We started OpenAI seven ago because we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are to 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 want to show the current state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an rather than building it for a human. So we have a new DALL-E model, which generates images, and are 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 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 get out of ChatGPT. And here 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. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that it actually did was write a prompt just like a human could. And so you of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s for later, and let 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 tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort of take all those details from you. So you 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 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 thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something 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 been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as tries things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, 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 process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this 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 that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that really listen to our users and make sure we’re building something that’s useful 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 all the toys in closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our to supervise the machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check 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 where the model can issue search queries and click web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search this and it actually does the search. It 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 a very tedious task. It’s a thing that humans really want to do. It’s much more to be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any 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. so thing that’s so interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to data for another AI to become more useful to human. And I think this really shows the shape of something that we expect to be much more common in the future, where have humans and machines kind of very carefully and designed in how they fit into a problem and how we want solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we will able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the names 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 there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind has to infer 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, word of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how may end up using this technology in the future. person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is 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, for an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of that artificial general intelligence benefits 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 at 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 right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s 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. If look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? We tried a lot things that didn’t work, so you only see the things did. And I think that the most important thing has been get teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by 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 language models that meant that if you continue to in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, one person was working on training a model to the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where a houses 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 something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and look an incredible number of pieces of 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 science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I think is very undersung in field is 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. is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going 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 the expansion of the scale and the human feedback that talked about is basically going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing 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 the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then you “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 think that this alternative approach is 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 give input. You do have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number thing was 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 sitting in room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there 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 tell a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you think about it for a moment, you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years and people get more time to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of course do the 500 years. My brother was in the military at time and like, he puts his life on the in a much more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still 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 moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do literate in this technology, figure out how to provide 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 we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)