Implications of Recursive Distributed Representations

I will describe my recent results on the automatic development of fixed-width recursive distributed representations of variable-sized hierarchal data structures. One implication of this work is that certain types of AI-style data-structures can now be represented in fixed-width analog vectors. Simple inferences can be performed using the type of pattern associations that neural networks excel at Another implication arises from noting that these representations become self-similar in the limit. Once this door to chaos is opened, many interesting new questions about the representational basis of intelligence emerge, and can (and will) be discussed.

[1]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[2]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[4]  V. Braitenberg Vehicles, Experiments in Synthetic Psychology , 1984 .

[5]  Robert M. Farber,et al.  How Neural Nets Work , 1987, NIPS.

[6]  Geoffrey E. Hinton,et al.  Parallel Models of Associative Memory , 1989 .

[7]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[8]  M. Barnsley,et al.  Solution of an inverse problem for fractals and other sets. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[9]  M. F. Wiser,et al.  When heat and temperature were one , 2014 .

[10]  Carl H. Smith,et al.  Inductive Inference: Theory and Methods , 1983, CSUR.

[11]  Thomas K. Landauer,et al.  How Much do People Remember? Some Estimates of the Quantity of Learned Information in Long-Term Memory , 1986, Cogn. Sci..

[12]  W. Hillis Intelligence as an emergent behavior: or, the songs of Eden , 1989 .

[13]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

[14]  Dana H. Ballard,et al.  Modular Learning in Neural Networks , 1987, AAAI.

[15]  Lawrence Birnbaum,et al.  Integrated processing in planning and understanding , 1986 .

[16]  Stephen Wolfram,et al.  Universality and complexity in cellular automata , 1983 .

[17]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[18]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[19]  A. Lapedes,et al.  Nonlinear Signal Processing Using Neural Networks , 1987 .

[20]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[21]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[22]  Tad Hogg,et al.  Phase Transitions in Artificial Intelligence Systems , 1987, Artif. Intell..

[23]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[24]  R. Schvaneveldt,et al.  Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. , 1971, Journal of experimental psychology.