We started OpenAI seven years because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are to define a technology that will be so important our society going forward. And I believe that we can manage for good.
So today, I want to show you the current state that technology and some of the underlying design principles that we dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a nice post-TED meal and draw 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 you get 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 going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” 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, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the is using these tools, which allows us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information to 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 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 how it tastes.
But you can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one who out every single sort of little piece of what’s to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still very valuable, right? If look at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able 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 build this, it’s just about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? And to this, we use an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a 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 a human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And over the course of 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 lots of improvements to the models this way. And when you push that thumbs down in ChatGPT, that 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 that we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy help us provide even 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 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 getting better time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where the can issue search queries and click into web pages. it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s 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 this manager’s where you can, if you want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so to me 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 order to produce data for another AI to become more useful to a human. And think this really shows the shape of something that we should expect to be much more in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a 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 to actually create more trustworthy machines. And I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to analyze a data set 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 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, the column like you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values and so therefore it’s number 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 know what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory 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 in. And so it comes up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the most common. It’s going to then this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and it dropped the cliff. What could be going on there? By 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 pretty unhappy about 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 on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a of how I think we … A vision of how we may end up this technology in the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second 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 and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something should all reflect on, think about as we consider how 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 we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring 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 of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing been to get teams of people who are very different each other to work together harmoniously.
CA: Can we the water, by the 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 in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, 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 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 was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged 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 as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how do 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 see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And it is learning things that you didn’t know it was 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 these emergent capabilities. And do that actually, one 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 you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even 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 here, that as you scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at this 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 to look at that math problem and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able to carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that expansion of the scale and the human feedback that you talked 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 be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But think that our approach has always been, you’ve got push to the limits of this technology to really it in action, because that tells you 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 a controversial stance you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of I 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 this out here without proper guardrails or we die. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously 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 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 default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. 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 I think this alternative approach is the only other path that I see, which is that do let reality hit you in the face. And think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to elections. Instead, the 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 you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it 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 an conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And you think about it for a moment, 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 be years away. But if it gets to be 500 years away and people get time 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 was in the military at the time and like, puts his life on the line in a much more way than any of us typing things in computers developing this technology 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 playing the field as it truly lies. Like, if you look at the whole history of computing, 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 you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one I take away is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, sort of, in what humans could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue 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 much for coming to TED blowing our minds.
(Applause)