We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions at once. And honestly, that’s we 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 be so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that technology and some of underlying design principles that we hold dear.
So the 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 things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but a very, very detailed spread. So let’s 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 power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
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I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and see that what it did was write a prompt just like a human could. And you sort of have this ability to inspect how the is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with 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 out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is able sort of take away all those details from you. So 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, is a live demo, so sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think shows 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 very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these very high-level questions? And to do this, use an old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries out 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 through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back on humans 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 of weakness where you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually use the AI to fact-check. 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 tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to for this and it actually does the search. It it finds the 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 you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to more useful to a human. And I think this really shows the shape something that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we this process right, we will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we 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 changed that in that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column names like saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work for a human do, and 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 exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI of has to infer what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By 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 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 of papers 2022 were even posted 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, is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to the 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 and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a medical professional and with as a brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take away from 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 the reasons we released ChatGPT.
Together, 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. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that 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 my first actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first was just to confront reality as it lays. And that we thought really hard about like: What is it going to take make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I think the most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: we have 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 that you saw in these language models that meant that if you continue invest in them and grow them, that 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, we always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single 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 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 see coming.
GB: Yeah, well, so you can try this in ChatGPT, if 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 do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can see that it’s 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 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 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 things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re to really get good at is predicting some of emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell 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 able to predict. So we were able to predict, for example, performance 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 actually scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important thing will be we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the expansion of the and the human feedback that you talked about is going to take it on that journey of actually to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has been just like, let reality hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural 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 what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see 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 in public and then harness all this, you know, of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so 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 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 were afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe in that box is something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to five years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away people get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at time and like, he puts his life on the in a much more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the moment that 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 safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change in humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do 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 that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)