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Are not LLMs supposed to just find the most probable word that follows next like many people here have touted? How this can be explained under that pretense? Is this way of problem solving 'thinking'?
But that does not mean that the results cannot be dramatic. Just like stacking pixels can result in a beautiful image.
Are not LLMs supposed to just find the most probable word that follows next like many people here have touted? How this can be explained under that pretense? Is this way of problem solving 'thinking'?
That description is really only fair for base models†. Something like Opus 4.6 has all kinds of other training on top of that which teach it behaviors beyond "predict most probable token," like problem-solving and being a good chatbot. (†And even then is kind of overly-dismissive and underspecified. The "most probable word" is defined over some training data set. So imagine if you train on e.g. mathematicians solving…
It just changes the probability distribution that it is approximating.
To the extent that thinking is making a series of deductions from prior facts, it seems to me that thinking can be reduced to "pick the next most probable token from the correct probability distribution"...
Earlier quoted context omitted.
A bit related: open weights models are basically time capsules. These models have a knowledge cut off point and essentially forever live in that time.
This is the most fundamental argument that they are not, directly, an intelligence. They are not ever storing new information on a meaningful timescale. However, if you viewed them on some really large macro time scale where now LLMs are injecting information into the universe and the re-ingesting that maybe in some very philosophical way they are a /very/ slow oscillating intelligence right now. And as we narrow tha…
Sure, it's not how we work, but I can imagine a system where the LLM does a lot of heavy lifting and allows more expensive, smaller networks that train during inference and RAG systems to learn how to do new things and keep persistent state and plan.
I asked Claude to solve the pentominoes puzzle made famous by Arthur C. Clarke. It struggled mightily until I told it how I'd solved the problem using 64 bit unsigned integers to represent the board and pieces. Then, it created a C# program that solved the problem very quickly. However, in the 20x3 case it found four solutions when there are only two. Turns out it had incorrectly mapped one of the pentominoes. Sort o…
Time to sit down, read, digest and understand it without the help of LLM.
Earlier quoted context omitted.
If AGI will ever come, then. Currently, AI is only a statistical machines, and solutions like this are purely based on distribution and no logic/actual intelligence.
I don't even think that's the issue. The issue to my mind is a lack of data at the meeting of QFT/GR. Afterall few humans historically have been capable of the initial true leap between ontologies. But humans are pretty smart so we can't say that is a requirement for AGI.
Earlier quoted context omitted.
That description is really only fair for base models†. Something like Opus 4.6 has all kinds of other training on top of that which teach it behaviors beyond "predict most probable token," like problem-solving and being a good chatbot. (†And even then is kind of overly-dismissive and underspecified. The "most probable word" is defined over some training data set. So imagine if you train on e.g. mathematicians solving…
I'd disagree, the other training on top doesn't alter the fundamental nature of the model that it's predicting the probabilities of the next token (and then there's a sampling step which can roughly be described as picking the most probable one). It just changes the probability distribution that it is approximating. To the extent that thinking is making a series of deductions from prior facts, it seems to me that thi…
Earlier quoted context omitted.
A bit related: open weights models are basically time capsules. These models have a knowledge cut off point and essentially forever live in that time.
This is the most fundamental argument that they are not, directly, an intelligence. They are not ever storing new information on a meaningful timescale. However, if you viewed them on some really large macro time scale where now LLMs are injecting information into the universe and the re-ingesting that maybe in some very philosophical way they are a /very/ slow oscillating intelligence right now. And as we narrow tha…
I wonder how long we have until we start solving some truly hard problems with AI. How long until we throw AI at "connect general relativity and quantum physics", give the AI 6 months and a few data centers, and have it pop out a solution?
I think a very long time because part of our limit is experiment. We need enough experimental results to explain to solve these theoretical mismatches and we don't and at present can't explore that frontier. Once we have more results at that frontier we'd build a theory out from there that has two nearly independent limits for QFT and GR. What we'd be asking if the AI is something that we can't expect a human to solv…