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The computational perspective

In this step, we look at the computational perspective on intelligence, a key influence on the early development of AI.

The early pioneers we encountered in the previous step can be seen as following a computational perspective to intelligence.

The computational perspective

In the computational sense, intelligence is seen as the ‘processing of information’ by the resources of ‘hardware’ (the body) and ‘software’ (the mind). The computational approach to intelligence, associated with Claude Shannon (1948) also follows the input, process, output model, similar to what happens with a computer.

This image represents the input, process, output model. Input is sensory perceptions. Process is knowledge, memory, decision, interference, learning. Output model is output and interaction. Click to expand

(Adapted from Jou 2019)

According to the computational model, input is made up of sensory perceptions, processing is made up of knowledge and memory, decision and interference and learning, and output is output and interaction, see diagram (Jou 2019). In many ways, the computational perspective can be summed up in the statement associated with the cognitive scientist Marvin Minsky, that the brain is a ‘meat machine’ (Levy 2016).

Limitations of the computational perspective

Fischler and Firschein (1987) point to human and machine limitations in the computational perspective on intelligence, and with respect to hardware;

…this operates according to strict mechanical laws. In the case of computers, the electronic mechanism constitutes the formal system, while for the human, the formal system is the neural structure. Therefore, there will be truths unknowable by both man and machine.
(Fischler and Firschein 1987: 46)

The importance of failure and error

Humans are hardwired for failure and being wrong is part of survival. Fischler and Firschein (1987) remind us:
From the day of birth… upward of 1,000 neurons die in the human brain and are not replaced. How can the brain continue to function under such conditions, since the loss of even a single component in a modern digital computer will typically render it inoperative?
… Even more to the point, some biological mechanisms appear to be deliberately designed to take advantage of failure and error in their physical components.
(Fischler and Firschein 1987: 49-51)
n.b. upward of 1,000 neurons die in the human brain die per day

Your task

How useful do you consider the information processing approach of understanding the working of the human brain to be? See if you can find an article to support your findings.
Post your responses to the comments area.

Further reading

Knapp, S. (2006, July 24). Artificial Intelligence: Past, Present and Future. Vox of Dartmouth: The Newsletter for Dartmouth Faculty and Staff


Fischler M. A. and Firschein, O. I. (1987). Intelligence: the Eye, the Brain, and the Computer. Addison-Wesley

Jou, S. (2019). AI & Machine Learning 101 – Part 1: Machine vs. Human Learning. Interset

Levy, S. (2016). Marvin Minsky’s Marvellous Meat Machine. Wired

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3)

© Coventry University. CC BY-NC 4.0
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