Learning by conditioning and learning by discovery

I had an “entertaining” discussion with two fellows – l call them A and B -, which taught a lot, I hope also for A and B -, and actually gave a good example of two kinds of learning. Learning by conditioning and learning by discovery.

[This discussion by the way gave me good laughs. A and B - first summarized his academic background by "studied strings" and second was Bachelor in computer science but pretending to be M-theorist. They tried to demonstrate that I am a crackpot. They carried out an "investigation" following the principles of investigations made for witch candidates at middle ages. The victim had two options: she drowns or not in which case she is burned at stake.]

1. Learning by conditioning

Returning to learning. The first kind of learning is learning by conditioning, which deep learning algorithms try to mechanize. Second kind of learning is learning by discovery. The latter is impossible for computers because they obey deterministic algorithm and are unable to do anything creative.

Emotions play a strong role in the learning by conditioning in the case of living systems and in the simplest form it is learning of X-good and X-bad type associations helping C elegance to survive in the cruel world. In case of humans this kind of associations can be extremely dangerous as for instance the course of events in USA has shown.

Very large part of our learning is just forming of associations: this is what Pavlov’s dogs did. In school we learn to associate to “2×3=” symbol “6″. In our youth we learned also algorithms for sum, division, multiplication and division, and even for finding the roots second order polynomial. Often this is called learning of mathematics. Later some mathematically gifted ones however discovered that this is just simple conditioning of an algorithm, and has very little to do with genuine mathematical thinking. The discovery of the algorithm itself would be mathematical thinking. The skill to code for algorithm – usually given – is also an algorithm and it can be also coded in AI.

If we are good enough in getting conditioned we get a studentship in University and learn science. This involves also learning of simple conditionings of type X-good and X-bad. In this learning social feedback from others reinforces learning: who would not like to earn the respect of the others!

For X-bad conditionings X can be homeopathy, water memory, cold fusion, telepathy, remote viewing, non-reductionistic/non-physicalistic world view, quantum theories of consciousness, TOEs other than M-theory, etc… For X-good conditionings X can be physicalism, reductionism, strong AI, superstrings, Witten, etc…

The student learns also to utter simple sentences demonstrating that he has learned the desired conditionings. This is important for career. Proud parents who hear the baby say their first word encourage the child. In the same manner environment reinforces the learning of “correct” opinions by a positive feedback. The discussion with A and B ga a quite a collection of these simple sentences. “I guessed that he is a crank” from A is a good example intended to express the long he life experience and wisdom of the youngster.

These conditionings make it also easy “recognize” whether someone is a crank/crackpot/etc… and even to carry out personal investigations – analogous with witchcraft investigations at middle ages – whether some-one is a crank or not. This is what A and B in their young and foolish arrogance indeed decided to carry out.

2. Learning by Eureka experience

There is also second kind of learning. Learning by discovery. Computers are not able to do this. I mentioned in the discussion what happens when you look certain kind of image consisting of mere random looking spots in plane. After enough staring suddenly a beautiful 3-D patterns emerges. This is a miracle like phenomenon, eureka experience. Quantum consciousness based explanation is the emergence of quantum coherence in the scale of the neuronal cognitive representation in visual cortex at least. New 3-D mental image emerges from purely 2-D one. One goes outside of the context.

The increase of dimension might provide an important hint about what happens more generally: and this would indeed occur for the dimension of extension of rationals in eureka quantum jump in TGD based model of what could occur. Physically this would correspond to the increase of the effective Planck constant h_eff= n*h_0, h=6*h_0 assignable to the mental image created by the image. n is indeed the dimension of extension of rationals and would increase and also scale of quantum coherence would increase from that of single spot to that for the entire pictures.

This kind of learning by eureka is probably very common for children: they are said to be genii. Later the increasing dominance on the learning by conditioning often eliminates this mode of learning and the worst outcome is a mainstream scientist who is hard-nosed skeptic. Solving genuine problems is the manner to gain these learning experiences but they come only now and then. Some of them are really big: during my professional career there have been – I would guess about 10 really big experiences of this kind involving discovery of a new principle or totally new physical idea.

The discussion was initiated by a totally non-sense hype about transferring consciousness of C elegance to computer program. Concerning deep learning and AI in general. We had two years ago collaboration about AI, IIT approach to consciousness, and about a possible connection with remote mental interactions together with Lian Sidorov and Ben Goertzel, who is behind Sophia robot.

There two chapters related to this (see this and this). I think that the latter chapter is published in a book by Goertzel.

There is also a critical article inspired by Sophia robot about which Ben Goertzel wrote an enthusiastic article and sent to Lian Sidorov and me (this).

For a summary of earlier postings see Latest progress in TGD.

Articles and other material related to TGD.