We started seven years ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear 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 we’re entering an historic period right now where we as a world going to define a technology that will be so important for our society forward. And I believe that we can manage this for good.
So today, I to show you the current state of that technology and some the underlying design principles that we hold dear.
So the first I’m going 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 DALL-E model, which generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do things 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 back-and-forth and taking care of the details for you that you out 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 to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was a prompt just like a human could. And so you of have this ability to inspect how 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 information and integrate with other applications too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so 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 of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able to sort of away all those details from you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping 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 interesting is that the traditional UI is still very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what we even want it to do when we ask very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments 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, we produce what Turing would called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will 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 his own time to provide feedback to the alongside our team. And over the course of a couple of we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and from human feedback. And the 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 check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. It says, I’m just to search for this and it actually does the search. then it finds the publication date and the search results. then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually out two months was wrong. Two 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 process is that it’s 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 human. And I think this really shows the shape of that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if get this process right, we will be able to impossible problems.
And to give you a sense of 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, about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to 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 the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, column names like you saw 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 sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to with it.
Now I don’t even know what I 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 of intent behind it. I don’t even know what I want. And the AI kind of has to infer what might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to what it’s doing, it’s very 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 to the 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 a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information a 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 medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of 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 looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, 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 that a large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is 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 it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that 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 thing has been to teams of people who 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 to it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these language that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there 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, and exactly how to it? I think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a model predict the next character in Amazon reviews, and he a result where — this is a syntactic process, 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 analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that you this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and 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 ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal for how to do it. And the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible number of pieces text. And 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. 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 is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? have to read the whole book. No one wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, it is true at any one moment, but that expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the approach here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, of these experts saying X is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can move to 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, you know, things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s 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 forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only path that I see, which is that you do reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, before they are powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in small print there 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 don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to 500 years away and people get more time to get it right, which do you pick? you know, I 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, he his life on the line in a much more real way than any of us typing in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at whole history of 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 that sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you 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 of precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it 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 say this shift, right? We’ve got to take each step as we encounter it. And I it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)