We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying to 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 from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a that will be so important for our society going forward. And I believe we can manage this for good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for an AI than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app for to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that is something that really the power of what it can do on your behalf terms 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 know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see what it actually did was write a prompt just like a could. And so you 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 you what it’s like to use information and to integrate with other applications too. You say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools 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 it’s a great experience within an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take all those details from you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list 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 interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it and sort 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 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 able to access 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 we ask these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on 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 punishments as it tries things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to what comes next, that green nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that 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, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And over the course a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs down in ChatGPT, that 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 to 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 in closet. This is a nice DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will to scale 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 and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning and 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 every time we provide some feedback. But we can use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. one is a browsing tool where the model can search queries and click into web pages. And it writes out its whole chain of thought as it does it. says, I’m just going to search for this and it actually the search. It then it finds the publication date the search results. It then is issuing another search query. It’s 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 much more fun to be in driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more useful to a human. And think this really shows the shape of something that should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense just 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 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. here is 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 right here. But let show you the ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, the column names like you saw then the actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s these 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 a human to do, and the AI is happy help with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of 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 kind of most common. It’s going to then make this nice plot of the papers per year. Something is happening in 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 is code, you can inspect. And then we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make 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 of machine here. I really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I inject 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 what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows a parable of how think we … A vision of how we may end using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save the dog’s life. Now, 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 to an outcome that would not have happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we 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 … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very 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 to rethink way 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, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the days. And the first one was just to confront as it lays. And that we just thought really hard about like: is it going to take to make progress here? We a lot of things that didn’t work, so you only see things that did. And I think that the most thing has been to get teams of people who very different from each other to work 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. isn’t there something also just about the fact that saw something in these language models that meant that if you to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training a to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the 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 the stuff you showed us just now. the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s 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 to up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think very undersung in this field is 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 machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, the on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level 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 integration with the is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step 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. And I we’re going to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying is going to happen, Y is how it works. have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like is what you need. But I think that our approach always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on to new 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 is put it out there in public and then harness this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think about questions 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 thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which is that you do reality hit you in the face. And I think you do give people time to give input. do have, before these machines are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a strong chance 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 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 you a story that I haven’t actually told before, which is that shortly after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people having a time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe 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 you pick? And you know, I just really 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 on the line a much more real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at 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 sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being like a zero to one, 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 technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)