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I disagree with Geoff Hinton regarding "glorified autocomplete"

statmodeling.stat.columbia.edu

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Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#71

Post by [deleted] »

albertzeyer wrote: post #10
mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

> You would realize that all we are doing is using statistics to predict what the next item might be. So what does "understanding" really means then? "Understanding" is not really well defined. Either we (humans) do it, but then LLMs might just do it as well, depending on the definition of "understanding", or we both don't do it. But if no-one is really "understanding" anything, then this definition of "understanding…

[deleted]

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#72

Post by skepticATX »

continuational wrote: post #28
mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

I think the most useful definition of understanding something is that you can explain it and use it in context. ChatGPT routinely does both.

How about this: understanding is the ability to generalize knowledge and apply it to novel scenarios.

This definition is something that humans, and animals for that matter, do every day - both in small and large ways. And this is something that current language models aren't very good at.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#73

Post by continuational »

BobaFloutist wrote: post #47
continuational wrote: post #28

Earlier quoted context omitted.

I think the most useful definition of understanding something is that you can explain it and use it in context. ChatGPT routinely does both.

Someone sufficiently fast and skilled at googling can explain and use in context a lot of things that they don't really properly understand. So unless you're saying that the composite of the googler and of google understand something that neither does individually, your definition has some holes.

This is a variation of the Chinese room argument.

If you consider understanding an observable property, then the Chinese room in aggregate displays understanding of Chinese.

Would you say that humans understand nothing, because atoms don't understand anything, and we're made up of atoms?

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#76

Post by fritzo »

Andrew's distinction between associative vs logical thinking reminds me of two kinds of programming thinking. Half of my time while programming is spent churning out glue code, satisfying a type checker and test suite, and implementing the simple solution that turns out to work. The other half of my time is spent slowly thinking through why some simple solution is wrong, and step by step proceeding towards the correct but complex solution. The former phase is intuitive, the latter phase is scientific, where I hypothesize, test, and repeat.

Reading through the code-as-transcript afterwards it's unclear which bits of code required shallow associative vs deep rational thinking, pure autocomplete vs latent chain of thought.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#77

Post by [deleted] »

fritzo wrote: post #76

Andrew's distinction between associative vs logical thinking reminds me of two kinds of programming thinking. Half of my time while programming is spent churning out glue code, satisfying a type checker and test suite, and implementing the simple solution that turns out to work. The other half of my time is spent slowly thinking through why some simple solution is wrong, and step by step proceeding towards the correc…

[deleted]

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#78

Post by usrbinbash »

mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

> You would realize that all we are doing is using statistics to predict what the next item might be.

Two questions:

1. How can humans come up with novel ideas then? Statistically predicting the next item means I am limited to the training set. How do humans formulate new concepts in this scenario?

2. Why can humans learn from their own output then? If all we do is statistically predicting the next token, then, by necessity, feeding our own output back into the model (aka. letting humans learn from it) should degrade human capabilities by overfitting to the training set. But we observe exactly the opposite.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#79

Post by lordnacho »

corethree wrote: post #66
lordnacho wrote: post #59

Earlier quoted context omitted.

OTOH, doing something that only humans have done thus far would be a huge step in demonstrating understanding. Does that mean when a computer outputs a new proof it understands?

You can do something similar to this without giving it a problem that might be impossible. Train the LLM on a bunch of things but avoid certain things...Things that humans already know about. The you query the model about that thing. See if the model can come to the same conclusions humans do. You can actually do this right now with chatGPT.

So what happens?

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#80

Post by continuational »

skepticATX wrote: post #72
continuational wrote: post #28

Earlier quoted context omitted.

I think the most useful definition of understanding something is that you can explain it and use it in context. ChatGPT routinely does both.

How about this: understanding is the ability to generalize knowledge and apply it to novel scenarios. This definition is something that humans, and animals for that matter, do every day - both in small and large ways. And this is something that current language models aren't very good at.

What is the test for this?

I taught it Firefly, which is an undocumented programming language I'm working on, through conversion.

I find it's a lot quicker than any human at picking up syntax and semantics, both in real time and in number of messages, and makes pretty good attempts at writing code in it, as much as you could expect from a human programmer.

That is, until you run out of context - is this what you mean?

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