This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. The article argues that although robot learning is a new subfield, there is significant potential for synergy with traditional machine learning if the differences in research cultures can be overcome.

Machine translation of human languages (for example, Japanese, English, Spanish) was one of the earliest goals of computer science research, and it remains an elusive one. Like many AI tasks, trans-lation requires an immense amount of knowledge about language and the world. Recent approaches to machine translation frequently make use of text-based learning algorithms to fully or partially automate the acquisition of knowledge. This article illustrates these approaches.

I review current statistical work on syntactic parsing and then consider part-of-speech tagging, which was the first syntactic problem to successfully be attacked by statistical techniques and also serves as a good warm-up for the main topic-statistical parsing. Here, I consider both the simplified case in which the input string is viewed as a string of parts of speech and the more interesting case in which the parser is guided by statistical information about the particular words in the sentence. Finally, I anticipate future research directions.

This article surveys the use of empirical, machine-learning methods for a particular natural language-understanding task-information extraction. The author presents a generic architecture for information-extraction systems and then surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture.

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

Busey Bank (Champaign, Ill.) is using intelligent-agent technology to launch its Lloyds Bowmaker Motor Finance (Petersfield, U.K.) has implemented a The Philadelphia Stock Exchange care products, has developed a rulebased neural network-based system for credit (Philadelphia, Pa.) has adopted an multinational order-entry and scoring new loan applications. The company is system helps Lloyds determine whether increase the reliability and scalability using the system to process orders to accept a loan and gives the reasons of network-supported options-trading from its network of more than for its choice. The system uses an electronic facilities. The software will permit installed a rule-based expert system to camera to image the front face of letters, team members in different geographic manage the complexity of producing identify the destination address, locations to explore similar multisensory more than 20,000 new designs and and determine its delivery-point bar environments both independently 2.4 billion greeting cards annually. The company has completely reengineered its operation, converting an Telecommunications providers MCI Healthcare software developer HBO & antiquated job-shop operation into a (Washington, D.C.) and BT (London, Company (Atlanta, Ga.) is developing state-of-the-art cellular one.

"Emperor AI, Where Is Your New Mind?" The "emperor's new mind" is not here yet, but it is The first two tiers are necessary for developing s AI dead as a science for investigating the Penrose (1989), is the emperor's mind actually an AI theory of the mind simply because without hollow? Should AI be used simply to superficially the right technology, one just could not describe those studies that produce implement such a theory. However, as demonstrated intelligent programs? Several indicators suggest in past AI research and as I argue later, that the answer to these questions might they alone are not sufficient; the machines created be yes.

In recent years, there has been a resurgence in research on empirical methods in natural language processing. These methods employ learning techniques to automatically extract linguistic knowledge from natural language corpora rather than require the system developer to manually encode the requisite knowledge. The current special issue reviews recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction, and machine translation. This article presents an introduction to the series of specialized articles on these topics and attempts to describe and explain the growing interest in using learning methods to aid the development of natural language processing systems.

Gary Kasparov in the final game of of Technology Computer Science Computer Chess, created in a tied, six-game match last May 11. Soon thereafter, the team moved to IBM, where they have been ever since, working under wraps on Deep Blue. The $100,000 third tier of the prize was awarded at AAAI-97 to this IBM team, who built the first computer chess machine that beat a world chess champion. The members of the Deep Blue team were also honored for their achievement with the Allen Newell Research Excellence Medal, sponsored by Carnegie Mellon University. Allen Newell Medals were presented to each of the major researchers in the field whose earlier contributions ultimately led to the success of Deep Blue.

One of the best things about conferences, as we all know, is the opportunity they afford to consolidate old friendships and make new contacts. Clusters of con-versation provide a more valuable way to spend ones time than attending sessions. At the last national meeting we escaped from the celebrations of the recent victory of Deep Blue over the dreaded Kasparov, to find just such a group, already engaged in an animated discussion ....