We started OpenAI seven years because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to 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 so many wonderful things. We hear from people 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 a world are going to define a technology that be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do 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 of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really the power 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 know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt just like human could. And so you sort of have this ability inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if do make 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 which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take all those details from you. So you don’t have to the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? you look at this, you still can click through it and of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been for our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able access this 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 these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we an old idea. If 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. 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 out does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And 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 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 what 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 human rates them, “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort infer 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 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. there’s some bad math in there, it will happily pretend that one plus one equals and run with it.” So we had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the machine alongside team. And 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 actually made lots and lots of to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If think 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 to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning 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 happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And let me you 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 time we provide some feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where model can issue search queries and click into web pages. it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. And I think this shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we this process right, we will be able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some 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 papers 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 like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just literally upload a 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 the name of the file, column names like 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 there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of the papers per year. 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, can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the year not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think we … 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 to say, “Let’s just 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, are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink 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 actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on 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 building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing has been get teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person working on training a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today are just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just pop that just blew your mind that you just 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 model will do it, means it’s really 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 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly 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 incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book 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 step step. And that we say, OK, as we move on to 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 to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can be 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 just like, reality hit you in the face, right? It’s like this field is the field broken promises, of all 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 been yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because tells you then, oh, here’s how we can move 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 the right way do this is to put it out there in public then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to catch up. 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 reckless.
GB: Yeah, we think about these questions all 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 incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. I don’t know how 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 approach is only other path that I see, which is that you do reality hit you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, you actually have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils 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 I haven’t actually told before, which is 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 over this water, all these people having a good time. And think 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 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 time get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real way any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And so I think one thing I take away is like, even you about development 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 I actually that if you look at capability, it’s been quite over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)