The Arguments: Amanda Sharkey
Dr Amanda Sharkey is a lecturer in the Department of Computer Science at the University of Sheffield. She is director of the Artificial Intelligence course and Head of NRG research group. Her work involves applications of neural networks taking inspiration from how the brain works. She has a background in Psychology and is also interested in modelling biological systems and how that approach can be applied to A.I.

On why A.I. scientists haven't already far exceeded the capacity of brains:
"Well, maybe it's the way in which the brain is not perfect that accounts for some of our abilities. So that, for instance, our memories are not perfect - we don't remember everything, and that explains some of the things that we are able to do. And when you produce a perfect system that does whatever you tell it to, and stores every piece of information you've ever got, you can't produce the same kind of behaviour".

On the difference between a conventional computer and a neural network:
"With a neural network you've got no central set of instructions, and they're particularly useful where you have something where you don't have a rule to describe the relationship between the inputs that it gets so - say, the sensory information that it gets and the behaviour that you want. What you've got is a set of input units, and a set of output units and you've then got a weight between them which is analogous to the idea of the synapse between neurones in the brain. You then train the network, as in you've presented it with a set of inputs, you've told it what output you would like it to produce, so it adjusts the weights so that it does produce the right outputs. You can then give it a set of inputs that are similar, but not the same as the ones it saw. It will then produce an output that is like the output. So it's flexible in the way it operates."

On whether machines can have the same kind of capabilities as humans:
"I think that you could model what you think it is to be intelligent of conscious, and you'll know more about it as a result, and you can check out your explanation and make sure it works, because you've built a model. But you're still only modelling it and I don't think you've done anything like actually creating something that's really having experience of the world."

On the possibility that A.I. will have a sort of consciousness which is even better than ours:
"I think because of something about how conscious evolved, you're not going to be able to create anything like it. Because you can use evolutionary mechanisms to create something that you could say was similar, but you're using them towards a goal, you're indirectly programming the thing to do it. So you're rather back to the original symbolic A.I.

If you look at what we have achieved, at what systems we have that seem to be intelligent, we don't have systems then really. You could model an aspect of intelligence, but we don't have anything that is a whole intelligent system. And my hunch is that it is in principle impossible to go further. If you create an artificial system its not integrated with the environment and actually motivated by itself. You've still got an external person who is making it do that."

On the importance of evolution in A.I.:
"You can use evolutionary techniques. It's interesting to see what kind of behaviour you can evolve with a simple reactive mechanism, but then the hardware isn't evolving - it's abstraction of the real thing."

On the future:
"I think that we'll certainly end up with robots doing simple tasks for us where you don't need to worry about whether they're intelligent or not, for example, vacuuming the floor. Or similarly using neural networks, you could perhaps have a neural network that made your car go at the appropriate speed or not bump into the car in front. So I think we will certainly see artificial intelligence used in our daily environment in that kind of way."

Further Reading
Intelligent Systems for Engineers and Scientists (second edition) by Adrian A. Hopgood, published by CRC Press (2000), ISBN: 0849304563.
Part of the OU course, Artificial Intelligence for Technology (T396), is based on this book.

Professor Aaron Sloman's out of print book is now online - The Computer Revolution in Philosophy: Philosophy, science and models of mind

Combining Artificial Neural Nets: Ensemble and Modular Multinet Systems, Amanda Sharkey (Ed), Springer-Verlag, London (1999), ISBN 1-85233-004-X.

Computing Machinery and Intelligence, Alan Turing, Mind, 1950.

Brainchildren: Essays on Designing Minds, Daniel Dennett, MIT Press, 1998.

Defending AI Research : A Collection of Essays and Reviews, John McCarthy, CSLI Publications 1997.

Jargon Buster

A.I.

The science and engineering of making intelligent machines, especially intelligent computer programs.

Cognition

The mental activity by which an individual is aware of and knows about his or her environment, including such processes as perceiving, remembering, reasoning, judging and problem solving.

Computer Vision/Machine Vision

The combination of electronic eyes and brains allowing a system to see an object, make some decisions about that object and, in certain cases, implement an action based on those decisions.

Expert Systems

Systems that use human knowledge to solve problems that normally would require human intelligence.

General Problem Solver

A theory of human problem solving stated in the form of a simulation program.

Geometry Theorem Prover

A computer tool for proving elementary theorems in geometry.

Knowledge Representation

The study of how knowledge about the world can be represented and what kinds of reasoning can be done with that knowledge.

Language Analysis

Computer system that will analyse, understand, and generate natural human-languages.

LISP

List Processing Language, a programming language generally regarded as the language for A.I., ideal for representing knowledge from which inferences are to be drawn.

Logic Theorist

The first expert system, used to prove mathematical theorems.

Neural Networks

An information processing technique based on the way biological nervous systems, such as the brain, process information.

Robot

A device that can move and react to sensory input.

Robotics

The study and technology of robots.

SAINT

Symbolic Automatic INTegrator, an early problem solving program.

Search

The finding of a path from a start state to a goal state.

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