We started seven years ago because we felt like something really was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s gratifying to hear from 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 people who feel both those 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 world are going to define a technology will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to show the current state of that technology and some of the underlying principles that 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 it 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 like ask, you know, suggest a post-TED meal and draw a 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 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 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 behalf in of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” 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 by the way, this coming to you, all ChatGPT users, over upcoming months. And can 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 using these tools, which allows to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make 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 new way of thinking about the user interface. Like, are so used 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 kind know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the is able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of 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 look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. And so after this talk, you will 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 the AI 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 old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are 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. If there’s bad math in there, it will happily pretend that one one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months 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. when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you 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, providing high-quality feedback is hard thing. If you think about asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This a 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. But for this, AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability supervise the machine as time goes on. And let show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. one is a browsing tool where the model can search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going to for this and it actually does the search. It then it finds publication date and the search results. It then is issuing another 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 a that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it actually out 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 me about this whole process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I think this really shows the of something that we should expect to be much more common the future, where we have humans and machines kind of carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. let me show you the ChatGPT take on how analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together 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 in the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might interested in. And so it comes up with some good ideas, I think. a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how we may up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not 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 deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh my goodness, 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, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with this technology that shocked 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, data progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that we thought really hard about like: What is it going to take make progress here? We tried a lot of things didn’t work, so you only see the things that did. I think that the most important thing has been get teams of people who are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something in these language models meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working 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 model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a review was or negative. I mean, today we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps explain riddle that baffles everyone 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 could 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 different emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in 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 all of these are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we this step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up a track record with machines that 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 you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any one moment, but that 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 so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like field is the field of broken promises, of all experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way do this is to put it out there in and then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going get it right. But one thing I think has incredibly important, from the very beginning, when we 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 default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got 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 I think that alternative approach is the only other path that I see, which is that you do let reality you in the face. And I think you do give people to give input. You do have, before these machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, 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. believe that 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 to everyone. there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all people having a good time. And you think about for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 years away and people get time to get it right, which do you pick? And you know, just really felt it in the moment. I was like, of course you the 500 years. My brother was in the military at time and like, he puts his life on the line a much more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach 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 even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s 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 out how to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach 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 say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)