YouSaid · the spoken record

Greg Brockman

lines on the record: 101
first: 2019-04-03
most recent: 2019-04-03
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Know, it becomes pretty hard to know that the answer is no. And it becomes pretty hard to really think about what that would mean if the answer were yes. And it's very possible, for example, you could imagine that maybe the reason that humans have consciousness is because it's a convenient computational shortcut, right? If you think about it, if you have a being that wants to avoid pain, which seems pretty important to survive in this environment and wants to eat food, then that maybe the best way of doing it is to have a being that's conscious, right? That, you know, in order to succeed in the environment, you need to have those properties. And how are you supposed to implement them? And maybe this consciousness way of doing that. If that's true, then actually maybe we should expect that really competent reinforcement learning agents will also have consciousness. But, you know, it's a big if. And I think there are a lot of other arguments that you can make in other directions.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think that in terms of do we need consciousness, do we need a body? It seems the answer is probably not, right? That we could probably just continue to push kind of the systems we have. They already feel general. They're not as competent or as general or able to learn as quickly as an AGI would, but they're at least kind of proto-AGI in some way. And they don't need any of those things. Now let's move to the grand answer, which is if our neural nets conscious already, would we ever know? How can we tell? Here's where the speculation starts to become... Would happen if you pointed one of those at Adota neural net? And if you're training in this massive simulation, do the neural nets feel pain?”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I'll stick to the kind of non-grand answer first, right? So the non-grand answer is just to look at what are we already making work? You'll get GPT-2. A lot of people would have said that to even get these kinds of results, you need real-world experience. You need a body, you need grounding. How are you supposed to reason about any of these things? How are you supposed to even kind of know about smoke and fire and those things if you've never experienced them? And GPT2 shows that you can actually go way further than that kind of reasoning would predict.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I feel like there's two separate questions, right? So, you know, kind of at the core there of like can we use simulation for self-driving cars, take a look at our robotic system, Dactyl, right? That was trained in simulation using the DODA system, in fact. And it transfers to a physical robot. And I think everyone looks at our DODA system. They're like, okay, it's just a game. How are you ever going to escape to the real world? And the answer is, well, we did it with a physical robot that no one can program. And so I think the answer is simulation goes a lot further than you think. If you apply the right techniques to it. Now there's a question of, you know, are the beings in that simulation going to wake up and have consciousness? I think that one seems a lot harder to, again, reason about. I think that, you know, you really should think about where exactly does human consciousness come from and our own self-awareness. And is it just that like once you have a complicated enough neural net, you have to worry about the agents feeling pain. And I think there's like interesting speculation to do there.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's hard to have a real opinion about it. It's actually interesting. I separate out things that I think can have yield materially different predictions about the world from ones that are just kind of fun to speculate about. And I kind of view simulation as more like, is there a flying teapot between Mars and Jupiter? Maybe, but it's a little bit hard to know what that would mean for my life”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think reasoning is an important one. I think it's going to be hard to get good results in 2019. You know, again, just like we think about the life cycle takes time. I think for 2019, language modeling seems to be kind of on that ramp, right? It's at the point that we have a technique that works. We want to scale 100x, 1000x, see what happens.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think so, right? And I think that there's kind of other problems that are dual to theorem proving in particular. You think about programming, you think about even security analysis of code, that these all kind of capture the same sorts of core reasoning and being able to do some out of distribution generalization.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Two years or so to do it. The organization has been around for three years, so maybe we'll find that we also have longer lifecycle projects. But we'll work up to those. So one team that we were actually just starting, Illy and I are kicking off a new team called the Reasoning Team, and that this is to really try to tackle how do you get neural networks to reason. And we think that this will be a long-term project and it's one that we're very excited about.