We started seven years ago because we felt like something really was happening in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel those emotions 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 society going forward. I believe that we can manage this for good.
So today, I want to show you the current state that technology and some 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 for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also an 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 we speak. 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 ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually was write a prompt just like a human could. And so you sort of this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was 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 do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different 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 these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind 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 is able to sort of take away all those 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 as said, this is a live demo, so sometimes the unexpected will happen to us. But let’s 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 is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s that I think shows 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 very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, you be 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 just about building these tools. It’s about teaching the how to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, 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 actually solve the math problem.
But we have to do a second step, too, which is teach the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better than 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 allows it to generalize. It it to teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that plus one equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the course of a couple of we were able to teach the AI that, “Hey, you should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements to the this way. And when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one that we really listen to our users and make we’re building something that’s more useful for 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, 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 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. But for this, AI itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models 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 can actually check its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a tool where the model can issue search queries and into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for this and it does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go 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 back to the side. And so thing that’s so to me about this whole process is that it’s this many-step collaboration between a human an 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 shows the shape something that we should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, we will be able to impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For 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 spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is the name of the file, column names like you saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to 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 so it’s a number of authors in the paper,” like all of that, that’s for a human to do, and the AI is to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even what 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 paper titles. All of that, think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, 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. Something crazy happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. And that’s for how we want it to slot 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 this talk, it’s that this technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building 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 think within OpenAI, we made a of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that most important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that you saw this emergence, this of 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 this helps explain the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you 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 to do 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 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, that it hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to 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 be 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 emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 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 fears then, that arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but 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, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at 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 that math problem and like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step by step. And that say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take 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 think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like is what you need. But I think that our approach 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 how can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how 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. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only other that I see, which is that you do let hit you in the face. And I think you give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that 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 that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. think you 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 started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people a good time. And you think about it for moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which 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 it gets to be 500 years away and people get more time to get it right, do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother was in the at the time 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 at time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just almost like 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, all these things, they are happening. And if you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise and not to tear us all down. 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 step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)