So, what does a sentence produced by GPT3 signify, and what does it depict? What it signifies (ie causal history) is that this sequence of words is what human writers would most likely use when producing an utterance containing whatever trigger words the model has been fed. In other words, it's a statistical modlel. What it depicts is whatever the mapping rules for that language tell us it depicts. Since human beings usually tell the truth, a statistical model will usually produce true statements. It will also seem to have the ability to tell true from false, in many cases. But because GPT3 has no model for the significance of its sentences, it cannot be said to have any understanding of language, in the sense humans have it. LeCun's point about flying an aeroplane to the moon is essentially correct.
Yann LeCun on GPT-3
221–230 of 253 posts
Re: Yann LeCun on GPT-3
#222It could be a factor of 2 away from GPT-3! It could be something they already reached, if it is close! But we don't know. And without these answers, this is going to end up being one of the most interesting technical projects in the world.
Re: Yann LeCun on GPT-3
#223Earlier quoted context omitted.
> For all the talk about how language models "just" learn correlations, there's a remarkable dearth of evidence that humans do something qualitatively different. GPT3 doesn't know the difference between a given set of characters and the idea/object the characters represent. It can associate "river" and "stream" and "water" but has no understanding beyond that they appear in patterns together. It couldn't possibly mak…
> It couldn't possibly make the connection that river and streams are bodies of water, because there is no association with reality. Yet if you asked it if rivers and streams are bodies of water it would probably say that they are. Likewise if I asked you if black holes and neutron stars are both celestial bodies you would say yes... but you've presumably never seen them, only read about them. Now I think you could a…
Re: Yann LeCun on GPT-3
#224Earlier quoted context omitted.
If GPT-3 has a consistent position on anything, it's only because the corpus it was trained on was consistent about it. So, for example, it will reliably autocomplete Jabberwocky because there are a lot of copies of this poem in the corpus and they are all the same. If there were two versions of this poem that started the same way, it would pick between the variations in the corpus randomly. In other cases it might c…
I agree with you in that I would make a strong distinction between what a model like GPT-3 does and whatever it is that humans do. But I do think you're missing the point just a bit. When we speak and think, we use all kinds of metaphors that express judgements about the world, usually without realizing it. In other words, the way we use language encodes concepts in a deep way. To borrow an example from George Lakoff…
But a sophisticated understanding of metaphors could be used to tell the truth or to lie. In the case of GPT-3, it doesn't know the difference. Telling the truth and lying come out of the same autocompletion process.
If you consider the use of a metaphor to be showing judgement, it means that a particular metaphor seems to be appropriate to use in a particular context.
Re: Yann LeCun on GPT-3
#225Earlier quoted context omitted.
They were forced to give Microsoft exclusive access, because it was one of the terms of Microsoft's billion-dollar cloud credit investment. But you can't pay employees with cloud credits, so time will tell whether it was a correct decision. (It probably was. And I exaggerate slightly; the investment included a substantial sum of real dollars too. But most people see that billion dollar investment and think it's all d…
Is that only for GPT-3? Or for everything they produce?
Re: Yann LeCun on GPT-3
#226Re: Yann LeCun on GPT-3
#227I'm sure his group has done some rigorous research that I can't even understand. But in my experience, the few-shot learner attribute of GPT-3 makes it insanely useful. We have already found several use cases for it, one of which replaces 2 ML engineers. Yes, it's not perfect, but it's pretty good at many things, and REALLY easy to use.
Re: Yann LeCun on GPT-3
#228Reading this is really interesting: > GPT-3 doesn't have any knowledge of how the world actually works. I think this is a philosophical question. There is a view that, basically, there is no such thing as knowledge, just language (or, at least, there is no distinction between knowledge and language). In this view, all there really is is language, which is mostly composed of metaphors and, ultimately, metaphors only r…
At the risk of reigniting the perpetual war about how to characterize machine intelligence, and by extension how to characterize the risk they pose, Yann has been (and still is AFAIK) more in the "existential AI risk is a long-term problem" group. In a 2016 interview LeCun said [1]: > We’re very far from having machines that can learn the most basic things about the world in the way humans and animals can do. Like, y…
Except people are bad at exponential processes. Yet when economics drives us we are suddenly good at making them happen. And this combo seems to be what makes these existential risks. (Like climate change, or other manifestations of the coordination problem.)
Re: Yann LeCun on GPT-3
#229Earlier quoted context omitted.
> it's a language model designed to continue a sequence of words. If a language model were able to do this task perfectly, it would be indistinguishable from intelligence, because continuing a sequence of words requires reasoning. You cannot conclude that has no understanding based solely on what it is trained to do when the task it is trained on would be sufficient to demonstrate understanding were it to fully succe…
> you cannot conclude that [a model] has no understanding based solely on what it is trained to do agreed. but that's not everything we're basing our conclusions on – we also know that GPT-3 was trained purely on text, and i (and presumably GP) don't think that's a path towards "understanding". in other words, i think being a language model [trained only using a text corpus] is a valid reason to be skeptical of its p…
1. It's all just data in the end. The signals from your eye are not any more real because they are in response to photons.
2. Deafblind people exist, and can even learn to speak vocally. They might have misconceptions about the visual world, but it's certainly not true that they lack general intelligence.
3. As an objection to GPT-3 as a pathway to AGI, the only thing stopping us training these models on high-bandwidth highly-multimodal data is scale, anyway, so the objection doesn't work.
4. GPT-3 is clearly capable of reasoning in ways that can only be explained with more sophisticated world models than at smaller scales. People asserted reasoning was at a limit with GPT-2, and it just clearly isn't.