[sebzim4500]

>Basically my rant is that current progress was made over a long time and people just didn't really realize how far they have come until they opened it up to the public.

I'm sure that's true for lots of people but I doubt it's true for Hinton. He seems genuinely surprised by the success of large language models.

[galaxytachyon]

Perhaps. But previous to ChatGPT, I doubt anyone fully know how capable an LLM can be. There is a plethora of papers published after ChatGPT went public detailing all the cool and unexpected abilities from an LLM, things that surprised even its creators.

Before, it was easy to see how good these things were at generating texts and paying attention to text based tasks. But what made them a topic in AGI discussions is how they generalize beyond text and reaching out to different domains of expression despite never getting trained on those. Things like chess or simulating emotions and manipulations spontaneously happened and it wasn't until now that we have documented such events. Nobody saw or expected that.

[whimsicalism]

> Nobody saw or expected that.

I don't think this is true - at least not for people in the know in the last 3-4 years or so.

[galaxytachyon]

That would be interesting. If they knew about it shouldn't they published something? Everyone would see the importance being the first with a paper about an AI that can do things it wasn't trained to do?

Was it just corporate secrets or they don't want bad press? The Lemoine incident at Google and how Bing Chat turned into an obsessive lover made me think even those who worked with these AIs didn't really consider the full capabilities of their systems.

[whimsicalism]

I suppose it depends on what you mean by "an AI that can do things it wasn't trained to do"!

In some sense, the AI is still 'just doing what it was trained to do' in that it is 'just' predicting the next word. All examples of AI doing impressive behavior boil down to AI doing what it was trained to do (pick the next word [delta some RLHF tuning]), very well.

If you mean complex behavior arising out of what seems like a very simple unsupervised learning task, then this behavior has been known (although not to this scale) for a while.

For example, I distinctly remember being in my 2018 grad class on deep NLP and having a guest lecturer (Alec Radford) from OpenAI and they were demonstrating how their model got SOTA on summarization tasks just by taking the original text and appending the word "tl;dr" and using what the model produced after that token as the summary. It wasn't trained on a supervised summarization task, it just learned it incidentally from its unsupervised task.

The stuff we are observing is in the same vein as this, just even more impressive. But it is not unknown/completely unexpected behavior prior to ChatGPT.

Certainly it was already well known prior to this paper [0], which puts a lower limit on the timeline as at least 3 years ago.

> Was it just corporate secrets or they don't want bad press?

No, I think it was known & published about, although not as impressive as the most recent iterations. The press didn't realize this was a topic that interested people until 2022 (and really 2023).

[0]: https://arxiv.org/abs/2005.14165

[galaxytachyon]

Thank you. That was informative.