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you really can't rule out being able to build the kind of systems that we're talking about in the near term.
the impact, if you actually succeed, uh, is going to be on the order, you know, at a minimum of the agricultural revolution, and on a maximum, you're talking about like going to the stars and, you know, solving solving uh, all disease
But I think I think I think what we're starting to see is that you take these models, you scale them up, right? Unlike with Deep Blue, where that the model that you're working with is fundamentally hard to get in the real world, right? That you need the simulator that's perfect that lets you look ahead. Here, you just need background data, right? You can take data of books, you can take data from YouTube, you can take data of just images. And now, we're able to start to learn things that seem li
We spend, we spend more than that. Yeah. So as a nonprofit, you know, our, our nine nineties are all, all public. So you can, you can go and look them up. Um, I think our 20, uh, 16 budget, you know, basically we've been on an exponential ramp, uh, of, you know, I think like, you know, 10 to $15 million, I think was like kind of year, year one.
We're structured as a non-profit. Why? And the reason for that is that, you know, our goal at the end of the day is not to generate huge profits for us, right? The goal here is to build a system that we think that the impact, if you actually succeed, is going to be on the order, you know, at a minimum of the agricultural revolution. And on a maximum, you're talking about, like, going to the stars and, you know, solving all disease and, you know, sort of all the things that humans have wanted to
the gaming industry really subsidized all of the research and development to get to the point that we had AI supercomputers.
they bet very heavily, and that's the reason that they're the market leader here.
Nvidia stock price, if you look, has gone up quite a bit over the past couple years.
But I think I think I think what we're starting to see is that you take these models, you scale them up, right? Unlike with Deep Blue, where that the model that you're working with is fundamentally hard to get in the real world, right? That you need the simulator that's perfect that lets you look ahead. Here, you just need background data, right? You can take data of books, you can take data from YouTube, you can take data of just images. And now, we're able to start to learn things that seem li
I think that this work is all very early. It's preliminary.
we actually have a model that we trained earlier this year and other people now have kind of follow-on work and similar work where we just had a model that read 7,000 books. We didn't tell it what to care about the books. We just said, 'Predict what is going to come next. Predict what word is going to be the next word in the sentence.' And so it just read read it, predict what's going to happen next. And then, you can take this model and you can apply it to a bunch of different natural language
we have a program called the fellows program, which is actually also a 6-month program where you spend 3 months doing self-study, and then the next 3 months you write a paper.
you have some Deepmind people at your company. Yeah, we we we we do.
I actually think that it's it's harder than you think to to to get people in this field to not publish. And so, it's actually very very hard to hold anything back. And so, for example, when we when we just said that one day we're going to decrease our publishing due to safety and security concerns in our charter, that really raised a bunch of eyebrows. People said, "You're going to do what?" Huh? And I think that that's that's a really interesting thing about this field. I think it's actually ve
we just said that one day we're going to decrease our publishing due to safety and security concerns in our charter, that really raised a bunch of eyebrows.
we actually also find that you can take people from totally different fields, put them in Open AI for a couple months, and then they're going to be contributing to the machine learning at at an exceptional level. A great engineer can learn how to contribute to the AI movement, machine learning movement in 3 to 6 months?
the big thing that we've increasingly found really delivers a lot of surprising value is having these large projects, right? Where you take people who have that PhD or that that machine learning background, but you pair them with people who have the engineering background.
So, the reason you're doing the cube, the reason you're doing Dota is because Dota is because trying to cure cancer with it, we're not ready yet. That Well, that's that's certainly true, right?
I think that what a GI is really about is something a level deeper, right? That we as humans are are, you know, I think we're used to building technologies where fundamentally the decision-making capability is still it's up to us that we can make better decisions than our systems can. Got it. And that I think that the real transition point and that that the challenge and the benefit really is being able to build systems that can make better decisions than we can, that can understand problems bet
Elon's actually no longer involved in in OpenAI. Right. Um so, you know, he stepped off the board earlier this year. Got it. Was that because of conflicts? Yeah, conflicts with Tesla. You know, he's still still a supporter and uh you know, fan of the project, but no longer involved.
they're pretty blown away. Right? They For for them, that just people being able to do this, that was always the dream. But, you make it for 20 years, and no one can figure out to program it.
this is trained, right? So, the way that this was trained is that it has about somewhere between I think it's about 100 years of simulated experience. So, in So, the way we trained it is entirely in simulation.
the company that makes this robotic hand has made them for about 20 years. They sell about 10 of them a year because no one can program them.
the way to think about it is that is that narrow and general are really this this spectrum that that exists and to some extent you know you can almost say that that you know general intelligence is a is an artificial point on that and so it's more of a how far along are you
one year later is when we went up on stage at the international and played against one of one of the top teams there um and we actually lost um and uh you know we we we estimate it's always a little bit hard to tell but we put up a good fight. We put up a really good fight.
we we we first selected the game in November of 20 16. So about about 2 years ago. We we really started working on the project. It was just me and and one other person at you know, at the end of the year. I remember it was December, it was New Year's Eve when I was like hacking away frantically and managed to to actually we had to reverse our engineer our way into the game initially. So I was able to make that work. We we got our first machine learning results in March of that year. So that's ki
we released a document earlier this year called the OpenAI Charter. And so, this is based on about 2 years worth of a policy research that that that describes how we as an organization will operate over time. And one of the points there, which I think raised a lot of eyebrows for people, is that today one of the ways that we contribute is by publishing most of our research, but in the future we expect to reduce that due to safety and security.
we're about 80 people full-time. We've been around for 3 years. We're we're structured as a non-profit.
we actually have a pretty wide base of funders, uh, ranging from uh, so, you know, Sam and Elon have have contributed, but Reid Hoffman, Gabe Newell, who's the CEO of Valve, uh, and and a large large number of other individuals. Um, and uh, also the Open Philanthropy Foundation.
I think our 20, uh, 16 budget, you know, basically we've been on an exponential ramp, uh, of, you know, I think like, you know, 10 to 15 million dollars, I think was like kind of year year one.
the impact, if you actually succeed, uh, is going to be on the order, you know, at a minimum of the agricultural revolution, and on a maximum, you're talking about like going to the stars and, you know, solving solving uh, all disease
you really can't rule out being able to build the kind of systems that we're talking about in the near term.
to ensure that artificial general intelligence, and by that we're talking about systems that are as smart as we are, that can outperform humans at most economically valuable work. Whenever we can build that, that is something that actually benefits all of humanity.