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