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by benlivengood17 hours ago |
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upvoteby noosphr17 hours ago|
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I'm kind of stunned that someone is using my work to tell me I'm wrong. I wrote the code for the dish brain pong and encoding information was a huge part of what that experiment was about.

So when I way that the grok paper and the pong paper fundamentally agree I have some idea of what I'm talking about.

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upvoteby anon8487362814 hours ago|
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If you're going to claim the tokenizer is a dictionary then it doesn't really matter what paper you wrote code for.
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upvoteby benlivengood16 hours ago|
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I might have misunderstood the point you are making. I read the original article as "weights are like meat", and so I'm confused by what you consider fractally wrong.
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upvoteby noosphr16 hours ago|
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The point that when the rules the model learns are simple enough they stop being spread out over all the layers and become as easily interpretable as any expert system.

It's just that the rules we feed in the model are extremely poorly defined and we end up with the soup of disjoint rules smeared all across the weights.

This isn't a feature of the models. It's a feature of the training set.

Being shocked that you can store rules in floating point numbers is the same as being shocked you can store rules in integers. It's been a century since Goedel Numbering was invented, we should be used to it by now.

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upvoteby simonh16 hours ago|
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Right, but all of that is still in the weights. The point of the article/joke isn’t literally that there is no grammar, it’s that there is no grammar separate from the weights. It’s all in the weights. And yes, it’s absurd. It’s a joke, but a thought provoking one.
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upvoteby throwaway17373814 hours ago|
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So basically there are rules, we just can’t articulate them and so we can’t decode them from the weights. The Goedel Numbering metaphor is pretty appealing to me. You can represent any finite series of real numbers with a series of computations performed on some other finite series of real numbers. We just happen to be using matrices because the math is easy to parallelize. The trick is to realize that when you know the sequence you have and the sequence you want then you can compute the calculations. If you constrain the calculations to only matrix multiplication then you arrive at the scheme we have.
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upvoteby teiferer13 hours ago|
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> You can represent any finite series of real numbers with a series of computations performed on some other finite series of real numbers.

That statement caught my eye. It's either trivially true or quite clearly wrong, depending on how you mean it.

In the literal meaning it's true. Given any finite set of real numbers, I can easily produce a different set (like taking the original set and adding a number which wasn't in there like one plus the largest or so) from which you can trivially produce the original set computationally.

But if you mean you give me both sets then that can't be true. For example if you give me a single real number as set A and the empty set as set B then I can't create a program which generates set A from set B. Your real number in set A could encode anything.

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upvoteby skydhash7 hours ago|
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> For example if you give me a single real number as set A and the empty set as set B then I can't create a program which generates set A from set B. Your real number in set A could encode anything.

And that’s why in computation theory, the set of symbols is the union of the input and output. As set B is a subset of set A, then the set that govern any program from B to A has set A as its domain.

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upvoteby ufocia16 hours ago|
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Hubris much? I don't see a necessary contradiction in using someone's work to disprove another aspect of that same person's work.
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upvoteby anon8487362814 hours ago|
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Comparing the tokenizer to sensory processing is a great analogy. That's exactly what your visual cortex and initial layers of the language center are doing: decoding visual representation of text into the internal neural representation.

It's a learned mapping from one representation to another, not some semantic lookup against an exogenous source.

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