We started seven years ago because we felt like something really was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to how far this whole field has come since then. it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from who feel both those 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 world are going to define a technology that will so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look under the hood and see what it actually did was write a prompt just like a could. And so you sort of have this ability to inspect how the machine using these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information and integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able to sort 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 happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can 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 very valuable, right? If look at this, you still can click through it and sort 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 a tweet that’s been drafted for our review, 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 of the AI 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 about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these high-level questions? And to do this, we use an 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 who rewards and punishments as it tries things out and things that are either good or bad.
And this is 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 just show it the world, the whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. have the AI try out multiple things, give us 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 importantly, the whole process that the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI 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. one problem, it doesn’t double-check students’ math. If there’s some bad math in there, 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 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 in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind of like sending up 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 way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where the model can issue search queries and into web 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 to click the 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, to be this manager’s position where you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so to me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI to become useful to a human. And I think this really shows the shape of something that should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately 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 are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this right, we will be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet of all 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 set like this.
So we give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information 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 these columns mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like were on an 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 see word cloud. So can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look bad. Of course, the 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 papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I 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 have of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI 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 I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not 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 cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into world.
And one 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, for what an AI will won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, 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 and you think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to the way that we 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 first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on 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 think within OpenAI, we made a lot of very deliberate choices from the 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 here? We tried a lot of things that didn’t work, you only see the things that did. And I think the most important thing has been to get teams people who are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw in these language models that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a of things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got to scale 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 are described as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you saw just pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the 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 get it wrong. And you 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 are in the universe. So it had to have learned general, but 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 it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has to incredibly 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. tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able 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, though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build a 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, 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 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 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 you talked about is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these saying X is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really see it in action, because tells you then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s kind 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 now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going to it right. But one thing I think has been 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 of being, well, you build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, didn’t feel right. And so I think that this approach is the only other path that I see, which is you do let reality hit you in the face. And I you do give people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a 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 to your and to everyone. But there’s actually also a one percent thing in the 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. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico 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 Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if 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, of course do the 500 years. My brother was in the military at time and like, he puts his life on the line in much more real way than any of us typing things in and developing 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, you look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the 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 them together, get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child may have superpowers 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 us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and our minds.
(Applause)