We started seven years ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as world are going to define a technology that will so important for our society going forward. And I believe that we manage this for good.
So today, I want to show you current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show is what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal draw 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 of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on your behalf in terms of out your intent. And I’ll point out, this is a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just at 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 this little up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it 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, and me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within 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 is able sort of take away all those details from you. So don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something that I think shows they’re not 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 very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to do with 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.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is 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 happily pretend that one plus equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. over the course of a couple of months we were to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard 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 a nice DALL-E-generated image, 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 itself is happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI 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 thought it does it. It says, I’m just going to search for and it actually does the search. It then it finds publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can actually go and easily verify any piece of this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and 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 I think really shows the shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, the feedback, and 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, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how 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 that in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a set like 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 you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of the file, the column names like you and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like all of that, that’s 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 the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be interested in. so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. course, the 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 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think we … A vision of how 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 to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He brought that information to second 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, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who 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, I guess my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just to confront as it lays. And that we just thought really about like: What is it going to take to make here? We tried a lot of things that didn’t work, so only see the things that did. And I think that most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that if you continue 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, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We a lot of things, and one person was working training a model to predict the next character in 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 verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. 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 that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just did 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 do it, which means it’s learned an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll 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 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, but that it hasn’t really fully 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 to scale up and look at an incredible number of pieces 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 science we’re starting to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every piece of the 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 our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able to predict. So we able to 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 that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, 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 this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that say, OK, as 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 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 the machine be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never to know that 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 the scale and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the of broken promises, of all these experts saying X going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it there in public and then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, 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. That release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case that what 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 going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” and you hope you it right. I 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 the only other path that I see, which is you do let reality hit you in the face. And I think you do give people time to input. You do have, before these machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a much more way than any of us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got approach this 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 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 improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the 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 what kind of safety precautions you get. And so I think one thing I take away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to 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 that you … model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not tear 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 take each step as encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)