We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who excited, we hear from people who are concerned, we from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology will be so important for our society going forward. And I believe that we can manage for good.
So today, I want to show you the current state of that and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here 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 ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is all a live demo. This is all by 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 just looking it.
Now we’ve extended ChatGPT with other tools too, 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.” by the way, this is coming to you, all ChatGPT users, over upcoming months. you can 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 using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way thinking about 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 it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the is able to sort of take away all those details you. So you don’t have to be the one who out every single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. And can see we sent a list 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? you look at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, which 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 work of the 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 do when we ask these high-level questions? And to do this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who rewards and punishments 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 learning process. We show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 hours of his own to provide feedback to the 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 of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up 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 really listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and 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 very tedious task. It’s not a thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful a human. And I think this really shows the of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are 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 this process right, we will able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with 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 that much that time. And here is 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 there data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people submit and therefore 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 to do, and the AI is happy to help with it.
Now I don’t 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 with lots of intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be interested in. so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is 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 of papers 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that make a fair projection? So 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 an overreach it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, need 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. cannot overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as a 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 our world.
And one thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what an will 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 from people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really 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. has 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 all building on of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that we thought really hard about like: What is it going 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 the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these models that meant that if you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a lot of things, and person was working on training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will 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 like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary 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 of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things I think is undersung in this 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 tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. basically look at some models 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 still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s of surprising you. Why 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 emergent, sort of, very powerful thing too. And so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. have to build up a track record with these that they’re able to actually carry out our intent. I think 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 session, there are critics who say that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is 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 about basically going to take it on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, of all these experts saying 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 yet. They might be right maybe 70 years plus one or like that is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public and then all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out safety of it and then you push “go,” and you you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only path that I see, which is that you do let reality hit in the face. And I think you do give people time to give input. do have, before these machines are perfect, before they super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very strong chance it’s something absolutely glorious that’s going 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 this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away or 500 years away, which would 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 people get more time to get it right, which do pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the military at time and like, he puts his life on the line in a much real way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, you think about development of other sort of technologies, 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, every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)