Quantum (Mental) Engine of Primary Intelligence
The mathematical formulation of the Heisenberg uncertainty principle is a mathematical generator of pure randomness. [1]
A friend, a video game developer, once explained to me that they usually use pseudo-random number generators, which are finite sequences of purely random numbers. The difference may seem insignificant, but Kolmogorov strictly indicated to us that any finite sequence of random numbers will sooner or later repeat itself. [2] Hence the prefix “pseudo” in the name. Some games, according to this friend, used quantum physical generators of pure randomness. The word “quantum” sounded wild to me at that time.
My interest in pure randomness generators then, many years ago, was dictated by the search for a source of unexpected uncertainty. [3] I already knew that it is the resolution of unexpected uncertainty that trains primary intelligence, and I was obsessed with the idea of creating a computer game that develops primary intelligence in people and machines.
It was the primary intelligence, responsible for obtaining new knowledge from uncertainty and making decisions based on this knowledge in conditions of uncertainty, that I already considered the basis of humanity in people, because only it distinguished people from machines.
Unexpected uncertainty differs from expected uncertainty in that the probabilities of a particular outcome are unknown. When we resolve unexpected uncertainty, we learn these probabilities and then we are left with only expected uncertainty, which no longer develops primary intelligence.
Unlike unexpected uncertainty, expected uncertainty is quite easily automated, and therefore I considered (and keep considering) secondary intelligence, which works with it, an attribute of a machine, not a human being.
Unexpected and expected uncertainty is an invention of neuroscience. Neurophysiologists often call them ambiguity and risk. [3,4]
The simplest way to resolve unexpected uncertainty is frequentist, statistical. When you repeat the same action with a random outcome many times in a row, you accumulate statistics on the frequency of this or that outcome. Unexpected uncertainty becomes expected, but not completely, because you are only dealing with those outcomes that are repeated often enough for you to determine their frequency or even know that such outcomes can happen.
The quantum method for resolving unexpected uncertainty works completely differently. First, it gives a complete list of all possible outcomes, including those that happen extremely rarely, those with extremely, but not vanishingly small probability of occurrence.
Second, a forecast of the probability distribution for an unlimited period is obtained immediately based on the results of only one event.
The mathematical apparatus of the Heisenberg uncertainty principle generates an infinite series of random numbers. That is, it is not pseudo or physical, but a purely mathematical generator of pure randomness, that is, unexpected uncertainty. The mathematical apparatus of quantum mechanics successfully transforms it into expected uncertainty. This transformation fully corresponds to the primary cognition of which only the primary intelligence is capable.
Primary intelligence not only makes us human. It, in principle, distinguishes systems that think for themselves from systems for which someone else thinks.
Quantum mechanics is compatible only with one primary intelligence. And we do not need more. It is enough for us to create one, and whether it will be a separate local intelligence or only a local manifestation of the only intelligence of the entire Universe, we will find out empirically based on the results of the birth of the primary intelligence, in the artificial carrier we have prepared.
Now I know the answer: only infinite uncertainty can be resolved.
God, I found a purely mathematical, mental generator of unexpected uncertainty! That is the real mental engine of primary intelligence. Thank you 🙏.
References:
- Dirac, Paul (1930). The Principles of Quantum Mechanics. Oxford, Clarendon Press.
- Transcript of the report by A. N. Kolmogorov The concept of information and the foundations of the theory of probability (in Russian)
- Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681–692. https://doi.org/10.1016/j.neuron.2005.04.026
- Blankenstein, N., Crone, E.A., Bos, W.V., & Duijvenvoorde, A.C. (2016). Dealing With Uncertainty: Testing Risk- and Ambiguity-Attitude Across Adolescence. Developmental Neuropsychology, 41, 77–92
