Has anyone ever programmed a computer system to evolve theories?

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Discussion Overview

The discussion revolves around the concept of programming a computer system to evolve theories based on experimental data. Participants explore the feasibility of such a system, the definition of 'theory' in a computational context, and the potential methodologies for generating and refining theories through data analysis.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • Some participants propose feeding experimental data into a computer to generate theories or mathematical structures that fit the data, suggesting an evolutionary approach where successful theories are slightly mutated and tested against new data.
  • Others question how a computer would define 'theory', raising concerns about the necessity of definitions for the evolutionary process to function effectively.
  • A few participants suggest that the system could start with existing theories and modify them in numerous ways, testing against data to find viable theories, even if this approach may involve discarding many unsuccessful hypotheses.
  • Some contributions highlight the challenges of programming a computer to recognize relationships and patterns without predefined criteria, suggesting that the system should evolve its own understanding of these concepts.
  • There are references to historical perspectives on neural networks and the complexity of creating systems that can autonomously derive theories from data, indicating skepticism about the simplicity of the proposed approach.
  • A later reply discusses the potential of quantum computing in relation to this concept, suggesting that it may offer new avenues for theory evolution based on fundamental physical systems.
  • Participants express uncertainty about quantifying theories for a computer to manipulate, noting that while equations might be deduced, the semantic nature of theories presents additional challenges.

Areas of Agreement / Disagreement

Participants generally do not reach consensus on the feasibility of programming a computer to evolve theories, with multiple competing views on the definition of 'theory' and the methodologies for achieving this goal remaining unresolved.

Contextual Notes

Limitations include the ambiguity surrounding the definition of 'theory' in a computational context, the challenges of quantifying generalizations for computer understanding, and the unresolved nature of how a computer could autonomously evolve its understanding of theories.

  • #31
gk007 said:
To create new theories computer would have to think imaginatly, which goes against pretty much everything of what a computer is. A computer follows a fixed alogrithm, and although it can modify this alogrithm, a computer could never spit out something like string theory, because that requires imaginative thinking.

Nah...If your brain can do it, so can a computer. Just an engineering problem at this point.
 
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  • #32
Meatbot is correct. Imagination is simply a feature of a very complex system. Have a computer complex enough and it'll be imaginative.
 
  • #33
You would have to create a neural network with as many neurons as a human brain, which is several billion, and each of those neurons would have thousands of connections, but I suppose that is just an engineering problem...
 
  • #34
i think you could model imagination (to a degree at least) by throwing in some randomization. that's (part of) how the evolved circuits that Meatbot mentioned arrived at their "imaginative" solutions.
 
  • #35
gk007 said:
You would have to create a neural network with as many neurons as a human brain, which is several billion, and each of those neurons would have thousands of connections, but I suppose that is just an engineering problem...
There's also mimicing the subtletly of the stimuli and reinforcement between neurons.
 
  • #36
Proton Soup said:
i think you could model imagination (to a degree at least) by throwing in some randomization. that's (part of) how the evolved circuits that Meatbot mentioned arrived at their "imaginative" solutions.

Modeling imagination seems like it could be done. How about modeling creativity? What if you have it to throw out random cancepts/situations/problems that are at first glance probably unrelated to the problem at hand, and then have it look for similarities between them. It also examines the other attributes of the 2nd item that don't SEEM TO match and considers whether they might really match somehow if you thought about it.

Take a lamp and a desk fan. Both have mass. Both use electricity. Both are made of quarks. Both are plastic. Both are white. Etc... Possibly useful. Ok, now what about a quality of the fan that doesn't seem to be present in the lamp at first glance. A fan makes air move. At first glance, most people would not say a lamp makes air move and would overlook that when listing the qualities of a lamp. But it does make air move by heating it, causing it to rise. A fan also cools people off. So ask if a lamp cools people off. I bet nobody ever asked that question before. Well, I suppose it might. Maybe it makes hot air rise above it, pulling cooler air in the bottom to replace it and creating a cooling air current. Even harder: a fan creates a force that tries to accelerate it. Does a lamp do that? Maybe. Does a lamp have something that spins? Does a fan create light? Maybe doing this kind of thing creates useful insight.

You can do the same thing with cause and effect, with a variable thrown in:
"x causes mass" vs. "removing energy from water causes ice"
So, possible questions (which can be starter theories as well):
- Is mass caused by a modification of something that already exists?
- Does removing energy from something create mass?
- Is mass equivalent to a solid?
- Is there a "liquid" form of mass?

Just throwing stuff out there...a rough sketch.

An interesting related link, the Theory of Inventive Problem Solving. Some of these techniques could be applied: http://www.mazur.net/triz/
 
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