The Exchanges

Q example, you do have reasonably specialized systems or all neural networks via specialized systems for the visual cortex versus, you know, um, areas of higher thought, areas for empathy or other sort of aspects of everything from personality to processing. Do you think that the transformer architectures are the main thing that will just keep going and get us there? Or do you think we'll need other architectures over time?

A So I have two, I understand precisely what you're saying, and I have two answers to this question. The first is that in my opinion, the best way to think about the question of architecture is not in terms of a binary, is it enough, but how much Effort. How much, what will be the cost of using this particular architecture? Like at this point, I don't think anyone doubts that the transformer architecture can do amazing things, but maybe something else, maybe some modification could have some compute efficiency benefits. So it's better to think about it in terms of compute efficiency rather than in terms of, can it get there at all? I think at this point, the answer is obviously yes. To the question about, well, what about the human brain and with its brain regions? I actually think that the situation there is Subtle and deceptive for the following reasons. So what I believe you alluded to is the fact that the human brain has known regions. It has like, it has a speech perception region. It has a speech production region. It has an image region. It has a face region. It's like all these regions. And it looks like it's specialized. But you know what's interesting? Sometimes there are cases where very young children have severe cases of epilepsy. At a young age. And the only way they figured out how to treat such children is by removing half of their brain. Because it happened at su…

AI assessment note: “can it get there at all? I think at this point, the answer is obviously yes.”

Q you the intuition to think that that was the case? Because I think at the time it was reasonably Um, contrarian to think that despite to your point, you know, a lot of the, the human brain in some sense works that way or different, you know, biological neural circuits, but I'm just curious, like what gave you that intuition early on to think that this was a good direction?

A I think, yeah, looking at the brain and specifically the, if you like, all those things follow very easily. If you allow yourself, if you allow yourself to accept the idea right now, this idea is reasonably well accepted. Back then people still talked about it, but they haven't really accepted it or internalized the idea that maybe an artificial neuron in some sense is not that different from a biological neuron. So now whatever you imagine animals do with their brains, you could perhaps assemble some artificial neural network of similar size. Maybe if you train it, it will do something similar. So there, So that leads to that. So that leads you to start to imagine. Okay. Like you almost imagine the computation being done by the neural network. You can almost think like if you have a high resolution image and you have like, One neuron for like a large group of pixels. What can the neuron do? It's just not much it can do if you, but if you have a lot of neurons, then they can actually do something and compute something. So I think it was like art, like it was, this was, it was considerations like this plus a technical realization. The technical realization is that if you have a large training set that specifies the behavior of the neural network, And the training set is large enough such that it can constrain the large neural network sufficiently. And furthermore, if you have th…

AI assessment note: “considerations like this plus a technical realization”

Q space there. And at the time there was, uh, a, a suite of different efforts. There was robotic arms, uh, that were being manipulated. And then there was, um, you know, some video game related work, which was really cutting edge. Um, how did you think about how the research agenda evolved and what really drove it down this path of transformer based models and other forms of, of learning?

A So our thinking has been evolving over the years from when we started OpenAI. In the first year, we indeed did some of the more conventional machine learning work. By conventional machine learning work, I mean, because the world has changed so much, a lot of things which were Known to everyone in 2016 or 20 17 are completely and utterly forgotten. It's like the stone age almost. So in that, in that stone age, the world, the world of machine learning looked very different. It was dramatically more academic. The goals, values, and objectives were much more academic. They were about discovering small bits of knowledge and sharing them with the other researchers and getting scientific recognition as a result. And it's a very valid goal and it's very understandable. I've been doing AI for 20 years now. More than half of my time that I spent in AI was in that framework. And so what do you do? You write papers, you share your small discoveries. Two realizations. The first realization is Just at a high level, it doesn't seem like it's the way to go to for a dramatic impact. And why is that? Because if you imagine how an AGI should look like, it has to be some kind of a big engineering project that's using a lot of compute, right? Even if you don't know how to build it, what that should look like, you know that this is the ideal you want to strive towards. So you want to somehow move to…

AI assessment note: “our thinking has been evolving over the years from when we started OpenAI.”

Q What do you think is the likelihood of that goal? I mean, some of it, it feels like a outcome you can hopefully affect, right? But, uh, are we, are we likely to have pro-social AIs that we are friends with individually or, you know, as a species?

A Well, I mean, friends be is, I think that's, that part is not necessary. The, the, the friendship piece I think is optional, but I do think that we want to have very pro-social AI. I think it's, I think it's possible. I don't think it's guaranteed, but I think it's possible. I think it's going to be possible and the possibility of that will increase insofar as more and more people allow themselves to look into the future, into the five to 10 year future, and just ask yourself what What do you expect AI to be able to do then? How capable do you expect it to be then? And I think that with each passing year, if indeed AI continues to improve, and as people get to experience, because right now we are talking, making arguments, but if you actually get to experience, oh gosh, the AI from last year, which was really helpful. This year it puts the previous one to shame and you go, okay. And then one year later and one year it's starting to do science. The AI software engineer is starting to get really quite good. Let's say, I think that will create a lot more desire in people for What you just described for the future super intelligence to indeed be very pro-social. You know, I think there's going to be a lot of disagreement. It's going to be a lot of political questions, but I think that as people see AI actually getting better, as people experience it, the desire for the pro-social s…

AI assessment note: “I think it's possible. I don't think it's guaranteed, but I think it's possible.”

Q What do you think the role of open source is in this ecosystem?

A Well, open source is complicated. I'll describe to you my mental picture. I think that in the near term, open source is just helping companies produce useful Like, let's see. Why would one want to have an open source, to use an open source model instead of a closed source model that's hosted by some other company? I mean, I think it's very valid to want to be the final decider on The exact way in which you want your model to be used and for you to make the decision of exactly how you want the model to be used and which use case you wish to support. And I think there's going to be a lot of demand for open source models. And I think there will be quite a few companies that will use them. And I'd imagine that will be the case in the near term. I would say in the long run, I think the situation with open source models will become more complicated and I'm not sure What the right answer is there. Right now, it's a little bit difficult to imagine, so we need to put our future hat, maybe futurist hat. It's not too hard to get into sci-fi, into a sci-fi mode when you remember that we are talking to computers and they understand us. But so far, these computers, these models are actually not very competent. They can't do tasks at all. I do think that there will come a day where The level of capability of models will be very high. Like in the end of the day, intelligence is power, right? R…

AI assessment note: “I think that in the near term, open source is just helping companies”

Q And, uh, if you think about those bottom, I mean, either direction, top down or bottom up ideas, like clearly we have this dominant continue to scale transformers direction. Um, do you explore additional like architectural directions or is that just not relevant?

A It's certainly possible that various improvements can be found. I think, I think improvements can be found in all kinds of places, both small improvements and large improvements. I think the way to think about it is that while the current thing that's being done keeps getting better as you keep on increasing the amount of compute and data that you put into it. So we have that property. The bigger you make it, the better it gets. It is also the property that different things get better by different amounts as you keep on improving, as you keep on scaling them up. So not only you want to, of course, scale up what we are doing, we also want to scale, keep scaling up the best thing possible.

AI assessment note: “we also want to scale, keep scaling up the best thing possible.”