We OpenAI seven years ago because we felt like something 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 whole field has come then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world are going to define technology that will be so important for our society going forward. And I that we can manage this for good.
So today, I to show you the current state of that technology and of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use your behalf. And you can 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 details for you that you get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt like a human could. And so you sort of have ability to inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, and me show you 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 for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so used to thinking of, well, we have 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 menus know all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still very valuable, right? If you look this, you still can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask very high-level questions? And to do this, we use old idea. If 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 human child, and then teach it through feedback. Have a human 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 a child machine through an unsupervised learning process. We just show it the whole world, 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, only way to actually complete that math problem, to what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t 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, is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback and to our ability to supervise the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going to search for this and it does the search. It then it finds the publication date and the search results. It then issuing another search 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 not a thing humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more to a human. And I think this really shows shape of something that we should expect to be much common in the future, where 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 are the management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that in 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 the 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 a file ask questions about it. And very helpfully, you know, knows 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 column names you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once 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 of to infer what I might be interested in. And 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 can do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could 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 things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back 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 the cut-off date I believe. Can you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice thing, maybe it’s a 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 the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I think we … vision of how we may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right going to 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. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we 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 question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google 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 building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I think the most important thing has been to get teams of who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that if continue to invest in them and grow them, that something at 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, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and how 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 next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was or negative. I mean, today we are just like, come on, can do that. But this was the first time that you saw this emergence, this sort semantics that emerged from this underlying 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 explain riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the you showed us 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 run around, when bring enough of them together, you get these ant that 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, like and 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 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 model will do it, 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 really the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So we were able to predict, example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you up, things emerge 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 and timing. I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, 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 we kind of see right now, if look at this talk, a lot of what I on is 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 correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to this task properly. We 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, reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the feedback that you talked about is basically going to it on that journey of actually getting to things truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something that is what you need. But I think that our approach has always been, you’ve to 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 a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case that what 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, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is that you do reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story 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 in the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to 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 the moment. was like, of course you do the 500 years. My was in the military at the time and like, he puts life on the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the whole history computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s any time to 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 development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been quite over 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 out how to manage it for each moment that you’re it.
CA: So what I’m hearing 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 a new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do literate in this technology, figure out how to provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, but it’s so good we’re having this debate 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)