We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now we as a world are going to define a technology will be so important for our society going forward. And I 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 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 images, and we are exposing it as an app for ChatGPT use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, this is all a demo. This is all generated by the AI as speak. So 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 other too, for example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt just like a human could. And so sort 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 let me show what it’s like to use that information and to integrate with 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 for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as long as you of know the menus and know all the options. Yes, I 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 be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s 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 you need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still click through 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 to build them. now we have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes next in 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, that green nine up there, is to solve the math problem.
But we actually have to do second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what 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 teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat to our team 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 more useful everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re is inspecting 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 harder tasks, we have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to us provide even better 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 time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the 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 the AI a new tool. This one is a tool where the model can issue search queries and into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search this and it actually does the search. It then it finds the date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you do, but it’s a 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 you want, triple-check work. And out come citations so you can actually and very easily verify any piece of this whole of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data another AI to become more useful to a human. I think this really shows the shape of something we should expect to be much more common in the future, where have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t 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 can see there the data right here. But let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And you can 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 it for you.” The information here is the name of the file, the 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. has to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know I want. And the AI kind of has to what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of an overreach for it to have of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here 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 to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding how we want it to 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 take away from talk, it’s that this technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s 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 has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What it going to take to make progress here? We tried 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 different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was on training a model to predict the next character Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you 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 number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to have learned something general, 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 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 going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think all of 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. And so that’s of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that 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. even summarizing a book, like, that’s a hard thing to supervise. Like, how do know if this 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 this by step. And that we say, OK, as we move on to book summaries, we have to supervise 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 to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: we’re going 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 to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that talked about is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you the face, right? It’s like this field is the of broken promises, of all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think that approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is put it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make the case that what have done is 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 I think has incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. And think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the at the time and like, he puts his life the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole history of computing, I 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 you sort of, don’t 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 don’t put them together, you get an overhang, which means if someone does, or the moment that someone does to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that … 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. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. my hope is that that will continue to be the path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)