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So we really have a life cycle of project here. So we start with a few people just working on a small scale idea. And language is actually a very good example of this. That it was really one person here who was pushing on language for a long time. Then you get signs of life, right? And so this is like, let's say, with the original GPT, we had something that was interesting. And we said, okay, it's time to scale this, right? It's time to put more people on it, put more computational resources behind it. And then we just kind of keep pushing and keep pushing. And the end state is something that looks like Dota or robotics, where you have a large team of, you know, 10 or 15 people that are running things at very large scale and that you're able to really have material engineering and sort of machine learning science coming together to make systems that work and get material results that just would have been impossible otherwise. So we do that whole life cycle. We've done it a number of times, typically end to end. It's probably”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Our goal is to push the state of the art and reinforcement learning, and we've done that, right? And we've actually learned a lot from our system and that we have, you know, I think.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And, you know, it's interesting because the international was at a fixed time, right? So we knew exactly what day we were going to be playing, and we pushed as far as we could, as fast as we could. Two weeks later, we had a bot that had an 80% win rate versus the one that played at TI. So the march of progress, you know, you should think of as a snapshot rather than as an end state. And so in fact, we'll be announcing our finals pretty soon. I actually think that we'll announce our final match prior to this podcast being released. So there should be, we'll be playing against the world champions. And for us, it's really less about the way that we think about what's upcoming is the final milestone, the final competitive milestone for the project. That our goal in all of this isn't really about beating humans at Dota.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. So, well, one thing that's interesting is that we lose all the time Because we play here. So the Doda team at OpenAI, we play the bot against better players than our system all the time, or at least we used to, right? The first time we lost publicly was we went up on stage at the International and we played against some of the best teams in the world. And we ended up losing both games. But we gave them a run for their money, right? Both games were kind of 30 minutes, 25 minutes, and they went back and forth, back and forth, back and forth. And so I think that really shows that we're at the professional level and that kind of looking at those games, we think that coin could have gone a different direction and it could have had some wins. So that was actually very encouraging for us.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's something I think was very surprising to us, was something that doesn't really emerge from what we've seen with PPO at smaller scale, right? And the kind of scale we're running this stuff at was, you know, I could take 100,000 CPU cores running with like 100 GPUs. It was probably about something like hundreds of years of experience going into this bot every single real day. And so that scale is massive and we start to see very different kinds of behaviors out of the algorithms that we all know and love.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“That gets baked into this agent. And it's not really smart in the sense of a human, right? It's not able to go and learn calculus, but it's able to navigate its environment extremely well. It's able to handle unexpected things in the environment that it's never seen before pretty well. And we see the same sort of thing with our Dotabots, right? They're able to, within this game, they're able to play against humans, which is something that never existed in its evolutionary environment, totally different playstyles from humans versus the bots, and yet it's able to handle it extremely well.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yep, and so they approach that we used is self play. And so you have two agents that don't know anything, they battle each other, they discover something a little bit good, and now they both know it. And they just get better and better and better without bound. And that's a really powerful idea, right? That we then went from the one versus one version of the game and scaled up to five versus five, right? So you think about kind of like with basketball where you have this like team sport and you have to do all this coordination. And we were able to push the same idea, the same self-play to really get to the professional level at the full five versus five version of the game. And the things I think are really interesting here is that these agents in some ways, they're almost like an insect-like intelligence, right? They have a lot in common with how an insect is trained, right? Insect kind of lives in this environment for a very long time, or the ancestors of this insect have been around for a long time and had a lot of experience.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, that you look at the skill curve, and it was really very, very smooth one. So it's actually really interesting to see how that humid iteration loop yielded very steady exponential progress.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“What does it do? Don't it's a complex video game. And we started training, we started trying to solve Dota because we felt like this was a step towards the real world relative to other games like Chess or Go, right? Those very cerebral games where you just kind of have this board, very discreet moves. Dota starts to be much more continuous time that you have this huge variety of different actions that you have a 45 minute game with all these different units and it's got a lot of messiness to it that really hasn't been captured by previous games. And famously all of the hard-coded bots for Dota were terrible. It's just impossible to write anything good for it because it's so complex. And so this seems like a really good place to push what's the state of the art in reinforcement learning. And so we started by focusing on the one versus one version of the game and we're able to solve that. We were able to beat the world champions and the learning, the skill curve was this crazy exponential, right? And it was like constantly we were just scaling up that we were fixing bugs.