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Can LLM's compute any computable function? I thought that an LLM can approximate any computable function, if the function is within the distribution that it is
by sgt101 3y ago
Can LLM's compute any computable function? I thought that an LLM can approximate any computable function, if the function is within the distribution that it is are trained on. I think it's jolly interesting to think about different axiomizations in this context.
Also we know that LLM's can't do a few things - arithmetic, inference & planning are in there. They look like they can because they retrieve discussions from the internet that contain the problems, but when they are tested out of distribution then all of a sudden they fail. However, some other nn's can do these things because they have the architecture and infrastructure and training that enables it.
There is a question for some of these as to whether we want to make NN's do these tasks or just provide calculators, like for grade students, but on the other hand something like Alphazero looks like it could find new ways of doing some problems in planning. The challenge is to find architectures that integrate the different capabilities we can implement in a useful and synergistic way. Lots of people have drawn diagrams about how this can be done, then presented them with lots of hand waving at big conferences. What I love is that John Laird has been building this sort of thing for like, forty years, and is roundly ignored by NN people for some reason.
Maybe because he keeps saying it's really hard and then producing lots of reasons to believe him?
- RamblingCTO 3y agoI still believe that A(G)I will consist of subsystems and different network architectures (if NN's are the path to that), just like we humans have.
- trashtester 3y agoMany of the "specialist" parts of the brain are still made from cortical columns, though. Also, they are in many cases partly interchangeable, with some reduction in efficiency. Transformers may be like that, in that they can do generalized learning from different types of input, with only minor modifications needed to optimize for different input (or output) modes.
- RamblingCTO 3y agoAfaik some are similar, yes. But we also have different types of neurons etc. Maybe we'll get there with a generalist approach, but imho the first step is a patchwork of specialists.
- robwwilliams 3y agoCortical columns are one part of much more complex systems of neural compute that at a minimum includes recursive connections with thalamus, hypothalamus, midbrain, brainstem nuclei, cerebellum, basal forebrain, — and the list goes on. So it really does look like a society of networks, all working in functional synchrony (parasynchrony might be a better word) with some firms of “consciousness” updated in time slabs of about 200-300 milliseconds. LLMs are probably equivalent now to Wernicke’s and Broca’s areas, but much more is needed “on top” and “on bottom”—-motivation, affect, short and longterm memory, plasticity of synaptic weighting and dynamics, and perhaps most important, a self-steering attentional supervisor or conductor. That attentional driver system is what we probably mean by consciousness.
- trashtester 3y ago> That attentional driver system is what we probably mean by consciousness. You may know much more about this than me, but how sure are you about this? To me it seems like a better fit that the "self-steering attentional supervisor" is associated with what we mentally model (and oversimplify) as "free will", while "consciousness" seems to be downstream from the attention itself, and has more to do with organizing and rationalizing experiences than with than with the directly controlling behavior. This processed information then seems to become ONE input to the executive function in following cycles, but with a lag of at least 1 second, and often much more. > one part of much more complex systems of neural compute As for your main objection, you're obviously right. But I wonder how much of the computation that is relevant for intelligence is actually in those other areas. It seems to me that recent developments indicate that Transformer type models are able to self-organize into several different type of microstructures, even within present day transformer based models [1]. [1]: https://www.youtube.com/watch?v=Gg-w_n9NJIE https://www.youtube.com/watch?v=Gg-w_n9NJIE (comment from Ilya somewhere)
- vidarh 3y ago> Can LLM's compute any computable function? In a single run, obviously not any, because it's context window is very limited. With a loop and access to an "API" (or willing conversation partner agreeing to act as one) to operate a Turing tape mechanism? It becomes a question of ability to coax it into complying. It trivially has the ability to carry out every step, and your main challenge becomes to get it to stick to it over and over. One step "up", you can trivially get GPT4 to symbolically solve fairly complex runs of instructions of languages it can never have seen before if you specify a grammar and then give it a program, with the only real limitation again being getting it to continue to adhere to the instructions for long enough before it starts wanting to take shortcuts. In other words: It can compute any computable function about as well as a reasonably easily distractable/bored human.
- wegfawefgawefg 3y agoML still cant do sin. Functions that repeat periodically.
- Jeff_Brown 3y agoTo err is human, after all.
- vidarh 3y agoWhat exactly is it you think it can't do? It can explain and apply a number of methods for calculating sin. For sin it knows the symmetry and periodicity, and so will treat requests for sin of larger values accordingly. To convince it to continue to write out the numbers for an arbitrary large number of values without emitting "... continue like this" or similar shortcut a human told to do annoyingly pointless repetitive work would also be prone to prefer is indeed tricky, but there's nothing to suggest it can't do it.