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Machines, minds and computers

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Computers are becoming smarter and may soon become intelligent. This free course, Machines, minds and computers, looks at what intelligence is, how computers may become so, and whether they ever will really be intelligent. It is aimed at people interested in understanding what intelligence and thinking really are, and who want to understand the underpinnings of our ideas about them.

Course learning outcomes

After studying this course, you should be able to:

explain the distinction drawn in this course between artificial intelligence and Symbolic AI
describe various possible tests for machine intelligence
explain the concepts of a computer model and of an optimisation problem
distinguish between a simulation, a replication and an emulation
distinguish between strong and weak artificial intelligence.

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First Published: 10/08/2012

Updated: 06/12/2017

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Course content

Introduction
Learning outcomes
1 Machines
2 Minds
3 AI
4 Computers
5 Conclusion – Symbolic AI and Cybernetics
- 5 Conclusion – Symbolic AI and Cybernetics
Keep on learning
References
Further reading
Acknowledgements

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Sankalp Shukla 19 August 2022 3:50PM

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Very helpful course

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Andrew Morris 28 November 2020 12:30PM

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This course is technically out of date. While questions about the relation of mind to machines are more or less timeless, in so far as this course attempts to discuss this question in the light of current AI technology, it is very much out of date. It seems to have been written in the mid 1980s, when the author was working in AI. That is also when I started working in AI and have continued to work ever since. AI has made many major advances since the 1980s, especially over the last 10 years.

The definition given for Strong AI is incorrect. The author gets off to a bad start by giving prejudiced and outmoded definitions for "Weak and Strong AI". Strong AI has never required a computer to have a "mind". Definitions more in keeping with contemporary usage are given by Wikipedia (https://en.wikipedia.org/wiki/Strong_AI). Strong AI would exhibit general intelligence matching that of humans, whereas Weak AI only needs to show intelligence in a task specific field. Having incorrectly claimed that Strong AI requires a machine to have a mind, this course presently claims that all digital computation is essentially symbolic and that while it may someday simulate intelligent behaviour, it will never achieve Strong AI because symbolic computation can never be conscious. As pointed out in Turing's paper "Computing Machinery and Intelligence", which the course references (with an incorrect link), apart from our own mind, the possession of mind by another being can only ever be judged by its objective behaviour.

Neural models are not symbolic-rule based. Though using digital computation, data driven statistical modelling is based on structures inspired by the brain and which optimise mathematical information-theoretic quantities. Such models are not rule based. The only rules a computer follows are the physical laws used to construct the devices used to implement its instruction set. These laws are no more artificial than as the laws which govern our own brains.

There is almost no mention of the great advances made with artificial neural networks. From the technical point of view, the mid 1980s is also when the "back-error-propagation" technique was first popularised as a method for training any multi-layer artificial neural network (ANN). As such ANNs were known to be theoretically able to perform any associative mapping or classification, given sufficient neurons and training data, this set off an explosion of interest in artificial neural networks which has continued to this day, and yet this course barely mentions. Distributed processing is mentioned only in relation to concurrent software agents and not deep learning. It is claimed that Strong AI will require machines to learn from their environment. Many applications of deep learning now operate either without any human supervision (variational autoassociation) or with only environmental feedback (reinforcement learning).

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Machines, minds and computers

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