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