Alan M. Turing

“Alan M. Turing and Intelligent Machinery, a prelude to Artificial

Intelligence”

May 4, 2012 Version 2.0; 05/04/2012

John M. CasarellaProceedings of Student/Faculty Research

Day

Ivan G. Seidenberg School of CSIS, Pace

University

Introduction

Alan M. Turing:

MathematicianComputer Scientist

CryptographerThe Enigma Human Being

Turing - On Computable Numbers… "On computable numbers, with an application to the Entscheidungsproblem" was published in 1936 Introduces the world to his “computing machine”, complete with paper tape, symbols, scanner (reader) and of course “instructions” (program to manage changes in state) Sample Program Table:

State Scanned Square Operation

Next State

a blank P[0], R b

b blank R c

c blank P[1], R d

d blank R a

Top Contributions to Modern Computing: The Idea of controlling the function of the computing machinery by storing a program of symbolically encoded instructions in the machines memory. By demonstration, a single machine of fixed structure capable of completing all computations - the Universal Turing Machine.

Turing - Computing Machinery

“Computing Machinery and Intelligence” (Mind) was published in 1950 Within the paper, the famous question is asked:

Can Machines Think? Provides the basis for providing the answer Christened the “Turing Test” at a point in the future Much has been afforded the test and much has been placed on its significance

The Turing Test Critics ask if passing the test is

sufficient or a necessary condition for machine intelligence

Although widely accepted, limiting in determining if a machine is capable of intelligence

Turing never claimed passing the is a necessary condition for intelligence

In his papers, claims point of test was determine if a computer can “imitate a brain”

Can it be passed at all? If “machine intelligence” no longer a

oxymoron, then one of Turing’s predictions has come true

In the beginning…

Turing was harboring thoughts of Machine Intelligence as early as 1941

Turing’s computing machines - model a child’s mind and then ‘educate’ it

Learning from experience Start with an initial state of the mind /

computer Determine the education subjected to Experiences other than education

Start with a simple machine, progress to one more elaborate

A key concept - “teach” a network of artificial neurons to perform specific tasks, i.e., the machine learns

Turing - Intelligent Machinery

“Intelligent Machinery” (Unpublished report, 1948)

Remained unpublished until 1968 (Well after Hebb and Rosenblatt)

Introduces many of the concepts that were later to become central to AI, specifically to Neural Nets

The envisioned picture of the cortex as an unorganized machine is satisfactory from the point of view of evolution and genetics

Turing and AI Foundations Computing machines built out of simple,

neuron-like elements Elements randomly connected together into

networks Consisted of artificial neurons and devices

capable of modifying the connections between them

Training process renders certain pathways as effective or ineffective

Every neuron executes the same logical operation of “not and’ (NAND)

The idea that an initially unorganized neural network can be organized by means of “interference training” - this is significant

Referred to as “unorganized machines” or “B-type unorganized machine” neural net

Turing’s Unorganized Machine

Neurons

Unorganized Machines

Turing’s Unorganized Machine

Perceptron Model Unorganized Machines

B - Node

The Perception of Intelligence

How do we perceive intelligence?

What if a problem is presented to a mathematician or scientist to solve…

What if a problem is presented to a computer to solve…

Turing’s Perceptions

Turing (1946): there are indications however that it is possible to make the machine display intelligence at the risk of its making occasional serious mistakes

Turing (1947): but the human mathematician would likewise make blunders when trying out new techniques…in other words, if a machine is expected to be infallible, it cannot also be intelligent.

Intelligent humans, even highly regarded, intelligent humans are not infallible; they make errors, yet we do not consider them any less intelligent when they do, so why not apply the same standard or perception to computing machining when we attempt to determine machine intelligence.

And in the end…

His personal expression to be “human” led to condemnation

On 9/11/2009 the British Publicly Apologize and acknowledge Turing’s contribution to the War effort and for providing the foundation for modern computing

[1] A. M. Turing, "Computing Machinery and Intelligence," Mind, vol. 59, pp. 433 - 460, October 1950.

[2] B. J. Copeland, and D. Proudfoot, "What Turing Did after He Invented the Universal Turing Machine," Journal of Logic, Language, and Information, vol. 9, pp. 491-509, 2000.

