We started OpenAI seven years ago because felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we hear people who are concerned, we hear 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 that will so important for our society going forward. And I believe we can manage this for good.
So today, I want show you the current state of that technology and some the underlying design principles that we hold dear.
So the first thing I’m going to show is what it’s like to build a tool for an AI rather than building it for a human. we have a 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 of the, sort of, ideation and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll point out, this is all a live demo. is all generated by the AI as we speak. So I don’t even know 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 tools is they’re very inspectable. So you get this 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 what it actually did write a prompt just like a human could. And so you sort of this ability to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones to use in situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to sort of take all those details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also a important 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 to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what 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 to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce 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 of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI 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 that one.” And reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside 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 in this 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 gather feedback.” And so when you do that, that’s way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a 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, AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. then it finds the publication date and the search results. then is issuing another search query. It’s going to click into blog 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 to 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 actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I this really shows the shape of something that we should expect to be much common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that time, if we get this process right, we will able to solve impossible problems.
And to give you a of 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 been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a 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 data right here. But let me show you the take on how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names 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 a site that people submit and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for 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 once again, is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of to infer what I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. now, it does the 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 slide again. This slide shows a parable of how I think we … A of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided 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.” brought that 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 brainstorming was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just from 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 mission of ensuring 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 of reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot 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 people who are very different from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language that meant that if you continue to invest in and grow them, that something at some 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 what we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a model to predict the character in 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 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 the first time that you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that blew your mind that you just did not see 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 it. And the really interesting thing is actually, if have 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 really the process, but it hasn’t fully 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 yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to scale 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 capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. So we were 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 something this that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things that 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 terrible emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful 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 talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But even 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 the book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you about is basically going to take it on that journey of actually to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has been just like, let reality hit you in the face, right? It’s like field is 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 for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. But think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, because tells you 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 is to it out there in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow held them accountable and capable of 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 the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, think 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 I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. I think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people a good time. And you think 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 hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets be 500 years away and people get more time to it right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more real way than any of us typing in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost 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 computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one I take away is like, even you think about of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been smooth over 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 it for moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to is that we have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise not to tear us 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 to take each step as we encounter it. And think it’s incredibly important today that we all do literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)