Intelligence

Rare, but wonderful

The outcomes that make for success in any game have something profound in common. They are statistically unlikely.

Imagine all the possible futures ahead of a player. There will be a vast array of possibilities.

Now imagine the possible futures in which the games go well for the player. These desirable futures are a tiny fraction of all the futures that could possibly come to pass. So it’s highly unlikely that they would be reached by chance alone.

Randomness and order

This unlikeliness turns out to be connected with order and with information.

When our universe began, all existence was compressed into a tiny volume of immensely dense energy. Entropy was at a minimum, order at its maximum.

Since then, the universe has expanded and cooled and with every event the entropy increased. The distribution of energy in the universe becomes a little less organised.

Life and intelligence

Living things are special because they maintain highly ordered states of affairs within their bodies. They behave non-randomly and consequently arrive at non-random futures.

Life builds order.

Another way of putting this is that life builds structures that are information-rich, for order is mathematically equivalent to information.¹ It is the non-random arrangement of pieces of anything.² There is information encoded in the pattern of tiny pits on a CD, for instance, and information within the music that comes out of the speakers when the CD gets played. There is information encoded in the order of base-pairs in strands of DNA and information encoded in the structures of a human kidney.

Intelligent action

It makes sense to think of life’s information-building as intelligent action.

This is the core feature of all living systems. In the face of the vastness of possibility within the universe, living things use the order embedded in them to behave with the precision needed to build up new information-rich structures.

It’s easy to assume that intelligence is about the ability to reason. But, fundamentally, it’s about the ability to propagate information.

Notes

1. The most fundamental definition of entropy is the statistical definition, as all other definitions can be derived mathematically from it, but not vice versa. One way of formulating this statistical entropy is S = -k·∑·[Pilog(Pi)], where ‘S’ is the entropy, ‘k’ is an arbitrary constant (Boltzmann’s constant), Pi is the probability that particle i will be in a given microstate, and the ∑ tells us to calculate Pilog(Pi) for every particle in the system and add all our results together. This is exactly the same as Shannon’s formula for the capacity for uncertainty within a system, S = -k·∑·[Pilog(Pi)], where S is ‘Shannon’s entropy’ and Pi is the probability for the value of a given bit of information i.
2. The field of information theory was pioneered by Claude Shannon in the middle of the 20th century. Shannon was a mathematician and engineer who had worked on one of the first analogue computers in the 1930s, before going on to show how the electromechanical switches of telephone systems could be used to perform logical tasks. His landmark 1948 paper, A Mathematical Theory of Communication, explored how the information content of a message that was to be reproduced in another place could be quantified. “The fundamental problem of communication is that of reproducing at one point exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages.” C. E. Shannon, A Mathematical Theory Of Communication, Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, July and October, 1948, introduction.