Intelligence and the Brain: A Quick Summary

This is the concluding posting of the talk ‘Intelligence and the Brain’ which simply summarizes major points in a small number of bullet points:

  • Intelligence has been presented here with reference to Karl Friston’s ‘Free Energy’ tentative theory of how the brain works.
  • It potentially bridges the explanatory gap between the physical behaviour of neurons and the psychological behaviour of the brain.
  • Friston’s Free Energy theory is about ‘the minimization of surprise through action and perception’ and I have tried to explain what this means.
  • Putting this more colloquially at a behavioural level, intelligence is about having predictions, adjusting them in the light of experience and making an effort to improve them.
  • An aspect of that last part is building better mechanisms to improve our intelligence beyond the limits of our own brains, building more and more advanced models in the outside world.
  • Not the least of our extending mechanisms is the social dimension, with the cooperation of individuals which leads ultimately to scientific knowledge.
  • The social dimension can extend intelligence, whether the individual members of the society are simple creatures, sophisticated social animals or advance beings each using advanced models in the outside world.
  • A two-dimensional map of intelligence is proposed, extending Daniel Dennett’s concept of  ‘the Tower of Generate and Test’.
  • The abstract, behavioural model of intelligence is mapped to the physical structure of what neurons are doing within the cortical columns of the brain.
  • The brain’s neocortex comprises many millions of these cortical columns.
  • At the simplest level of understanding, these columns create a hierarchy with each column communicating with those above and below them in the hierarchy.
  • In this way, ‘surprise’ is minimized at all levels – from low-level (sub-conscious) to our highest levels of understanding.
  • Each cortical column provides just a small increment in the organism’s intelligence. But, as Lao-tzu said, a journey of a thousand miles begins with a single step. The overall effect is a sophisticated ‘prediction machine’, interacting in its environment.
  • Each layer in the hierarchy is trying to ‘pull’ towards a most-likely ‘opinion’ – acting against entropy.
  • Intelligence can be understood in terms of Shannon entropy (in information theory).
  • Intelligence and life are related through entropy: intelligence is about counteracting Shannon entropy in the same way that life is about counteracting thermodynamic entropy.
  • Life embodies intelligence but intelligence can exist apart from life.
  • As such, artificial intelligence is no less real an intelligence than natural intelligence (although it is currently vastly inferior).
  • Analogies are made between intelligence and the slope of a hill and between intelligence and a chaotic pendulum as examples to help visualize how intelligence builds up over the many layers in the hierarchy to provide the agent with the freedom that helps protect it from its environment, and others.
  • Prediction and unpredictability are two sides of the same coin.

As an even shorter conclusion:

  • Intelligence has been described as a physical process, arising within the brain which has an interesting relationship with life, information theory, entropy, prediction and freedom.
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