This article calls for the research community broaden its focus by conducting research that begins with the discovery of a transfer opportunity, and only arrives at the identification of relevant knowledge and methods after significant processing.

There is a Transfer Learning Toolkit for Matlab available on the web. Transfer learning has developed techniques for classification, regression, and clustering (as summarized in Pan and Yang's 2009 survey) and for complex interactive tasks that are often best addressed by reinforcement learning techniques. And transfer learning has been applied to domains as diverse as named entity recognition, image clustering, information retrieval, link prediction, AP physics, and others. As with many human-level AI goals, transfer learning is still a long way from the ability for agents to take advantage of relevant previous learned knowledge and experience to perform (at least) competently and effectively on new tasks the first time they are encountered. However, there is a more practical and more feasible goal for transfer learning against which progress is being made.

The National Association of Securities Dealers, Inc., regulation advanced-detection system (ADS) monitors trades and quotations in The Nasdaq Stock Market to identify patterns and practices of behavior of potential regulatory interest. More important, it has greatly expanded surveillance coverage to new areas of the market and to many new types of behavior of regulatory concern. ADS combines detection and discovery components in a single system that supports multiple regulatory domains and shares the same market data. ADS makes use of a variety of AI techniques, including visualization, pattern recognition, and data mining, in support of the activities of regulatory analysis, alert and pattern detection, and knowledge discovery.

The National Association of Securities Dealers, Inc., regulation advanced-detection system (ADS) monitors trades and quotations in The Nasdaq Stock Market to identify patterns and practices of behavior of potential regulatory interest. ADS has been in operational use at NASD Regulation since the summer of 1997 by several groups of analysts, processing approximately 2 million transactions a day, generating over 10,000 breaks. More important, it has greatly expanded surveillance coverage to new areas of the market and to many new types of behavior of regulatory concern. ADS combines detection and discovery components in a single system that supports multiple regulatory domains and shares the same market data. ADS makes use of a variety of AI techniques, including visualization, pattern recognition, and data mining, in support of the activities of regulatory analysis, alert and pattern detection, and knowledge discovery.

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed.

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed. FAIS's unique analytic power arises primarily from a change in view of the underlying data from a transaction-oriented perspective to a subject-oriented (that is, person or organization) perspective.

Daewoo Heavy Industries, in conjunction with the Korean Advanced Institute of Science and Technology, integrated applicability and limitations of various five separate schedulers based on different several papers from the AI techniques. IAAI has been held annually that are addressed at the conference. Mita Industrial Co., Ltd., said Japan's troubleshooting expert system proceedings were published in book Seventeen applications represent this has been supplied as an embedded form through 1992. Since 1993, a year's award winners: 11 were from component of its photocopiers conference proceedings volume has the United States, 4 from the Pacific since April 1994. It uses new reasoning been published, and selected papers Rim, 1 from Europe, and 1 from the methods based on virtual cases have been republished as articles in Middle East.