We OpenAI seven years ago because we felt like something really interesting was in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will so important for our society going forward. And I believe that we can this for good.
So today, I want to show you the state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is what it’s like to a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was write a prompt just like a could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do 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 it explicitly which ones to in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you of know the menus and know all the 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 to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the 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 the traditional is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have a that’s been drafted for our review, which is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. And 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 slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even it to do when we ask these very high-level questions? to do this, we use an old idea. If you go back to Turing’s 1950 paper on 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 teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to say what next, that green nine up there, is to actually solve math problem.
But we actually have to do a step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it 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 received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually the 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 AI a tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. And all of 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 where you can, if want, triple-check the work. And out come citations so can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another AI become more useful to a human. And I think this really shows the shape of something that we expect to be much more common in the future, where have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I be interested in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the kind 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 this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how I we … A vision of how we may end up this technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that you 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 right? Who thinks that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees 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. If you look at the progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it going to to make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think that the 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 to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in language models that meant that if you continue to invest in 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 we knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried lot of things, and one person was working on training model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict where the commas go, the nouns 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 just like, on, anyone can do that. But this was the first time that 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 got see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is 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 colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for to do 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 it wrong. And so you can see that it’s really learning 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 in universe. So it had to have learned something general, but that 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 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, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field 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 machine learning. You to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth curves. They tell 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 look at some that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of the big 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 confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, think all of these are 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 incredibly emergent, of, very powerful thing 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 kind of see now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no 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 that expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with high 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 is where 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 is the field of broken promises, of all these saying X is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this technology to see 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 quite a controversial stance you’ve taken, that the right 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. 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 nonprofit, well you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other that I see, which is that you do let reality hit you in the face. And think you do give people time to give input. do have, before these machines are perfect, before they super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, 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 spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a 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 percent thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. 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 before, which is that shortly after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. if it 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 in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are 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, which 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, knows what kind of safety precautions you get. And so I think one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite 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 got to figure how to manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)