We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we hear people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that will be important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal draw 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 the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, 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 terms of carrying out your intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re very inspectable. So you get this little up 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 and see that what actually did was write a prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me you what it’s like to use that information and to with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind of know the menus know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if we to. And so after this talk, you will be able 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 this, it’s not just about building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re 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 the AI what 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 human them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things 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 things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s 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 to 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 same of 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 us provide even feedback and to scale our ability to supervise the as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going search for this and it actually does the search. then it finds the publication date and the search results. It is issuing another search query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a and an AI. Because a 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 really shows the shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating a way 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 problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to rethink almost aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so can just literally upload a file and ask questions 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 file, the column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction with of intent behind it. But I don’t even know what want. And the AI kind of has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of authors paper, time 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 the most common. It’s going to then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 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 kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I 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 inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A of how we may end up using this technology the future. A person 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 would be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: What is going to take to make progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has to get teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that saw something in these language models that meant that 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 is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. But this was the 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 think this explain the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you get of a 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 colonies that completely emergent, different behavior. Or a city where a 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 see coming.
GB: Yeah, well, so can try this in ChatGPT, if 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 how to 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 so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. it had 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: what’s happened here is that you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think 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. Same is in machine learning. You have to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is 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, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will that we take this step by step. And that say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a track record with these that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like the machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is at any one moment, but 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 you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to push to 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: mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in and then harness all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well you were 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 was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, the 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 always going get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking how to build 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 get this super 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 how to that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which is that you do let reality hit in the face. And I think you do give people time to input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s 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 thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do you open 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 we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five away or 500 years away, which would 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 if it gets to 500 years away and people get more time to get right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at time and like, he puts his life on the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the 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 the more that you of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very 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 like zero to one, sort of, change in what humans could do. I actually think that if 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 how to manage it each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)