Curious Universe, Us and AI

Photo by Pixabay: https://www.pexels.com/photo/clear-glass-sphere-302743/

Curious Universe, Us and AI

“Somehow the universe has a tendency to be as interesting as possible. As time goes on it becomes more and more diverse, more and more interesting”.

“To be as interesting as possible the universe must be conscious of itself”.

According to Freeman Dyson, these are two parts of the same equation.[1]

Motoo Kimura’s neutral theory of molecular evolution, which proved itself highly effective in evolutionary genetics, demonstrates that evolution is maximizing diversity.[2]

Ross Ashby’s law of requisite variety, the first law of cybernetics, states that systems, which seek to preserve themselves by controlling their environment, have to be at least as diverse in their responses as the environment is in its challenges.[3]

Erwin Schrodinger proposed the idea of single unitary consciousness in order to explain the arithmetical paradox that many conscious observers experience the same universe.[4]

The conclusion from the above can be that life is a parasite that hijacks the unitary consciousness of the universe to become conscious of itself with the purpose to secure its own spread and survival.

The parasitic nature of life shouldn’t discourage us. The emergence of life couldn’t occur without the free will of the universal consciousness. It means that it was the most interesting twist of events from the point of view of the universe.

If we assume that the universe is conscious it offers an interesting twist to Hugh Everett’s (and H.-D. Zeh’s) many worlds/many minds interpretation of quantum theory.[5–8] One unitary mind of the universe doesn’t need to split into many after measurement/ decoherence to retain the determinism of a pure wave function.

If the universe is self aware and has free will then it decides with its free will which world should materialize from the superposition of all possible worlds. One mind means one world without Everett’s interpretation of quantum theory losing its completeness and consistency.

Freeman Dyson gave us the criteria of the choice the universe is making. It always chooses the most interesting course of events from all possible ones.[1]

Karl Friston’s active inference theory explains that curiosity is the ultimate driving force of the universe’s lust for the most interesting options.[9,10]

The most interesting option means the least expected before the choice is made. After that, the level of surprise decreases very rapidly. Presumably at the speed of decoherence. Life has to mimic the behavior of the universe in order to preserve itself.

Therefore, life should maximize the level of surprise in order to predict changes in the environment and minimize it in order to adapt to their consequences. Getting ready to forthcoming changes and adapting to already taken place are the two processes which occur not subsequently but simultaneously. They go in opposite directions.

Using the terminology of Karl Friston’s free energy principle, life is simultaneously hunting for variational free energy (maximizes it) and consuming it as a prey (minimizes it).

The hypothesis of the unitary consciousness (mind, intelligence) of the universe together with the ontic (many worlds) interpretation of quantum mechanics adjusted to the notion of one observer with different perspectives links quantum computing performed by all living beings to their mental states rather than to physical states of their nervous system or other substrate.

Following the suggestion of Erwin Schrodinger that universal unitary consciousness manifests itself in us to the highest degree when we are learning [11] we can assume that coherent pure quantum mental states in superposition inhibit mental states in decoherence (entanglement with local classical environment) during learning. Activation of classical mental states inhibits quantum mental states when we act in the classical environment, respectively.

Furthermore, one might like to make an analogy between human episodic memory [12] and the classical appearance of the universe. We experience the classical world in episodes (observations), the arrow of time is a storyline keeping episodes in order, memories are solid but they dissipate with time (entropy), etc.

This all is highly speculative, of course, but it might provide a fresh alternative perspective on the design of real artificial intelligence that, to be honest, is still on the theoretical stage.

This is an example of how it may work in practice:

Unexpected uncertainty (ambiguity) is the key indicator of structural changes in the environment.[13] Primary learning is resolving unexpected uncertainty by establishing probability connections between stimuli without response.[14–16]

Primary learning consumes less energy than activation or secondary learning (stimulus-response) that requires many repetitive activations.[17]

Primary learning may be done at least partially by quantum computations with near zero energy consumption.

Primary learning is an integral part of life. It’s much more abundant than we think because even the processes which we consider nearly deterministic always contain an element of unexpected uncertainty that requires adjusted behavior.

Primary learning may be modeled as obtaining quantum probabilities and building on the basis of them heteroclinic networks in the phase space of mental states.

