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Minds, machines and phenomenology: Some reflections on Dreyfus’ What Computers Can’t Do

Classics

Rather than provide a general review of Dreyfus critique this article concentrates on certain fundamental criticisms that Dreyfus directs at the information-processing approach to cognitive psychology and points out the unique conception of what it means to understand cognition which separates a phenomenologist from the typical cognitive psychologist.


A Framework for Representing Knowledge

Classics

This is a partial theory of thinking, combining a number of classical and modern concepts from psychology, linguistics, and AI. Whenever one encounters a new situation (or makes a substantial change in one's viewpoint) he selects from memory a structure called a frame, a remembered framework to be adopted to fit reality by changing details as necessary. A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child's birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame.


Semantics and speech understanding

Classics

In researc which lan uac; assumed knowled way it use of provide impreci recent h into a is to e. In that on re of th is used the cons s, to na se acous years, utomati (r,et a nost e need e lan u (pragma traints ke sens tic sit there has c speech u computer of this s to pro are (its s tics). It and expec e of the i nal that i been a nderstan to und recent a vide th yntax an will th tations nherentl s human rroat increase in dine, the purpose of erstand the spoken ctivity, it has been e computer with a d semantics) and the en be able to make which this knowledfre y vaf ue, sloppy and soeech. Syntactic constraints and expectations are based on the patterns formed by a Riven set of linguistic objects, e. .


Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences

Classics

See also Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY: John Wiley & Sons, Inc. Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature323 (6088): 533–536. doi:10.1038/323533a0.Ph.D. thesis, Harvard University.


Speech understanding systems: Final report of a study group

Classics

"A five-year interdisciplinary effort by speech scientists and computer scientists has demonstrated the feasibility of programming a computer system to “understand” connected speech, i.e., translate it into operational form and respond accordingly. An operational system (HARPY) accepts speech from five speakers, interprets a 1000-word vocabulary, and attains 91 percent sentence accuracy. This Steering Committee summary report describes the project history, problem, goals, and results." Amsterdam: North- Holland.


Artificial intelligence and the concept of mind

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Kenneth Mark Colby, 1920 - 2001 Kenneth Colby was born in Waterbury, Connecticut and graduated from Yale in 1941. Two years later he graduated from Yale's School of Medicine. Colby started his career as a professor of computer science at Stanford, and also did some research for the National Institute of Mental Health. It was there that he created Parry in the university's Artificial Intelligence Library. Parry was a chatterbot, and able to have conversations with people.


The structure of belief systems

Classics

Kenneth Mark Colby, 1920 - 2001 Kenneth Colby was born in Waterbury, Connecticut and graduated from Yale in 1941. Two years later he graduated from Yale's School of Medicine. Colby started his career as a professor of computer science at Stanford, and also did some research for the National Institute of Mental Health. It was there that he created Parry in the university's Artificial Intelligence Library. Parry was a chatterbot, and able to have conversations with people.



Some necessary conditions for a master chess program

Classics

Since 1967 there has again been great interest in chess programming. This paper demonstrates that the structure of today's most successful programs cannot be extended to play Master level chess. Certain basic requirements of a Master player's performance are shown to be outside the performance limits to which a program of this type could be extended. The paper also examines a basic weakness in the tree-searching model approach when applied to situations that cannot be searched to completion. This is the Horizon Effect, which causes unpredictable evaluation errors due to an interaction between the static evaluation function and the rules for search termination. The outline of a model of chess playing that avoids the Horizon Effect and appears extendable to play Master level chess is presented, together with some results already achieved In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California, pp. 77-85