We started OpenAI seven years ago because felt like something really interesting was happening in AI and wanted 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 from people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as world are going to define a technology that will be so important for society going forward. And I believe that we can this for good.
So today, I want to show the current state of that technology and some of the design principles that we hold dear.
So the first I’m going to show you is what it’s like build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very 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. that is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the AI as we speak. I actually don’t even know 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 for later.” And the interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see what it actually did was write a prompt just a human could. And so you sort of have this to inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT is selecting all these different tools without having to 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 have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details from you. you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think 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 drafted for review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an 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 provides rewards and punishments as it tries things out and 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 unsupervised process. We just show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all sorts of wonderful skills. 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 actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it scenarios that it hasn’t seen 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 is so great, We’re going to be able to students 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 one equals three and run 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 machine alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending 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 and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it actually writes out its 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 into the blog post. And of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And think this really shows the shape of something that we should to be much more common in the future, where we have and machines kind of very carefully and delicately designed how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every of how we interact with computers. For example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed 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. let me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able 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 it for you.” The only information here is the of the file, the 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 knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now I don’t even 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. so it comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can all these wonderful things that 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 push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of 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 ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted of 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 I inject 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 inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I we … A vision of how we may end up this technology in the future. A person brought his sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he the blood 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 vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, think, shows 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 into our world.
And one thing I really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for setting the rules the road, for 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. different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having 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 is how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If 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 it lays. And that 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 of people who are very different from each other work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these models that meant that if you continue to invest them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think 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 deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, expect, you know, the model will predict where the go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. yet, what 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. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for when you saw just something pop that just blew mind that you 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, model will do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to learned something general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible number pieces of 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 some of emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you 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 to predict. we were able to predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I one thing people 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 that we it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of what 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 a book, like, that’s hard thing to supervise. Like, how do you know if book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take step by step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going always — we’re never 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, but the expansion of the scale and the human feedback that you talked is basically going to take it on that journey actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field broken promises, of all these experts saying X is to happen, Y 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 or something like that is what you need. 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 that tells then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial 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 just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models 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 I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. don’t know how to 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 let reality you in the face. And I think you do people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are 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 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 actually also a one percent thing the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. 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 after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, puts his life on the line in a much real way than any of us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if look at the whole history of computing, I really it when I say that this is an industry-wide or just 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 making computers, we’re still improving the algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage connect 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 get. And so I think that one thing I take is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)