[3] B. J. Copeland, and D. Proudfoot, "The Legacy of Alan Turing," Mind, vol. 108, pp. 187-195, 1999.

[4] B. J. Copeland, "The Essential Turing," Oxford, Great Britain: Oxford University Press, 2004.

[5] A. M. Turing, "The Turing Digital Archive," http://www.turingarchive.org/: University of Southamption and King's College Cambridge, 2002.

[6] N. Block, "Psychologism and Behaviourism," Philosophical Review, vol. 90, pp. 5-43, 1981.

[7] R. French, "Subcognition and the Limits of the Turing Test," Mind, vol. 99, 1990.

[8] P. Hayes, and K. Ford, "Turing Test Considered Harmful," in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995, pp. 972-977.

[9] K. M. Ford, and P.J. Hayes, "On Computational Wings: Rethinking the Goals of Artificial Intelligence," Scientific American Presents, vol. 9, pp. 78-83, 1998.

[10] J. Copeland and Diane Proudfoot "On Alan Turing's Anticipation of Connectionism," Synthese, vol. 108, pp. 361 - 377, 1996.

[11] J. Copeland and Diane Proudfoot, "Alan Turing's Forgotten Ideas in Computer Science," Scientific American, pp. 99 - 103, 1999.

[12] A. M. Turing, "Intelligent Machinery," in Machine Intelligence 5, B. Meltzer, and D. Michie, Ed. Edinburgh: Edinburgh University Press, 1948, pp. 3-23.

[13]B. G. Farley, and W.A. Clark, "Simulation of Self-Organizing Systems by Digital Computer," Institute of Radio Enigneers Transactions on Information Theory, vol. 4, pp. 76 - 84, 1954.

[14]D. Gelernter, "Artificial Intelligence is Lost in the Woods," in Technology Review, 2007.

[15]A. M. Turing, "On computable numbers, with an application to the Entscheidungsproblem," Proceedings of the London Mathematical Society, Series 2, vol. 42, pp. 230-265, 1936.

[16] M. L. Minsky, and Papert, Seymour S., Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press, 1969.

[17] D. E. Rumelhart, and McClelland, J.L. editors, "Parallel Distributed Processing: Explorations in the Microstructures of Cognition." vol. 1 - Foundations Cambridge, MA: MIT Press, 1986.

[18] J. L. McClelland, and Rumelhart, D.E., "Parallel Distributed Processing: Explorations in the Microstructures of Cognition." vol. 2 - Psychological and Biological Models Cambridge, MA: MIT Press, 1986.

[19] D. O. Hebb, The Organization of Behavior. New York: John Wiley & Sons, 1949.

References

References[20] W. S. McCulloch, and Pitts, Walter H., "A Logical Calculus of the Ideas Immanent in Neural Nets," Bulletin of Mathematical Biology,

vol. 52, pp. 99 - 115, 1943.

[21] F. Rosenblatt, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," Psychological Review, vol. 65, pp. 386 - 408, 1958.

[22] F. Rosenblatt, Principles of Neurodynamics. Washington, DC: Spartan Books, 1962.

[23] J. Hawkins, with Sandra Blakeslee, On Intelligence, First ed. New York: Times Books, Henry Holt and Company, 2004.

[24] D. George, and Hawkins, J., "Belief Propagation and Wiring Length Optimization as Organizing Principles for Cortical Microcircuits," Numenta, Inc., 2005.

[25] D. George, and Hawkins, J., "Invariant Pattern Recognition using Bayesian Inference on Hierarchical Sequences," Numenta, Inc., 2006.

[26] J. Hawkins, and Dileep George, "Hierarchical Temporal Memory, Concepts, Theory, and Terminology," Numenta, Inc., 2006.

[27] J. Hawkins, "Hierarchical Temporal Memory (HTM): Biological Mapping to Neocortex and Thalamus," Numenta, Inc., 2007.

[28] J. Hawkins, "An Investigation of Adaptive Behavior Towards a Theory of Neocortical Function," 1986.

[29] D. George, "How the Brain Might Work: A Hierarchical and Temporal Model for Learning and Recognition," Doctoral Dissertation; Stanford University, 2008.