We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come since then. And it’s really 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 from people who both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now we as a world are going to define a that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to you the current state of that technology and some of underlying design principles that we hold dear.
So the first thing I’m going show you is what it’s like to build a for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. 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 in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the of what it can do on your behalf in terms of out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other tools too, 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 here says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was a prompt just like a human could. And so you sort 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 let me show you what it’s like to that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly ones to 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 apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still 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 build them. And now we have a tweet that’s been drafted for review, which is also a 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 of the AI if we want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing 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 the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a human child, and teach it through 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, we what Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole 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, only way to actually complete that math problem, to what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the try 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 the thing that the AI said, but very importantly, the whole process that the AI used to produce 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 to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to 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 had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way that we really listen our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. you think 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 them to stuff all toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we 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 me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the can issue search queries and click into web pages. it actually writes out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s to click into the blog post. And all of this could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations so you can actually go and 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 process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking is doing it in order to produce data for AI to become more useful to a human. And think this really shows the shape of something that we should expect to much more common in the future, where we have humans machines kind of very carefully and delicately designed in how fit into a problem and how we want to that problem. We make sure that 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 able to actually create more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I think we’re going to be able to rethink 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. 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 the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask questions about it. very 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 the file, the column names like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a number authors 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 even know what I want ask. So 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 even know I want. And the AI kind of has to what I might be 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 paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even 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 out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. And now, it does 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 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 his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human with a medical professional and with as a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for the rules of the road, for what an AI and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different anything people 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 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 … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we 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 is how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no 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. the first one was just to confront reality as it lays. And we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in these models that meant that if you continue to invest them and grow 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 always knew that was what we wanted to be, was a learning lab, and exactly how to 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 — this a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was positive negative. I mean, today we 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. there we knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. happens 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 a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw just something pop that just blew mind that you 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 it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number 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 generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning 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 that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort 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 stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can 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 are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises 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 emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, 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 I on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy look at that math problem and be 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 book summary is good? You have to read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We have to build up a record with these machines that they’re able to actually carry our intent. And I think we’re going to have 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, there critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but that the expansion 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 degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance 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 giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is 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 of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that this approach is the only other path that I see, which is that you let reality hit you in the face. And I think you give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do 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 for 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 chance it’s something absolutely glorious that’s going to give beautiful to your 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, not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just looking out over this water, all these 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, maybe you personally, it’s better to have it be five years away. 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 you do 500 years. My brother was in the military at the time and like, he his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on 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 whole history of computing, I really 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 you sort of, don’t put 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 you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to the circuit, then you 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 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 actually that if you look at capability, it’s been quite over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to 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 we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)