We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a are going to define a technology that will be so important our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to 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 are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just 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 carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. So I actually don’t even know 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 to you, all ChatGPT users, upcoming months. And you can look under the hood and see that it actually did was write a prompt just like a human could. so you sort of have this ability to inspect the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the tasty thing 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 you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And you see we 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 that I think shows that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been drafted 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 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, the 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 we these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries 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 would have a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat to 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 hard thing. If you think about asking a kid to clean room, if all 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 the sort of reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to 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 this, of how much time passed between these two 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 better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going to search for this and it does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is 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 a human. And think this really shows the shape of something that we expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be to rethink almost every aspect 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 spreadsheet 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 you 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 to code, just like a data scientist would. And so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a 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 authors in the paper,” like all that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has infer what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could be 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 appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 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 was I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI 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, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I we … A vision of how we may end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not be here today 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.” brought that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for 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 anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that 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 feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re 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, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you saw something in these language that meant that if you continue to invest 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, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs 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, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, 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 about this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as you scale up, 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 of truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math problem be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing will be we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. have to build up a track record with these machines that they’re able to actually carry out intent. And I think we’re going to have to 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, there critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of the and the human feedback that you talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y 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 years plus one something like that is what you need. But I think our approach has always been, you’ve got to push the limits of this technology to really see it in action, because that you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and then all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, know, the original 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 that of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But 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 you supposed do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which that you do let reality hit you in the face. I 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, we really were that the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash evils 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 I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts his life on line in a much more real way than any us typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history computing, I really mean it when I say that is an 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 are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we encounter it. And I think it’s important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)