When I first spotted Shift Technology with their focus on fraud detection for insurance, I assumed I would find a venture in Israel (which is known for smarts in finding the bad guys in cyberspace, as we outlined when we went to Israel on our Fintech global tour). So I was surprised to find that Shift Technology is a Paris based venture. There is a lot more tech innovation in France than the image of economic sclerosis would lead you to assume. The next thing that jumps out at you is that they recently closed a 10m Series A round in a tough market from a top tier VC (Accel Partners). So they must be doing something right.

In a major breakthrough for artificial intelligence, a computing system developed by Google researchers in Great Britain has beaten a top human player at the game of Go, the ancient Eastern contest of strategy and intuition that has bedeviled AI experts for decades. Machines have topped the best humans at most games held up as measures of human intellect, including chess, Scrabble, Othello, even Jeopardy!. But with Go--a 2,500-year-old game that's exponentially more complex than chess--human grandmasters have maintained an edge over even the most agile computing systems. Earlier this month, top AI experts outside of Google questioned whether a breakthrough could occur anytime soon, and as recently as last year, many believed another decade would pass before a machine could beat the top humans. But Google has done just that.

I've just run my second briefing on intelligent assistance. Much happened in the few months between sessions. This time, the second half of the day was dominated with stories about bots and their use cases on messaging platforms. It also included the amazing things now possible via automated voice which Amazon's Alexa Challenge exemplifies. Meanwhile IBM's Watson continues its conquest of carbon life forms with the trashing of an extraordinarily talented Go grand master.

One day, filled with a mix of invited talks and presentations of peer-reviewed papers. Invited speaker Karl Branting The MITRE Corporation, USA Accepted papers (full, position and short) Sudhir Agarwal, Kevin Xu and John Moghtader Toward Machine-Understandable Contracts Trevor Bench-Capon Value-Based Reasoning and the Evolution of Norms Trevor Bench-Capon and Sanjay Modgil Rules are Made to be Broken Markus Fatalin Product Liability for Autonomous Systems in Europe Raghav Kalyanasundaram, Krishna Reddy P and Balakista Reddy V Analysis for Extracting Relevant Legal Judgments using Paragraph-level and Citation Information Niels Netten, Susan van Den Braak, Sunil Choenni and Frans Leeuw The Rise of Smart Justice: on the Role of AI in the Future of Legal Logistics Marc van Opijnen and Cristiana Santos On the Concept of Relevance in Legal Information Retrieval Livio Robaldo and Xin Sun Reified Input/Output logic - a position paper Olga Shulayeva, Advaith Siddharthan and Adam Wyner Recognizing Cited Facts and Principles in Legal Judgements Pieter Slootweg, Lloyd Rutledge, Lex Wedemeijer and Stef Joosten The Implementation of Hohfeldian Legal Concepts with Semantic Web Technologies Floris Bex, Joeri Peters and Bas Testerink A.I for Online Criminal Complaints: from Natural Dialogues to Structured Scenarios Robert van Doesburg, Tijs van der Storm and Tom van Engers CALCULEMUS: Towards a Formal Language for the Interpretation of Normative Systems Henry Prakken On how AI & law can help autonomous systems obey the law: a position paper Giovanni Sileno, Alexander Boer and Tom Van Engers Reading Agendas Between the Lines, an Exercise Bart Verheij Formalizing Correct Evidential Reasoning with Arguments, Scenarios and Probabilities

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted endeavor of building a “Creative Economy” and “Software Centric Society”. However, AI was not always flourishing as it is now. Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.

The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

The Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. In light of recent advances in machine intelligence, a number of scientists, philosophers and technologists have revived the discussion about the potential catastrophic risks entailed by such an entity. In this article, we trace the origins and development of the neo-fear of superintelligence, and some of the major proposals for its containment. We argue that such containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) infeasible.

One purpose of CASC is to provide a public evaluation of the relative capabilities of ATP systems. The TPTP version used for CASC is released beyond the ATP community. Fulfillment of these after the competition, so that new problems have not objectives provides insight and stimulus for the been seen by the entrants. In some divisions the systems development of more powerful ATP systems, leading are ranked according to the number of problems to increased and more effective use. The most recent CASC, accompanied by a proof or model (thus giving only held at CADE-25 in Berlin, Germany, in 2015, was an assurance of the existence of a proof/model).

Recent years have seen significant technical progress on robot planning, enabling robots to compute actions and motions to accomplish challenging tasks involving driving, flying, walking, or manipulating objects. However, robots that have been commercially deployed in the real world typically have no or minimal planning capability. These robots are often manually programmed, teleoperated, or programmed to follow simple rules. Although these robots are highly successful in their respective niches, a lack of planning capabilities limits the range of tasks for which currently deployed robots can be used. In this article, we highlight key conclusions from a workshop sponsored by the National Science Foundation in October 2013 that summarize opportunities and key challenges in robot planning and include challenge problems identified in the workshop that can help guide future research towards making robot planning more deployable in the real world.

This paper presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The paper provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.