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