We started OpenAI seven years ago because felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really amazing to see how far 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 from 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 historic period right now where as a world are going to define a technology that be so important for our society going forward. And I believe that we manage this for good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see what we’re 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 expands power of what it can do on your behalf terms of carrying 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 tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and see that what actually did was write a prompt just like a could. And so you sort of have this ability inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart 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 the UI is still very valuable, right? If you look this, you still can click through it and sort of the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” 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. so after this talk, you will be able to access yourself. And there we 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 these tools. It’s teaching the AI how to 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 on the Turing test, he says, you’ll never program an to 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 provides rewards and punishments it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And this process imbues 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 there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that 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. you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort 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, the itself is happy to help. It’s happy to help us provide better 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 this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. we can actually use 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 actually given the a new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out whole chain of 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 query. It’s going to click into the blog post. And of this you could do, but it’s a very task. It’s not a thing that humans really want to do. It’s 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 easily verify any piece of whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to human. And I think this really shows the shape of something that should expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed in they fit into a problem and how we want to that problem. We make sure that the humans are 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 trustworthy machines. And I that over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to 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 ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what things are and that these are integer values and therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI 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, is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has to infer what I might interested in. And so it comes up with some ideas, I think. So a histogram of the number authors per paper, time series of papers per year, cloud 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 three is kind of the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is the 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 papers in 2022 were even by April 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel 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 overreach for it to have of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI 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 updates the title. I didn’t for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how think we … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the of the road, for what an AI will 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 anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but 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 is you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you see the things that did. And I think that the important thing has been to get teams of people who are very different from each 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 the fact you saw something in these language models that meant that if you continue to invest them and grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that you just not see 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 for how to do it. And the really interesting 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 see 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 than there are the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened 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, and it’s nuanced, too. So one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing 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 see of these curves in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth 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 in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we 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 lot of what I on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math 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, how you know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track 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 reliable ways scaling this, sort of like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically 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 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 these experts saying X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I that our approach has always been, you’ve got to push the limits of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil with AI. And you were going to build models that of, you know, somehow held them accountable and was of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to that plan. Maybe someone else does. But for me, 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 perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought 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 give beautiful gifts to family and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But if gets to be 500 years away and people get more to get it 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 military at the time and like, he puts his life the line in a much more real way than of us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look the 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 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 these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s 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)