We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering historic period right now where we as a world are going to define a technology that will be important for our society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we dear.
So the first thing I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of 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 AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And 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, upcoming months. And you can look under the hood and see that it actually did was write a prompt just like human could. And so you sort of have this to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know all the options. Yes, I like 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 spells out 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 us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a list of 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 have a new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to this 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 the AI to use them. Like, what do we even want to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have called a machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you gather feedback.” And 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 is a hard thing. If you think about a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale our ability supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning 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 time we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search and click into web 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 date and the search results. It then is issuing another query. It’s going to click into the blog post. And all of you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so can actually 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 interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think really shows the shape of something that we should expect to be more common in the future, where we have humans and kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this process right, will be 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 how we interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here is a specific 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 right here. But me show you the ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the names like you saw and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some 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. the AI kind of has to infer what I be interested in. And so it comes up with good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can you use that to 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 wanted out the machine here. I really wanted it to notice this thing, it’s a little 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 inspect what it’s doing, it’s possible. And 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 the slide again. This slide shows a parable of I think we … A vision of how we may end up this technology in the future. A person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are hypotheses.” He brought 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 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 these systems into our world.
And one thing I really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing take away from this 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 the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out 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 about way I work, I need to rethink.” Like, there’s just possibilities there. Am I 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 actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very choices from the early days. And the first one just to confront reality as it lays. And that we just thought really hard about like: What it going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how do it? I think that in the early days, didn’t know. We tried a lot of things, and one person working on training a model to predict the next character in Amazon reviews, he got 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 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 riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that 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 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 in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get wrong. And so you 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 table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting 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 entire stack. you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that take this step by step. And that we say, OK, we move on to book summaries, we have to this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no real understanding inside, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the scale and the human that you talked about is basically going to take 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, think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this to put it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” you hope you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach 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 time to give input. You have, before these machines are perfect, before they are powerful, that you 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 people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely 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 this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. 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 that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And you about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get more time to it right, which do you pick? And you know, just really felt it in the moment. I was like, of course do the 500 years. My brother was in the at the time and like, he puts his life the line in a much more 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 got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans could do. But I actually think if you look at capability, it’s been quite smooth 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 figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that that will continue to be the path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)