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I know nothing about ML, but as far as "maybe human thought isn't Turing complete:" If you teach a human how a Turing machine works, they are quite capable of

I know nothing about ML, but as far as "maybe human thought isn't Turing complete:" If you teach a human how a Turing machine works, they are quite capable of simulating it with paper and pencil with a high probability of each step being correct. If you give several humans the same problem and have them work through it, keeping track of their work, then you can find points of divergence and go with the majority solution, then recalculate starting from the divergence. Prior to electronic computers, groups of humans using pencil, paper, reference-tables (that themselves had been hand-calculated by humans), and slide-rules worked this way to get accurate answers to arbitrarily difficult computations. That is to say that while individual humans might arguably not be Turing complete, groups of humans, with the right manipulatives, are limited only by storage, so are as Turing complete as any general-purpose electronic computer. Given that these recent language models are both non-deterministic, and significantly more human-like in the mistakes they make, I see no reason why similar techniques could not be used, given proper training. It does seem rather inefficient, since for problems in which a well-defined algorithm exists we can already program a computer to do it far better than I suspect any LLM would do.

deanCommie 4y ago
> Prior to electronic computers, groups of humans using pencil, paper, reference-tables (that themselves had been hand-calculated by humans), and slide-rules worked this way to get accurate answers to arbitrarily difficult computations. There's a scene of this in Apollo 13. Line of humans with slide rulers manually confirming calculations made on the craft whose computer is out.
WorldMaker 4y ago
Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how human-like neurons seem to work (primarily in isolation studies in a petri dish or similar experiments), but it is very easy to forget that the model/analogy is not the reality when you work with those models a lot. The current "map" certainly doesn't seem to be the territory here. Neurons are just one part of the human brain, there's also complex chemical receptors and hormonal pathways that we mostly still only grasp in the abstract of how they interplay with neurons and other signal pathways. Additionally, there's indirect evidence that brains may take some advantage of quantum effects among/between neurons such as entangled particle states, but we don't know a lot more than we know about the quantum states of human brain activity. (How do we even probe that in a living human with our current knowledge of quantum physics?) Neural Networks/Perceptrons have been very useful models in computation, but that doesn't mean they are actually good or representative models for human cognition.
- aidenn0 4y ago
  > Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how human-like neurons seem to work (primarily in isolation studies in a petri dish or similar experiments), but it is very easy to forget that the model/analogy is not the reality when you work with those models a lot. I'm pretty sure that you were making a tangent rather than a rebuttal, but I did want to clarify that I'm not saying that these large models replicate the structural thinking of humans. Instead I'm saying that, given a ML model that is "sufficiently human like" in the mistakes it makes, using techniques that prevent humans from making those mistakes (e.g. showing work, having multiple agents calculate in parallel) could demonstrate an ability to solve problems that the ML model itself appears incapable of solving.

deanCommie 4y ago

> Prior to electronic computers, groups of humans using pencil, paper, reference-tables (that themselves had been hand-calculated by humans), and slide-rules worked this way to get accurate answers to arbitrarily difficult computations. There's a scene of this in Apollo 13. Line of humans with slide rulers manually confirming calculations made on the craft whose computer is out.

WorldMaker 4y ago

Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how human-like neurons seem to work (primarily in isolation studies in a petri dish or similar experiments), but it is very easy to forget that the model/analogy is not the reality when you work with those models a lot. The current "map" certainly doesn't seem to be the territory here. Neurons are just one part of the human brain, there's also complex chemical receptors and hormonal pathways that we mostly still only grasp in the abstract of how they interplay with neurons and other signal pathways. Additionally, there's indirect evidence that brains may take some advantage of quantum effects among/between neurons such as entangled particle states, but we don't know a lot more than we know about the quantum states of human brain activity. (How do we even probe that in a living human with our current knowledge of quantum physics?) Neural Networks/Perceptrons have been very useful models in computation, but that doesn't mean they are actually good or representative models for human cognition.

aidenn0 4y ago
> Related to this topic, we still don't have a good, strong idea of how human thought works. Neural Networks/Perceptrons were built out of analogies of how human-like neurons seem to work (primarily in isolation studies in a petri dish or similar experiments), but it is very easy to forget that the model/analogy is not the reality when you work with those models a lot. I'm pretty sure that you were making a tangent rather than a rebuttal, but I did want to clarify that I'm not saying that these large models replicate the structural thinking of humans. Instead I'm saying that, given a ML model that is "sufficiently human like" in the mistakes it makes, using techniques that prevent humans from making those mistakes (e.g. showing work, having multiple agents calculate in parallel) could demonstrate an ability to solve problems that the ML model itself appears incapable of solving.