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, so to that point with Doda and PPO, I mean, here's a very concrete one, right? It's actually one thing that's very surprising about Dota that I think people don't really pay that much attention to is the degree of generalization out of distribution that happens, right? That you have this AI that's trained against other bots for its entirety, the entirety of its existence.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Behaviors that emerge that are qualitatively different from anything we saw at small scale and that the original inventor of whatever algorithm looks at and says, I didn't think it could do that. This is what we saw in Dota, right? So PPO was created by John Shulman, who's a researcher here. And with Doda, we basically just ran PPO at massive, massive scale. And there's some tweaks in order to make it work. But fundamentally, it's PPO at the core. We were able to get this long-term planning, these behaviors to really play out on a timescale that we just thought was not possible. And John looked at that and was like, I didn't think it could do that. That's what happens when you're at three orders of magnitude more scale than you test it at.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Seeds. So take a look at, you know, I always like to look at examples that exist, right? Look at real precedent. And so take a look at the June 2018 model that we released that we scaled up to turn into GPT-2. And you can see that at small scale, it set some records. But there is an asterisk here, a very big asterisk, which is sometimes we see”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Right. And so that's a real trade-off there. And I think that's a very personal choice. But I think there's value in both sides.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And for him, he said, I'm done with that. I want to be the person who's actually doing building and deploying. And I think that there's a similar dichotomy here, right? I think that there are people who really actually find value. And I think it is a valuable thing to do, to be the person who produces those ideas, right? Who builds the proof of concept. And yeah, you don't get to generate the coolest possible gan images, but you invented the GAN.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, to some extent, this question reminds me of a blog post from one of my former professors at Harvard, this guy Matt Welsh, who was a systems professor. I remember sitting in his tenure talk, right? And, you know, he had literally just gotten tenure. He went to Google for the summer and then decided he wasn't going back to academia, right? And kind of in his blog post, he makes this point that, look, as a systems researcher, that I come up with these cool system ideas, right? And I kind of little proof of concept. And the best thing I can hope for is that the people at Google or Yahoo, which was around at the time, will implement it and actually make it work at scale. That's like the dream for me, right? I build the little thing and they turn the big thing that's actually working.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“These are ones that I think you could come up without having. And in practice, people did come up with them without having massive, massive computational resources.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, so the way that I think about it is that there's this space of possible progress, right? There's a space of ideas and sort of systems that will work that will move us forward. And there's a portion of that space. And to some extent, an increasingly significant portion of that space that does just require massive compute resources. And for that, I think that the answer is kind of clear and that part of why we have the structure that we do is because we think it's really important to be pushing the scale and to be building these large clusters and systems. But there's another portion of the space that isn't about the large-scale compute, that are these ideas that, and again, I think that for the ideas to really be impactful and really shine that they should be ideas that if you scale them up, would work way better than they do at small scale, but that you can discover them without massive computational resources. And if you look at the history of recent developments, you think about things like the GAN or the VAE.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“AGI is because we look at the system that exists in the most successful AI systems and we realize that you scale those up, they're going to work better. And I think that that scalability is something that really gives us hope for being able to build transformative systems.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I think that one thing that I think was really interesting about the reaction to that blog post was that a lot of people have read this as saying that compute is all that matters. And that's a very threatening idea, right? And I don't think it's a true idea either, right? It's very clear that we have algorithmic ideas that have been very important for making progress and to really build AGI, you want to push as far as you can on the computational scale, and you want to push as far as you can on human ingenuity. And so I think you need both. But I think the way that you phrase the question is actually very good, right? That it's really about what kind of ideas should we be striving for? And absolutely, if you can find a scalable idea, you pour more compute into it, you pour more data into it, it gets better. Like that's the real holy grail. And so I think that the answer to the question, I think, is yes, that's really how we think about it. And that part of why we're excited about the power of deep learning, the potential for building.