepared to meet these needs," Ziad Obermeyer, M.D., and Thomas H. Lee, M.D., who also serves as chief medical officer at Press Ganey, wrote in the New England Journal of Medicine.