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Simply explained: How does GPT work?

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Re: Simply explained: How does GPT work?

#241

Earlier quoted context omitted.

But Lacan? And no, there is a vast new world of cognitive neuroscience that was undreamed even 10 years ago.

> But Lacan? Well, if you're in need of an established theory of (semantically driven) talking machines and what derives from this, and what this may mean for us in terms of freedom, look no further.

Can you recommend a specific work of his? What Lacan I have leaves me bemused by his brilliance but not informed. Dennett provides both without the fireworks.

Re: Simply explained: How does GPT work?

#242

I’d be interested in hearing from anyone who takes the Chinese Room scenario seriously, or at least can see how it applies to any of this. I cannot see that it matters if a computer understands something. If it quacks like a duck and walks like a duck, and your only need is for it to quack and walk like a duck, then it doesn’t matter if it’s actually a duck or not for all intents and purposes. It only matters if you…

> if it quacks like a duck and walks like a duck, and your only need is for it to quack and walk like a duck, then it doesn’t matter if it’s actually a duck or not for all intents and purposes.

This isn't true. If a plane flies like a bird and you only need it for flying it doesn't then follow that a plane is a bird "for all intents and purposes".

Re: Simply explained: How does GPT work?

#243
post #200

I’d be interested in hearing from anyone who takes the Chinese Room scenario seriously, or at least can see how it applies to any of this. I cannot see that it matters if a computer understands something. If it quacks like a duck and walks like a duck, and your only need is for it to quack and walk like a duck, then it doesn’t matter if it’s actually a duck or not for all intents and purposes. It only matters if you…

Here's an example that I think garners more agreement that properties of a limit ("really understanding") don't necessarily mean that any path towards that limit has the properties of the limit. I think there's a lot of room for disagreement about whether this is a factually-accurate analogy and I'm not trying to argue either way on that, just trying to answer your question about how one might make these sorts of arg…

The only thing that separates your mechanism for doing addition from what computers actually do is efficiency. Computers can only add numbers up to some fixed size, e.g. 64 bits, and you have to use repetition to add anything larger.

Does that mean computers are not "really doing" addition?

Re: Simply explained: How does GPT work?

#244
post #166

Earlier quoted context omitted.

> What is the entropy per word of random yet grammatical text? More colourless green dreams sleep furiously in garden path sentences than I have > This does not help you one bit. Dunno, how many bits does ELIZA? I assume more than 1…

> What is the entropy per word of random yet grammatical text? That is what these 5-11bit estimates are about. Those would correspond to a choice out of 32 to 2048 options (per word), which is much less than there are words in english (active vocabulary for a native speaker should be somewhere around 10000-ish). Just consider the XKCD "thing explainer" which limits itself to a 1k word vocabulary and is very obviously…

> Eliza is not comparable to GPT because it does not even hold up to very superficial scrutiny; its not really capable of even pretending to intelligently exchange information with the user, it just relies on some psychological tricks to somewhat keep a "conversation" going...

That's kinda the point I was making — tricks can get you a long way.

The comparison with GPT is not "and therefore GPT is bad" but rather "it's not necessarily as smart as it feels".

Perhaps I should've gone for "clever Hans" or "why do horoscopes convince people"?

Re: Simply explained: How does GPT work?

#245
I'd be interested in hearing people's takes on the simplest mathematical reason that transformers are better than/different from fully connected layers. My take is:

    Q = W_Q X
    K = W_K X
    A = Q^T K = (X^T W_Q^T) (W_K X) = X^T (...) X
Where A is the matrix that contains the pre-softmax, unmasked attention weights. Therefore, transformers effectively give you autocorrelation across the column vectors (tokens) in the input matrix X. Of course, this doesn't really say why autocorrelation would be so much better than anything else.

Re: Simply explained: How does GPT work?

#246
post #223

I’d be interested in hearing from anyone who takes the Chinese Room scenario seriously, or at least can see how it applies to any of this. I cannot see that it matters if a computer understands something. If it quacks like a duck and walks like a duck, and your only need is for it to quack and walk like a duck, then it doesn’t matter if it’s actually a duck or not for all intents and purposes. It only matters if you…

> It seems to me to posit that to understand requires that the understandee is human. Here's a thought experiment. Suppose we make first contact tomorrow, and we meet some intelligent aliens. What are some questions you would ask them? How would you decide on their sentience or understanding? Sentience involves goal-seeking, understanding, sensory inputs, first-personal mental states (things like pain, happiness, sad…

Does that mean sentient beings need to have had the evolutionary history that people have had? After all why would aliens have feelings like we have feelings? Don't we have feelings because either it was useful to our ancestors, or they are a spandrel of something that was?

Re: Simply explained: How does GPT work?

#247

I'd be interested in hearing people's takes on the simplest mathematical reason that transformers are better than/different from fully connected layers. My take is: Q = W_Q X K = W_K X A = Q^T K = (X^T W_Q^T) (W_K X) = X^T (...) X Where A is the matrix that contains the pre-softmax, unmasked attention weights. Therefore, transformers effectively give you autocorrelation across the column vectors (tokens) in the input…

It’s a perception problem, as are most things on the edge of mathematics and computing. Displays are built to be visible to human eyes, data is structured to be perceivable to our minds… often we never see the “math” a program does to produce the GUI or output we interact with.

Do you see what I mean?

Re: Simply explained: How does GPT work?

#248

Earlier quoted context omitted.

> has burdened me with pseudo-philosophical questions about the nature of cognition that I am not well equipped to articulate, and make me wish I'd studied more neuroscience, philosophy, and comp sci earlier in life Welcome to the club. There pretty much are no answers, just theories primarily played out as thought experiments. Its on of those areas where you can pick out who knows less (or is being disingenuous) by…

It's interesting to me how many commenters on HN are absolutely convinced that GPT4 is incapable of thought or understanding or reasoning, it's "just" predicting the next word. And then they'll insist that it'll never be able to do things that it's already capable of doing... Interestingly, more than one of these folks have turned out to be religious. I wonder if increasingly intelligent AI systems will be challengin…

It's less unsettling when you think of LLMs as an approximation to a kind of "general intellect" recorded in language. But then the surprising thing is that we as "individual intellects" tend to operate the same way, perhaps more than we imagined.

Re: Simply explained: How does GPT work?

#250

> It is able to link ideas logically, defend them, adapt to the context, roleplay, and (especially the latest GPT-4) avoid contradicting itself. Isn't this just responding to the context provided? Like if I say "Write a Limerick about cats eating rats" isn't it just generating words that will come after that context, and correctly guessing that they'll rhyme in a certain way? It's really cool that it can generate coh…

It's all about emergent complexity. While you can reduce it to "just" statistical auto-completion of the next word, we are seeing evidence of abstraction and reasoning produced as a higher-order effect of these simple completions. It's a bit like the Sagan quote: "If you wish to make an apple pie from scratch, you must first invent the universe". Sometimes for GPT to "just" complete the next word in a way that humans…

Turns out good usage of "language" requires a model of the world in which that language exists. "The purple, two eyed, green, five eyed, invisible frog said moo" is a grammatically fine sentence. But logically it makes no sense, does it have two eyes or five? Is it green or purple or invisible? Frogs don't typically say moo. To have actual coherent usage of language, you need a model of the world. Not just the world, but the current domain you're using language in. "The frog brainwashed the crowd with its psychic powers" is nonsense in a biology paper, but perfectly valid inside of the cartoon Futurama.

In ChatGPT the language-model and world-model are really just the same model, which makes a lot of sense.

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