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think there's another piece which is interesting, which is this out of distribution generalization, right? Like thinking somehow lets us do that, that we haven't experienced a thing and yet somehow we just kind of keep refining our mental model of it. This is, again, something that feels tied to whatever reasoning is. And maybe it's a small tweak to what we do. Maybe it's many ideas and we'll take as many decades.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's unlikely that if we just scale GPT-2, that will have reasoning in the full-fledged way. And I think that there's the type signature is a little bit wrong, right? There's something we do that we call thinking, where we spend a lot of compute, like a variable amount of compute, to get to better answers. I think a little bit harder, I get a better answer, and that kind of type signature isn't quite encoded in a GBT. GPT will kind of like it's been a long time and it's like evolutionary history baking in all this information, getting very, very good at this predictive process. And then at runtime, I just kind of do one forward pass and am able to generate stuff.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“But I think that in terms of how far language modeling will go, it's already gone way further than many people would have expected. I think that things like, and I think there's a lot of really interesting angles to poke in terms of how much does GPT-2 understand physical world? Like, you know, you read a little bit about fire underwater in GPT2. So it's like, okay, maybe it doesn't quite understand what these things are. But at the same time, I think that you also see various things like smoke coming from flame and a bunch of these things that GPT2 has no body. It has no physical experience. It's just statically read data. And I think that the answer is like, we don't know yet. These questions, though, we're starting to be able to actually ask them to physical systems, to real systems that exist. And that's very exciting.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think the Turing test in its real form isn't just about language, right? It's really about reasoning to really pass the Turing test, I should be able to teach calculus to whoever's on the other side and have it really understand calculus and be able to go and solve new calculus problems. And so I think that to really solve the Turing test, we need more than what we're seeing with language models. We need some way of plugging in reasoning. How different will that be from what we already do? That's an open question, right? It might be that we need some sequence of totally radical new ideas, or it might be that we just need to kind of shape our existing systems in a slightly different way.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Of your emotional buttons get triggered in the same way as if there was a real human that was on the other side of that phone. And so I think that this is one way of thinking about it is that I think that we can have meaningful interactions and that if there's a funny joke, sometimes it doesn't really matter if it was written by a human or an AI. But what you don't want, and I think we should really draw hard lines, is deception. And I think that as long as we're in a world where, you know, why do we build AI systems at all, right? The reason we want to build them is to enhance human lives, to make humans be able to do more things, to have humans feel more fulfilled. And if we can build AI systems that do that, assign me up.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think this is actually a really interesting question. This comes back to the how do you even picture a world with some new technology? And I think that one thing that I think is important is, let's say, honesty. And I think that if you have almost in the Turing test style sense of technology, you have AIs that are pretending to be humans and deceiving you. I think that feels like a bad thing, right? I think that it's really important that we feel like we're in control of our environment, right? That we understand who we're interacting with. And if it's an AI or a human, that's not something that we're being deceived about. But I think that the flip side of can I have as meaningful of an interaction with an AI as I can with a human? Well, I actually think here you can turn to sci-fi. And her, I think, is a great example of asking this very question, right? One thing I really love about her is it really starts out almost by asking how meaningful are human virtual relationships, right? And then you have a human who has a relationship with an AI and that you really start to be drawn into that, right?”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, here's another question, why is solving this problem important? What aspects are really important to us? And I think that probably where we'll end up is we'll hone in on what do we really want out of knowing if we're talking to a human. And I think that, again, this comes down to identity. And so I think that the Internet of the Future, I expect to be one that will have lots of agents out there that will interact with you. But I think that the question of is this real flesh and blood human or is this an automated system may actually just be less important.