Pentti Haikonen comments invited by Conscious-Robots.com editor on the recent article about Haikonen's Architecture for Conscious Machines written by Trung Doan.

I wish to thank Trung Doan for his analysis of my approach towards machine consciousness here. Trung Doan has done a lot of work here as he illustrates the main principles with practical examples, which are worked out along the principles that I present in my book "Robot Brains".

Dr. Haikonen

Trung Doan notes correctly that the neural machinery does not operate with numeric values, instead the individual signals represent elementary features of sensed entities and these are the basic meaning of these signals. It is useful to note that in the machinery groups of signals that represent some sensed entity may be used to stand for completely different things; these signal groups act as symbols for these things. This is a necessary prerequisite for e.g. natural language and inner speech.

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Pentti Haikonen's architecture for conscious machines

By Trung Doan (doanviettrung a_t gmail dot com). 

Haikonen's contribution to the machine-consciousness endeavor is an architecture based on cognitive principles. He also developed some electronic microchips as a first step to building a machine based on that architecture.

Below, we look at how a Haikonen machine might achieve consciousness once built, by examining some of its cognitive capabilities, and in the process will briefly discuss the Haikonen architecture.

The Haikonen machine perceives

Robot Brains by Pentti Haikonen

The machine does not internally represent the ball as a round graphic, nor a set of numbers representing diameter, color, etc., but by this signal array. Haikonen calls this a "distributed signal representation".

Suppose the machine is shown several balls of different sizes, colors, etc., one at a time, and each time its microphone hears the sound pattern we humans understand as the word "ball". Because they appear at the same time repeatedly, the machine associates the sound pattern and the visual pattern together. The making of associations is how the machine's perception is done.

After several different balls are associated with that sound pattern, the machine finally learns to associate the "ball" sound pattern with anything that is round.

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