We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far this 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 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 historic period right now where as a world are going to define a technology that be so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that technology and some the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a tool for AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out of ChatGPT. And we go, it’s not 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, doesn’t generate text, it also generates an image. And that is that really expands the power of what it can on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting thing about these is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was a prompt just like a human could. And so sort 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 to use that information and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus know all 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 is able to sort take away all those details from you. So you don’t to be the one who spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, 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 list of ingredients Instacart. Here’s everything 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 something I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And to do this, use an old idea. If you go back to Alan Turing’s 1950 paper on Turing 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 teach it through feedback. Have a human teacher who provides and punishments as it tries things out and does things that are good or bad.
And this is exactly how we 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 before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the 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 is to teach AI what to do with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy help us provide even 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 and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use the AI to fact-check. And 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 is a browsing tool where the model can issue search queries click into web pages. And it 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 blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to more useful to a human. And I think this really the shape of something that we should expect to be more common in the future, where we have humans machines kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, we get this process right, we will be able solve impossible problems.
And to give you a sense of how impossible 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 they’ve really changed that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw and then actual data. And from that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put 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 authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help with it.
Now I don’t even know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the AI of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that 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 year is not over. So I’m to push back on 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 believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of machine here. I really wanted it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is just code again, so 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. I didn’t ask that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems into our world.
And 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 setting rules of the road, for what an AI will won’t do. And if there’s one thing to take away this 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 of the we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the 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 thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. that we just thought really hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you see the things that did. And I think that most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person working on training a model to predict the next character in Amazon reviews, and he got result where — 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 tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city 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 you when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to do it. the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to 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 one science we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of these curves in there. And now we’re to be able to predict. So we were able to predict, example, the 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 this that is actually scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict 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 of these are questions of degree and scale and timing. I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of reasons that we think 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 I focus on is providing high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we on to book summaries, we have to supervise this properly. We have to build up a track record 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, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. it your 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 of actually getting things like truth and wisdom and 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, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People have been neural nets 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 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 a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of slowing 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 of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure out the of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that see, which is that you do let reality hit you in face. And I think you do give people time give input. You do have, before these machines are perfect, 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 thing people were going to do with it was misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the 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 be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five 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 like, of course you do the 500 years. My was in the military at the time and like, he puts life on the line in a much more real way than any of us things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, I really mean when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I think that if you look at capability, it’s been 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 to figure out how to manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity to 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 us all down. Is basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)