Sam Altman

using the model as a database instead of using the model as a reasoning engine.

The thing that's really amazing about this system is that it, for some definition of reasoning, and we could, of course, quibble about it, and there's plenty for which definitions this wouldn't be accurate, but for some definition, it can do some kind of reasoning.

And, you know, maybe like the scholars and the experts and like the armchair quarterbacks on Twitter would say, no, it can't, you're misusing the word, you're, you know, whatever, whatever.

But I think most people who have used the system would say, okay, it's doing something in this direction.

and and i think that's remarkable and the thing that's most exciting and somehow out of ingesting human knowledge it's coming up with this reasoning capability however we want to talk about that um

Now, in some senses, I think that will be additive to human wisdom.

And in some other senses, you can use GPT-4 for all kinds of things and say that it appears that there's no wisdom in here whatsoever.

Yeah, it's always tempting to anthropomorphize this stuff too much, but I also feel that way.

So two separate things going on here.

Number one, some of the things that seem like they should be obvious and easy, these models really struggle with.

So I haven't seen this particular example, but counting characters, counting words, that sort of stuff, that is hard for these models to do well the way they're architected.

That won't be very accurate.

Second, we are building in public and we are putting out technology because we think it is important for the world to get access to this early, to shape the way it's going to be developed, to help us find the good things and the bad things.

And every time we put out a new model, and we've just really felt this with GPT-4 this week, the collective intelligence and ability of the outside world helps us discover things we cannot imagine, we could have never done internally.

and both like great things that the model can do new capabilities and real weaknesses we have to fix and so this iterative process of putting things out finding the the the great parts the bad parts improving them quickly and giving people time to feel the technology and shape it with us and provide feedback we believe is really important the trade-off of that

is the trade-off of building in public, which is we put out things that are going to be deeply imperfect.

We want to make our mistakes while the stakes are low.

We want to get it better and better each rep.

But the bias of chat GPT when it launched with 3.5 was not something that I certainly felt proud of.

It's gotten much better with GPT-4.