Speaking after the first day of the ICT meeting, Takaichi said she introduced eight basic principles Tokyo believes important when developing computer science that gives machines human-like intelligence, and that she was generally supported in calling for further discussion. The eight principles include making AI networks controllable by human beings and respect for human dignity and privacy. "The development of AI is expected to progress at a tremendous pace of speed, and it should be amazing technology that does not give anxiety to people," the minister of internal affairs and communications told reporters, noting the need to deepen international discussion about establishing a basic set of rules. The first G-7 ICT ministerial meeting in nearly two decades comes at a time when cyberattacks have become a global reality and the development of such potentially revolutionary technologies as artificial intelligence and the "Internet of Things" (IoT) -- the concept of connecting various products to the Internet -- continues apace. With cyberattacks having become a global reality, participants from Britain, Canada, France, Germany, Italy, Japan and the United States discussed at the G-7 meeting ways to utilize advances in the field to drive economic growth while ensuring data security.

Using humanoid robots to study human behavior.

This paper offers some suggestions on how governments and businesses can manage the risks, whilst maximising the benefits, posed by Artificial Intelligence (AI) over the course of this century. It begins by outlining the history and present state of the art, summarising predictions made by experts in the field on how it might progress in the coming decades. Following this it provides an overview of the dangers that advanced AI applications may present across a range of industries. In its conclusion the paper provides a summary of the policy recommendations made across these areas. It continues with a discussion of the policy themes that have been developed, followed by some final remarks. Throughout we avoid discussion of the technical details of AI development, but focus on how states and private institutions can increase the likelihood that the social, economic and political impact of advanced AI will be positive.

Research funded by the National Science Foundation may have found an ingenious solution to poaching: applying game theory and computer science to real-life situations. One of the biggest factors in why there are so many endangered animals today is poaching – a centuries-old problem. The dwindling tiger population is one of the most glaring examples of this. Whether for sport, medicine, pelts or other body parts, poaching remains a huge threat to wildlife. Patrols have long been the most direct form of human intervention in wildlife protection.

Infrared cameras that track eye movements could detect concussions in less than a minute, offering insight into whether athletes or children have sustained the injury. The device, called'Eye-Sync' has now been approved by the US Food and Drug Administration and has been developed amid growing concerns over brain injuries in contact sports. Head trauma affects the brain's anticipatory neural network and Eye-Sync focuses on analysing visual response in this network. Infrared cameras that track eye movements could detect concussions in less than a minute, offering insight into whether athletes or children have sustained the injury. The device, called'Eye-Sync' (pictured) has now been approved by the US Food and Drug Administration The device was developed by Boston-based SyncThink. A user puts on a virtual reality headset connected to a computer tablet, with a moving circle appearing in the display.

Our First meetup will be focused on H2O, an open-source Machine Learning Platform. It will take place the CIC on Milk Street in Boston.. the Date is set for 5/12 at 6:00PM. NOTE: Bring Laptops if you want to partake in hands on demo. This workshop will provide an overview of how to use H2O, the scalable open source machine learning library, from Python/R/Flow UI. The core algorithms of H2O are implemented in Java, however, fully-featured APIs are available in R, Python, Scala, and also through the Flow UI web interface.

We review several different languages for collective decision making problems, in which agents express their judgments, opinions, or beliefs over elements of a logically structured domain. Several such languages have been proposed in the literature to compactly represent the questions on which the agents are asked to give their views. In particular, the framework of judgment aggregation allows agents to vote directly on complex, logically related formulas, whereas the setting of binary aggregation asks agents to vote on propositional variables, over which dependencies are expressed by means of an integrity constraint. We compare these two languages and some of their variants according to their relative succinctness and according to the computational complexity of aggregating several individual views expressed in such languages into a collective judgment. Our main finding is that the formula-based language of judgment aggregation is more succinct than the constraint-based language of binary aggregation. In many (but not all) practically relevant situations, this increase in succinctness does not entail an increase in complexity of the corresponding problem of computing the outcome of an aggregation rule.

A central question in knowledge representation is the following: given some knowledge representation formalism, is it possible, and if so how, to simplify parts of a knowledge base without affecting its meaning, even in the light of additional information? The term strong equivalence was coined in the literature, i.e. strongly equivalent knowledge bases can be locally replaced by each other in a bigger theory without changing the semantics of the latter. In contrast to classical (monotone) logics where standard and strong equivalence coincide, it is possible to find ordinary but not strongly equivalent objects for any nonmonotonic formalism available in the literature. This paper addresses these questions in the context of abstract argumentation theory. Much effort has been spent to characterize several argumentation tailored equivalence notions w.r.t. extension-based semantics. In recent times labelling-based semantics have received increasing attention, for example in connection with algorithms computing extensions, proof procedures, dialogue games, dynamics in argumentation as well as belief revision in general. Of course, equivalence notions allowing for replacements are of high interest for the mentioned topics. In this paper we provide kernel-based characterization theorems for semantics based on complete labellings as well as admissible labellings w.r.t. eight different equivalence notions including the aforementioned most prominent one, namely strong equivalence.

The popularity of tagging systems provides a great opportunity to improve the performance of item recommendation. Although existing approaches use topic modeling to mine the semantic information of items by grouping the tags labelled for items, they overlook an important property that tags link users and items as a bridge. Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. Towards solving this challenging problem, we propose a novel tag and rating based collaborative filtering (CF) model for item recommendation, which first uses topic modeling to mine the semantic information of tags for each user and for each item respectively, and then incorporates the semantic information into matrix factorization to factorize rating information and to capture the bridging feature of tags and ratings between users and items.As a result, our model captures the semantic correlation between users and items, and is able to greatly improve recommendation performance, especially in DS-WO-CRI situations.Experiments conducted on two popular real-world datasets demonstrate that our proposed model significantly outperforms the conventional CF approach, the state-of-the-art social relation based CF approach, and the state-of-the-art topic modeling based CF approaches in terms of both precision and recall, and it is an effective approach to the DS-WO-CRI problem.

Search in general, and heuristic search in particular, is at the heart of many Artificial Intelligence algorithms and applications. There is now a growing and active community devoted to the empirical and theoretical study of heuristic search algorithms, thanks to the successful application of search-based algorithms to areas such as robotics, domain-independent planning, optimization, and computer games. In this extended abstract we highlight recent efforts in understanding suboptimal search algorithms, as well as ensembles of heuristics and algorithms. The result of these efforts are meta-reasoning methods which are applied to orchestrate the different components of modern search algorithms. Finally, we mention recent innovative applications of search that demonstrate the relevance of the field to general AI.