We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now we as a world are going to define a technology that will be so important our society going forward. And I believe that we can manage this for good.
So today, I to show you the current state of that technology and of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing it as an app for to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands power of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated 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 just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about these is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information and to integrate other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know menus and know all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is able to of take away all those details from you. So you don’t have 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, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been for our 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 the work the AI if we want to. And so after this talk, you will be to access this 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 to them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful 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 to teach the AI what to do with those skills. 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 thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually is kind of like up a bat signal 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 we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff all the toys in 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 will have to our ability to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This one is browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to for this and it actually does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, to be in manager’s position 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 actually turns out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I this really shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that time, if we get this process right, we will be able to solve problems.
And to 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 with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and 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 do, and the AI is happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an and it dropped off the cliff. What could be going there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we may end using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, that getting AI right is going to require participation everyone. And that’s for deciding 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 from talk, it’s that this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing the 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 also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I that the most important thing has been to get teams people who are very different from each other to together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these language models that meant if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you 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 sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us 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 bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that 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 some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and then you can start doing these predictions. are 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 these curves in there. now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one the big fears then, that 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. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I one thing people miss, too, is sort of the with the world 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. And so I that what we kind of see right now, if you look this talk, a lot of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going 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 understanding inside, the system is going to — we’re never going to know 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 one moment, that the expansion of the scale and the human that you talked about is basically going to take it on that journey of actually 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 is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s 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 to do this is to put it out in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety it and then you push “go,” and you hope got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this alternative approach is the other path that I see, which is that you do let reality hit in the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a box the 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 gifts to family and to everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this actually could unleash 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 you a story that I haven’t 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 hotel room just looking over this wonderful water, all these people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be five years away or 500 away, which 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 right, which do you pick? And you know, I just really felt in the moment. I was like, of course you the 500 years. My brother was in the military the time and like, he puts his life on the in a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to provide the guardrails for this 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 to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)