We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. hear from people who are excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be so for our society going forward. And I believe that can manage this for good.
So today, I want to you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an rather than building it for a human. So we a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and 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 of ChatGPT. And we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the of what it can do on your behalf in 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 what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was a prompt just like a human could. And so you of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by 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 single sort little piece of what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still 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 that they’re going away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for our review, which is also a 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 we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build 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 we ask these high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never 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, that green nine up there, is actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios that 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, we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able teach the AI that, “Hey, you really should push on humans in this specific 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 to our team to say, “Here’s an area of where you should gather feedback.” And so when you that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean their room, all you’re doing 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 will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. 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 the model can issue search queries and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for this it actually does the search. It then it finds publication date and the search results. It then is 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 that humans really want to do. It’s much more to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become useful to a human. And I think this really shows the shape something that we should expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we will be able to solve problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every of how we interact with computers. For example, think 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 past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you saw then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI is to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might 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, word of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, problem is that the 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 use to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the 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 even the title. I didn’t ask for that, but it 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 using 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.” And the dog not be here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would 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 I really deeply, is that getting AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of 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 mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having rethink the way that we 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 question is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? We a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has been get teams of people who are very different from other to work together harmoniously.
CA: Can we have 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 that you saw in these language models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, one person was working on training a model to predict next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, know, the model will 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 or negative. mean, today we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged 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 riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of them together, you get these colonies that show completely 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 and traffic jams. Give me one moment for you you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary 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 that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: here 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 that you can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, 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 think that what we kind of see right now, if look at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re to actually carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback you talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the field of broken promises, of these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. But I that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to models 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 release GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out 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 about these 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 been important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the other path that I see, which is that you let reality hit you in the face. And I you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually a one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on 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 that shortly after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out over this wonderful water, these people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My was in the military at the time and like, he puts his life the line in a much more real way than any of typing things in computers and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look the whole history of computing, I really mean it I say that this is an industry-wide or even just 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 them together, you get an overhang, which means if someone does, or the moment that someone does to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think one thing I take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But I think that if you look at capability, it’s been quite over time. And so the history, I think, of every technology we’ve 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 you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)