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Persuasive arguments are written by AI. All that stuff, it's not sci-fi anymore. You look at GPT2 making a great argument for why recycling is bad for the world. You got to read that and be like, huh, you're right. We are addressing those symptoms.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“But it's not that all hope is lost, right? And you think about how do we already authenticate ourselves? We have systems. Now there are problems with that. How can you have privacy and anonymity in a world where the only content you can really trust or the only way you can trust content is by looking at where it comes from? And so I think that building out good reputation networks may be one possible solution. But yeah, I think that this question is not an obvious one. And I think that we, you know, maybe sooner than we think, we'll be in a world where today I often will read a tweet and be like, do I feel like a real human wrote this? Or do I feel like this is genuine? I feel like I can kind of judge the content a little bit. And I think in the future it just won't be the case. You will get, for example, the FCC comments on net neutrality. It came out later that millions of those were auto-generated and that the researchers were able to do various statistical techniques to do that. What do you do in a world where those statistical techniques don't exist? It's just impossible to tell the difference between humans and AIs. And in fact, the most”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a losing battle ultimately, right? I think that that is that in terms of the content, in terms of the actions that you can take, I mean, think about how captures have gone, right? The captures used to be a very nice, simple, used to have this image. All of our OCR is terrible. You put a couple of artifacts in it. Humans are going to be able to tell what it is. An AI system wouldn't be able to today. Like, I could barely do captures. I think this is just kind of where we're going. I think CAPCHA is where a moment in time thing. And as AI systems become more powerful, that there being human capabilities that can be measured in a very easy automated way, that AIs will not be capable of. I think that's just like, it's just an increasingly hard technical battle.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, have you ever seen there's popular meme of robot with a physical arm and pen clicking the I'm not a robot button? I think that the truth is that really trying to distinguish between robot and human is a losing battle”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“There's the flip side, which is I think that there's a lot of awesome applications that we really want to see, like creative applications in terms of if you have sci-fi authors that can work with this tool and come with cool ideas. That seems awesome. If we can write better sci-fi through the use of these tools, and we've actually had a bunch of people write into us asking, hey, can we use it for a variety of different creative applications?”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“And you can ask it to generate content for you on basically any topic, right? You just give it a prompt and it'll just start writing and it writes content like you see on the internet, even down to saying advertisement. In the middle of some of its generations. And you think about the possibilities for generating fake news or abusive content. And, you know, it's interesting seeing what people have done with, you know, we released a smaller version of GPT-2 and that people have done things like try to generate, you know, take my own Facebook message history and generate more Facebook messages like me and people generating fake politician content or there's a bunch of things there where you at least have to think, is this going to be good for the world?”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Do I do right? And so, you know, the alternatives of, oh, I just always publish your exploits that doesn't seem good either, right? And so it really took a long time and took this, it was bigger than any individual, right? It's really about building a whole community that believe that, okay, we'll have this process where you send it to the company. If they don't act in a certain time, then you can go public and you're not a bad person. You've done the right thing. And I think that in AI, part of the response to GPD2 just proves that we don't have any concept of this. So that's the high-level picture. And so I think that this was a really important move to make. And we could have maybe delayed it for GPT-3, but I'm really glad we did it for GPT-2. And so now you look at GPT-2 itself and you think about the substance of, okay, what are potential negative applications? So you have this model that's been trained on the internet, which is also going to be a bunch of very biased data, a bunch of very offensive content in there.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Those ones, you're definitely not going to want to release into the wild. And so I think that we almost view this as a test case and to see, can we even design, how do you have a society or how do you have a system that goes from having no concept of responsible disclosure where the mere idea of not releasing something for safety reasons is unfamiliar to a world where you say, okay, we have a powerful model, let's at least think about it. Let's go through some process. And you think about the security community, it took them a long time to design responsible disclosure, right? You know, you think about this question of, well, I have a security exploit. I send it to the company. The company is like, tries to prosecute me or just ignores it.