Panel Looks to Foster Collaboration Around AI and Machine Learning for CNS Diseases GNS HealthCare


There's no question artificial intelligence and machine learning technologies are enabling important discoveries in healthcare, but there can be a bit of a disconnect among the various stakeholders using them. A panel discussion at the upcoming CNS Summit in Boca Raton, Fla. presents a rare opportun...

How humans will learn to love the robots of tomorrow


For the Perfect Strangedroids discussion panel on Wednesday, Engadget hosted a trio of robotics experts.. Sabri Sansoy, CEO and Chief Roboticist of Orchanic; Nader Hamda, Founder and CEO of Ozobot; and Stu Lipoff, IEEE Life Fellow and President of IP Action Partners all took the Engadget stage at CES 2018 with senior editor Andrew Tarantola moderating. Click here to catch up on the latest news from CES 2018. Click here to catch up on the latest news from CES 2018. Andrew has lived in San Francisco since 1982 and has been writing clever things about technology since 2011. When not arguing the finer points of portable vaporizers and military defense systems with strangers on the internet, he enjoys tooling around his garden, knitting and binge watching anime.

Seventh Workshop on the Validation and Verification of Knowledge-Based Systems

AI Magazine

The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing-and toolbased approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia. The workshop is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). It has influenced the evolution of the discipline from its origins in 1988; at this time, researchers were asking the questions, How can we evaluate the correctness of KBS? How is this process different from conventional system evolution?

Introduction to This Special Issue

AI Magazine

Developing agents that could perceive the world, reason about what they perceive in relation to their own goals and acts, has been the Holy Grail of AI. Early attempts at such holistic intelligence (for example, SRI International's AI researchers turned their attention to component technologies for structuring a single agent, such as planning, knowledge representation, diagnosis, and learning. Although most of AI research was focused on single-agent issues, a small number of AI researchers gathered at the Massachusetts Institute of Technology Endicott House in 1980 for the First Workshop on Distributed AI. The main scientific goal of distributed AI (DAI) is to understand the principles underlying the behavior of multiple entities in the world, called agents and their interactions. The discipline is concerned with how agent interactions produce overall multiagent system (MAS) behavior.

The CP'98 Workshop on Constraint Problem Reformulation

AI Magazine

Roughly 30 people attended this workshop. This article summarizes the papers presented at the workshop and highlights some of the questions and issues raised during the discussion. The task of selecting the best representation for solving a problem by means of automatically reformulating the problem is a core challenge in AI. Saul Amarel (1968) outlined the impact of different representations of the missionaries and cannibals problem on the performance of the algorithms used to solve the problem. He also proposed automating the reformulation process and, consequently, the process of selecting representations: The general problem of representation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem.

The 1988 AAAI Workshop on Explanation

AI Magazine

This article is a summary of the Workshop on Explanation held during the 1988 National Conference on Artificial Intelligence in St. Paul, Minnesota. The purpose of the workshop was to identify key research issues in the rapidly emerging area of expert system explanation. Expert system explanation is the study of how to give an expert system the ability to provide an explanation of its actions and conclusions to a variety of users (including the domain expert, knowledge engineer, and end user). The 1988 AAAI Workshop on Explanation brought together many of the world's experts on expert system explanation in an attempt to highlight key research areas and questions that should be the focus of subsequent work. The one-day workshop was organized into five sessions of short presentations, each followed by panelled open discussion among the 35 workshop participants.

Language, Vision, and Music

AI Magazine

The delegates enjoyed not only the academic content but also the surplus of social events and expressed their congratulations on the program and organization. CSNLP-8 attracted a large number of delegates and papers from abroad, including many from Britain, Europe, the United States, and Asia. It was run just before "MIND-IV: Two Sciences of Mind," the Annual Conference of the Cognitive Science Society of Ireland (CSSI), at Dublin City University, Dublin, Ireland, 15-18 August. CSNLP-8 was advertised internationally to mail groups and on usenet as well as by placing information at the Information Technology Centre, NUI Galway, on the World Wide Web. Paul Mc Kevitt was program chair for CSNLP-8, with Conn Mulvihill and Micheal Colhoun as local organization chairs and Seán Ó Nualláin as the general chair for CSNLP.


AI Magazine

The growth in the amount of available databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization. The importance of this topic is now recognized by leading researchers. Michie predicts that "The next area that is going to explode is the use of machine learning tools as a component of large scale data analysis'' (AI Week, March 15, 1990).

The First International Workshop on Rough Sets

AI Magazine

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design. The workshop was the first international meeting of researchers working in this relatively new area. The approximate definition of a set in terms of lower and upper bounds, as introduced in the ...

The Fourth International Workshop on Artificial Intelligence in Economics and Management

AI Magazine

The Fourth International Workshop on Artificial Intelligence in Economics and Management was held in Tel-Aviv, Israel, from 8 to 10 January 1996. This article discusses the main themes presented at the workshop, including the need for multiple methods in any system designed to solve real-world problems, the differences in the effectiveness of AI versus classic analytic techniques, and the use of AI techniques to customize products. The main themes that emerged during the workshop were (1) the need to use multiple methods in any system designed to solve major realworld problems, (2) a continuing interest in comparing the effectiveness of AI solutions with classic analytic techniques, and (3) a growing use of AI techniques to customize products to suit individual consumers. As a matter of course, almost every presentation at the workshop touched on AI techniques in one way or another. However, a group of papers at the workshop had AI techniques as their main focus.