We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going define a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I to show you the current state 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 tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. here we 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. But ChatGPT doesn’t just generate images in 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 all a live demo. This is all generated the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a prompt just a human could. And so you sort of have ability to inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now a shopping list for the 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 wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by 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 have to the one who spells out every single sort of little 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 a look at the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? If you look this, you still can click through it and sort of 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, augmented way to build them. now we have a tweet that’s been drafted for our review, 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 to change the work of the AI if want to. And 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 these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask these very high-level questions? And 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 answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually have to do a second step, too, is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to the AI that, “Hey, you really should push back on 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 kind like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And when you do that, that’s one way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale 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 these two foundational on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the date and the search results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process 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 a human. And I think this really shows the of something that we should expect to be much common in the future, where we have humans and machines kind of very 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 machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, 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 able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human to do, and the is happy to 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 a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you can inspect. And 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. It this year look really bad. Of course, the problem that the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how think we … A vision of how we may end up using technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought information to a second vet who used it to the 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 professional and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every 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 do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. Why is you who’s come up with 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. you look at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very choices from the early days. And the first one was just to confront reality as it lays. And 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 that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have the water, 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 you saw something in these language models that meant that if continue to invest in them and grow them, that something at some point might emerge?
GB: Yes. And 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 we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training a model to predict next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if review was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number houses, things 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 did not coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible number of of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance coding problems. We basically look at some models that 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 the big fears 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 level of confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that we kind of see right now, if you look at talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually out our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or like that is what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to it out there in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions 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 been important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra 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 believe that in that is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And think about it for a moment, if you could choose 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, for you personally, it’s better to have it be five years away. if it gets to be 500 years away and people get time to get it right, which do you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I 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 this is an industry-wide or even 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 faster computers, we’re improving the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what humans could do. I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model you want us to have is we have 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 child to collectively it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)