Summary--There is frequently more or less acrimonious discussion about artificial intelligence and intelligent machines and their place in science. Usually the discussion settles down to the reiteration of two points of view. This paper is concerned with the difference between them. Do they merely reflect two emotional or ethical biases, or is there an underlying technical judgment on which they disagree? The authors claim the latter and purport to show what that judgment is.
Reprinted from Information Theory, Fourth London Symposium published by Butterworths, 88 Kingsway, London, W.C.2. MARVIN MINSKY and OLIVER G. SELFRIDGE Lincoln Laboratory*, Massachusetts Institute of Technology INTRODUCTION THE general nature of the problem is that an organism must learn to make the'right', or appropriate, response to its inputs. Typically, the inputs are large amounts of data, so that the machine must learn to recognize the similarities between different inputs which call for the same response, contrasted with the distinctions that call for different responses. The particular machines we are concerned with are random nets. A random net is a large set of similar and simply-acting elements whose attributes and interactive connections may be randomly established.
Department of Electrical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 24601. 1 /Introduction Giving a machine the ability to learn, adapt, organize or repair itself are among the oldest and most ambitious goals of computer science. In the early days of computing, these goals were central to the new discipline called cybernetics , . Over the past two decades, progress toward these goals has come from a variety of fields - notably computer science, psychology, adaptive control theory, pattern recognition, and philosophy. Substantial progress has been made in developing techniques for machine learning in highly restricted environments. Each of these programs, however, is tailored to its particular task, taking advantage of particular assumptions and characteristics associated with its domain.
A Model For Learning Systems STAN-CS-77-605 Heuristic Programming Project Memo 77-14 Reid G. Smith, Tom M. Mitchell Richard A. Chestek and Bruce G. Buchanan ABSTRACT A model for learnina systems is presented, and representative Al, pattern recognition, and control systems are discussed in terms of its framework. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and the specific environment In which it operates. These components are performance element, instance selector, critic, learning element, blackboard, and world model. Consideration of learning system design leads naturally to the concept of a layered system, each layer operating at a different level of abstraction. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research ...
The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.
In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. For this reason, previous approaches, based on following musical rules trying to capture interpretation knowledge, had serious limitations. An alternative approach, much closer to the observation-imitation process observed in humans, is that of directly using the interpretation knowledge implicit in examples extracted from recordings of human performers instead of trying to make explicit such knowledge. In the last part of the article, we report on a performance system, SAXEX, based on this alternative approach, that is capable of generating high-quality expressive solo performances of jazz ballads based on examples of human performers within a case-based reasoning (CBR) system.