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“It wasn't clear which one outweighed the other. And I think that when we announced that, hey, we decide not to release this model, then there was a bunch of conversation where various people said, it's so obvious that you should have just released it. There are other people that it's so obvious you should not have released it. And I think that almost definitionally means that holding it back was the correct decision. If it's not obvious whether something is beneficial or not, you should probably default to caution. And so I think that the overall landscape for how we think about it is that this decision could have gone either way. There's great arguments in both directions. But for future models down the road and possibly sooner than you'd expect, because scaling these things up doesn't actually take that long.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“What are we doing now? Well, we're going to scale up GPT2 by 10x by 100x, by 1000x, and we don't know what we're going to get. And so it's very clear that the model that we released last June, you know, I think it's kind of like it's a good academic toy. It's not something that we think is something that can really have negative applications or, you know, to the extent that it can, that the positive of people being able to play with it is far outweighs the possible harms. You fast forward to not GPT2, but GPT 20, and you think about what that's going to be like. And I think that the capabilities are going to be substantive. And so there needs to be a point in between the two where you say, this is something where we are drawing the line and that we need to start thinking about the safety aspects. And I think for GPT too, we could have gone either way. And in fact, when we had conversations internally that we had a bunch of pros and cons.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so again, I think to zoom out, like the way that we thought about GPT-2 is that with language modeling, we are clearly on a trajectory right now where we scale up our models and we get qualitatively better performance. GPT2 itself was actually just a scale up of a model that we released in the previous June. We just ran it at a much larger scale and we got these results where suddenly starting to write coherent pros, which was not something we'd seen previously.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly to be regular. And that makes sense, right? That basically what we're saying is that we're going to have these technological systems that are going to be performing applications that humans already do. Great. We already have ways of thinking about standards and safety for those. So I think actually empowering those regulators today is also pretty important. And then I think for AGI, that there's going to be a point where we'll have better answers. And I think that maybe a similar approach of first measurement and start thinking about what the rules should be. I think it's really important that we don't prematurely squash progress. I think it's very easy to kind of smother the abutting field. And I think that's something to really avoid. But I don't think it's the right way of doing it is to say, let's just try to blaze ahead and not involve all these other stakeholders.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think there's kind of maybe two angles to it. So today with narrow AI applications that I think there are already existing bodies that are responsible and should be responsible for regulation. You think about, for example, with self-driving cars that you want the National Highway.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“There will be a time and place where that will change. And I think it's a little bit hard to predict exactly what exactly that trajectory should look like.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So I think that, first of all, it's really important the government's in there in some way, shape, or form. At the end of the day, we're talking about building technology that will shape how the world operates and that there needs to be government as part of that answer. And so that's why we've done a number of different congressional testimonies. We interact with a number of different lawmakers. Right now a lot of our message to them is that It's not the time for regulation, it is the time for measurement, right? That our main policy recommendation is that people, and you know, the government does this all the time with bodies like NIST spend time trying to figure out just where the technology is, how fast it's moving, and can really become literate and up to speed with respect to what to expect. So I think that today the answer really is about measurement.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's one thing we're very concerned about, right? Is that people, multiple teams figuring out we can actually get there? But, you know, if we took the slower path that is more guaranteed to be safe, we will lose. And so we're going to take the fast path. And so the more that we can both ourselves be in a position where we don't generate that competitive race, where we say if the race is being run and that someone else is further ahead than we are, we're not going to try to leapfrog. We're going to actually work with them, right? We will help them succeed as long as what they're trying to do is to fulfill our mission, then we're good. We don't have to build AGI ourselves. And I think that's a really important commitment from us. But it can't just be unilateral, right? I think it's really important that other players who are serious about building AGI make similar commitments, right? I think that, you know, again, to the extent that everyone believes that AGI should be something to benefit everyone, then it actually really shouldn't matter which company builds.”
2019-04-03 · Lex Fridman Podcast · Greg Brockman: OpenAI and AGI · IDENTIFIED FROM THE TRANSCRIPT · source