Within the time of a human generation, computer technology will be capable of producing computers with as many artificial neurons as there are neurons in the human brain. Within two human generations, intelligists (AI researchers) will have discovered how to use such massive computing capacity in brainlike ways. This situation raises the likelihood that twenty-first century global politics will be dominated by the question, Who or what is to be the dominant species on this planet? This article discusses rival political and technological scenarios about the rise of the artilect (artificial intellect, ultraintelligent machine) and launches a plea that a world conference be held on the so-called "artilect debate."

Since May 1988, Deep Thought (DT), the creation of a team of students at Carnegie Mellon University, has been attracting a lot of notice. In the Fredkin Masters Open, May 28-30, DT tied for second in a field of over 20 masters and ahead of three other computers, including Hitech and Chiptest (the winner of the 1987 North American Computer Championships). In August at the U.S. Open, DT scored 8.5, 3.5 to tie for eighteenth place with Arnold Denker among others. Its performance was marred by hardware and software bugs. However, DT astounded everyone by beating International Master (IM) Igor Ivanov, the perennial winner of the U.S. Grand Prix circuit prize, who is generally regarded to be as strong as the average Grandmaster.

Failing to recognize this, significance. All interested readers The medium has misplaced the message understanding intelligence and cognition should be directed to his Ph.D. thesis [that should have appeared in Dr. Franck can be reached is merely irreverent, not irrelevant, per se " As readers can see, quite at the following address Dr. Bruno to AI All I can say is mea Thank you Columbus, OH 43210 culpa, and I hope this letter may help James R Slagle and Michael R. Wick to square things I read with great interest the excellent Information Processing. Engineering" by Ken Forbus in AI manuscript, I somehow managed to Professor Forbus's forceful changed the intended meaning rather Our recent article, entitled "A I personally as published reads: "The goal of these examples of our evaluation process thank him for writing such an eloquent gatherings has been to understand One of these examples involved the Various groups, especially for narrowly defined tasks (expert systems)." Naturally, I would not expect Prof. Forbus to enumerate IIICAD stands for "Intelligent, Integrated, and Interactive CAD" and was CWI is a research AAAI Membership Directory center in pure and applied mathematics An invaluable networking tool, this annual roster of AAAI members and computer science at Amsterdam. You've heard of this AAAI conference-it's the most distinguished Email: paulh@cwi.nl. TR An exciting opportunity to view the latest Al products, services, and CSR8744, CWI, Amsterdam research from industry and the academic community. These are the state-of-the-art research papers presented at the AAAl's A copy of this publication is included in conference Data Description Language for Coding registration; AAAI members not attending the conference may purchase Design Knowledge."

In most situations, however, there is not enough information to make accurate predictions.

Patterns of activity over real neural structures are known to exhibit timedependent behavior. It would seem that the brain may be capable of utilizing temporal behavior of activity in neural networks as a way of performing functions which cannot otherwise be easily implemented. These might include the origination of sequential behavior and the recognition of time-dependent stimuli. A model is presented here which uses neuronal populations with recurrent feedback connections in an attempt to observe and describe the resulting time-dependent behavior. Shortcomings and problems inherent to this model are discussed. Current models by other researchers are reviewed and their similarities and differences discussed.

Here, X is a finite or infinite set, and Y is another finite or infinite set. A learning machine observes any set of pairs (x, y) sampled randomly from X x Y. (X x Y means the Cartesian product of X and Y.) And, it computes some estimate j:

LEARNING IN NETWORKS OF NONDETERMINISTIC ADAPTIVE LOGIC ELEMENTS Richard C. Windecker* AT&T Bell Laboratories, Middletown, NJ 07748 ABSTRACT This paper presents a model of nondeterministic adaptive automata that are constructed from simpler nondeterministic adaptive information processing elements. The first half of the paper describes the model. Chief among these properties is that network aggregates of the model elements can adapt appropriately when a single reinforcement channel provides the same positive or negative reinforcement signal to all adaptive elements of the network at the same time. This holds for multiple-input, multiple-output, multiple-layered, combinational and sequential networks. It also holds when some network elements are "hidden" in that their outputs are not directly seen by the external environment. INTRODUCTION There are two primary motivations for studying models of adaptive automata constructed from simple parts. First, they let us learn things about real biological systems whose properties are difficult to study directly: We form a hypothesis about such systems, embody it in a model, and then see if the model has reasonable learning and behavioral properties. In the present work, the hypothesis being tested is: that much of an animal's behavior as determined by its nervous system is intrinsically nondeterministic; that learning consists of incremental changes in the probabilities governing the animal's behavior; and that this is a consequence of the animal's nervous system consisting of an aggregate of information processing elements some of which are individually nondeterministic and adaptive. The second motivation for studying models of this type is to find ways of building machines that can learn to do (artificially) intelligent and practical things.

SYNCHRONIZATION IN NEURAL NETS Jacques J. Vidal University of California Los Angeles, Los Angeles, Ca. 90024 John Haggerty· ABSTRACT The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. This type of communication contrasts with that which is assumed in most other models which typically are continuous or discrete value-passing networks. Limiting the messages received by each processing unit to time markers that signal the firing of other units presents significant implemen tation advantages. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors.

W. Scott Stornetta Stanford University, Physics Department, Stanford, Ca., 94305 Tad Hogg and B. A. Huberman Xerox Palo Alto Research Center, Palo Alto, Ca. 94304 ABSTRACT Recognizing patterns with temporal context is important for such tasks as speech recognition, motion detection and signature verification. We propose an architecture in which time serves as its own representation, and temporal context is encoded in the state of the nodes. We contrast this with the approach of replicating portions of the architecture to represent time. As one example of these ideas, we demonstrate an architecture with capacitive inputs serving as temporal feature detectors in an otherwise standard back propagation model. Experiments involving motion detection and word discrimination serve to illustrate novel features of the system.

(June 1987).