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