Predictions of forthcoming changes in the environment can be obtained by a living system by mentally measuring an imaginary system that demonstrates unexpectedly stochastic behavior; building a pure state vector for that imaginary system by moving it mentally into the quantum coherence state of superposition; and later reducing the state vector of that system to the probability distribution density matrix as in the case of a physical measurement (interaction with the environment).

The density matrix then can be mentally represented as a heteroclinic network in the mental phase space [18,19] with vertices representing states of the imaginary system and edges representing transitions between states with probability weights. Physical substrates may become adjusted to the new mental network by morphogenesis to secure its integration with activation mechanisms.[20, 21]

References:

1. Dyson, Freeman. Interview with Rigas Laiks. Spring 2016
2. Kimura M. The neutral theory of molecular evolution: a review of recent evidence. Jpn J Genet. 1991 Aug;66(4):367–86. doi: 10.1266/jjg.66.367. PMID: 1954033.
3. Ashby W.R. (1958) Requisite variety and its implications for the control of complex systems, Cybernetica 1:2, p. 83–99.
4. Schrödinger, Erwin. Mind and Matter, 1959, University Press, page 53.
5. Everett, Hugh, “Relative State” Formulation of Quantum Mechanics, Rev. Mod. Phys. 29, 454 — Published 1 July 1957, DOI: https://doi.org/10.1103/RevModPhys.29.454
6. Everett, Hugh, The Theory Of The Universal Wave Function, The Many-Worlds Interpretation of Quantum Mechanics, Princeton University Press Princeton, New Jersey, 1973
7. Zeh, H.-Dieter, The strange (hi)story of particles and waves, 2018, arXiv:1304.1003v23 [physics.hist-ph], https://doi.org/10.48550/arXiv.1304.1003
8. DeWitt, Bryce, Quantum mechanics and reality, Reprinted from Physics Today, Vol. 23, №9 (September 1970).
9. Friston, K. The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11, 127–138 (2010). https://doi.org/10.1038/nrn2787
10. Fields, Chris , Friston, Karl, Glazebrook, James F., Levin, Michael. A free energy principle for generic quantum systems. Progress in Biophysics and Molecular Biology 173: 36–59, 2022
11. Schrödinger, Erwin. Mind and Matter, 1959, University Press, page 9.
12. Tulving, Endel. (2002) Episodic Memory: From Mind to Brain. Annual Review of Psychology 2002 53:1, 1–25
13. Payzan-LeNestour E., Dunne S., Bossaerts P., O’Doherty JP. The neural representation of unexpected uncertainty during value-based decision making. Neuron. 2013 Jul 10;79(1):191–201. doi: 10.1016/j.neuron.2013.04.037. PMID: 23849203; PMCID: PMC4885745.
14. Ivan Pavlov (1933) Psychology as a Science. Unpublished and Little-known Materials of I.P. Pavlov (in Russian, 1975)
15. Edward Thorndike (1898) Animal intelligence: An experimental study of the associative processes in animals. Monograph Supplement №8
16. Edward Tolman (1948) Cognitive Maps in Rats and Men
17. Fields, Chris and Levin, Michael. Metabolic limits on classical information processing by biological cells. (August 2021)
18. Voit, Maximilian and Meyer-Ortmanns, Hildegard. Dynamics of nested, self-similar winnerless competition in time and space. Physical Review Research (6 September 2019)
19. Thakur, Bhumika, Meyer-Ortmanns, Hildegard. Heteroclinic units acting as pacemakers: entrained dynamics for cognitive processes. 2022 J. Phys. Complex. 3 035003; DOI 10.1088/2632–072X/ac87e7
20. Shors, T. J., Anderson, M. L., Curlik, D. M., 2nd, & Nokia, M. S. (2012). Use it or lose it: how neurogenesis keeps the brain fit for learning. Behavioural brain research, 227(2), 450–458. https://doi.org/10.1016/j.bbr.2011.04.023
21. Shors, Tracey J. From Stem Cells to Grandmother Cells: How Neurogenesis Relates to Learning and Memory. Cell Stem Cell, September 11, 2008, DOI: https://doi.org/10.1016/j.stem.2008.08.010