The lead may not always be obvious and the trade off from transparency may be worth it. LLMs (or rather "foundation models") will continue to capture more and more areas of competence. If I want one that - for example - forms the front end chat bot to a store I have so that people can ask for product explanations, do I need then the 500 IQ GPT-7 that won two Nobel prizes last year?

I think it's most likely that there will always be black box huge models that form the peak of what is possible with machine intelligence but what people use and interact with in practice will simply be "good enough" smaller and open source models.

That depends on how well they will be able to keep their moat. There is a lot of hunger for running LLMs on your own - if not hardware than at least in software environments you control. People want to see what makes them tick rather than trust "Open"AIs black boxes.

Yeah they have a performance lead but time will tell how well they can stay ahead of the rest of the field trying to catch up.

What do you mean by "frame"? How many images do you think GPT-4 would need to get a cursor where it needs to go? I'd estimate four or five should be plenty.

I am not sure. Exactly how does inference scale with the complexity of the input? The output would be very short, just enough tokens for the "move cursor to" command.

>GPT-4 is potentially missing a vital feature to take this one step further: Visual Grounding - the ability to say where inside an image a specific element is, e.g. if the model wants to click a button, what X,Y position on the screen does that translate to?

You could just ask it to move a cursor around until it's on the specified element. I'd be shocked if GPT-4 couldn't do that.

I think this is really exciting. LLM applications like ChatGPT seem to still mostly just pipe the result of the model sampling directly out but with 100 times faster inference, maybe complex chain of thought procedures with multiple differently prompted model instances (well, the same model but different contexts) can be chained and work together to improve their output while still running close to real time.

You could also count water molecules.

> I think it's likely the ability to determine what is true and what isn't will come from a capability of the model rather than it being told what is and isn't true. It's not possible to mark text as true or not true as this assumes whomever is mafking these things is the sole authority on the truth and never makes mistakes.

I think there is a bit of a misunderstanding here. The issue isn't that GPT3 has wrong opinions about stuff. The issue is that it doesn't have any opinions about what is real or isn't whatsoever. Of course any future AI will operate on limited and flawed information and thus have opinions that are not perfectly true. But before we can even get to that point a model needs to even have the idea of "real" and "not real" as fundamental categories. For GPT3 everything is just text, Harry Potter is as real as Obama. Maybe I am wrong and inference can actually get you there through pure consistency checks, as you say. But we will have to see about that.

Well, if you are at a billion the difference between continuous and discrete quantities becomes kind of hair splitting anyway....

I think it's going to be interesting if we manage to teach a model to actually have a notion of "factual" and "counterfactual" - right now every prompt is treated as equally valid, GPT3 doesn't have an "opinion" as to what is actually really true. I am not sure that is even possible with text (maybe with some sort of special marker token?) but multimodality might lead the way there.

OpenAI stores the chat logs. That does not mean ChatGPT has any way to search through them.

I think the point of the metaphor was Amazon stealing product ideas from third party vendors on their site and undercutting them. They know what sells better than anyone and can then just produce it.

If Google or OpenAI offers people the opportunity to finetune their foundation models they will know when something valuable comes out of it and simply replicate it then. There is close to zero institutional cost for them to do so.

That's a reason why I think all these startups that want to build business models around ChatGPT are insane: if you do it and it actually turns out to work OpenAI will just steal your lunch and you have no way of stopping that.