We started OpenAI years ago because we felt like something really interesting happening in AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using the technology we building, and others, for so many wonderful things. We from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as a world are to define a technology that will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current state of that technology some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here we go, it’s not just 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 this case — sorry, it doesn’t generate text, it generates an image. And that is something that really the power of what it can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was write a prompt just like human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to feedback to 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 can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, are so used to 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 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 of tools, the is able to sort of take away all those from you. So you don’t have to be the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take a at the 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 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 we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there 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 this 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 building tools. It’s about teaching the AI how to use 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 human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going to be able to teach 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 feedback data. Khan himself was very kind and offered 20 hours of his own time to provide feedback 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 specific kind scenario.” And we’ve actually made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s way that we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we to harder tasks, we will 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 and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m going to search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. And out come so you can 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 was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful a human. And I think this really shows the of something that we should expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if we get process right, we 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 to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around in some 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 can see there the data right here. But let me you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just upload 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.” The only here is the name of the file, the column names like you saw and then actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, 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. didn’t ask for that, but it know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I 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 bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for setting rules of the 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 different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands 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 building on shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it going take to make progress here? We tried 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 are very different from each to work together harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models that that if you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I 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 next character in reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone 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 this thing, you’ve got to see where it goes.
CA: I think this helps explain the 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 stuff you showed just now. And the key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning 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. So it to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more 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 is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then you 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 there. And now we’re starting to be able to predict. we were able to predict, for example, the performance coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these are questions of degree and and timing. And I think one thing people miss, too, is of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, 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 math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. have to build up a track record with these machines 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 making the machine be aligned 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, the system is going to always — we’re never to know that it’s not 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 that the expansion the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth and and so forth, with a high degree of 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 has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, of all these experts X is going to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been 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 it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech that 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 here without proper guardrails or we die. You know, how you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that you do let hit you in the face. And I think you do give time to give input. You do have, before these machines are perfect, before 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 to do with 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 very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” there’s a chance that this actually 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 tell you a that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you think about it for a moment, if you could choose for basically that Pandora’s to be five 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 to have it five years away. But if it gets to be 500 years and people get more time to get it right, which you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if look at the whole history of computing, I really it when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think one thing I take away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve got to figure how to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And my hope is 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, you so much for coming to TED and blowing minds.
(Applause)