We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define a technology that be so important for our society going forward. And believe that we can manage this for good.
So today, I want show you the current state of that technology and of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was write a prompt just like a could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information and to with other applications too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you of know the menus and know all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is to sort of take away all those details from you. So don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what next, that green nine up there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing where the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become useful to a human. And I think this really the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and how want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. And here is a specific of all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. let me show you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you saw and the actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how we may up using this technology in the future. A person his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has to get teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if continue to invest in them and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take step by step. And that we say, OK, as move on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale and the human feedback that talked about is basically going to take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I that our approach has always been, you’ve got to push to the limits of this technology to see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public and then all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative is the only other path that I see, which is you do let reality hit you in the face. And think you do give people time to give input. do have, before these machines are perfect, before they are powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if gets to be 500 years away and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. brother was in the military at the time and like, he his life on the line in a much more real than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put together, you get an overhang, which means that if does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety you get. And so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)