We started OpenAI years ago because we 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 how far this whole field has come since then. it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering historic period right now where we as a world are to define a technology that will be so important our society going forward. And I believe that we manage this for good.
So today, I want to show you the state of that technology and 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 build a for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal 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 you that you get out of ChatGPT. And here we go, it’s just 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 in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the of what it can do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it did was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you make 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 any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI able to sort of take away all those details from you. you don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way build 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 we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this 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 pretend one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat to our team to 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 our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, 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 itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. the model 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 can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. It says, I’m just going to search this and it actually does the search. It then finds the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. all of this 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 be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. I think this really shows the shape of something that should expect to be much more 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 the management, the oversight, the feedback, and the machines are operating in 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 give you a sense of just how impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around 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 specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally upload 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 information here is the name of the file, the names like you saw and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a that people 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 of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you some exploratory 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 so it comes up with some ideas, I think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I we … A vision of how we may end using this technology in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He brought that information to a second vet who 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 human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look 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 I right? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What it going to take to make progress here? We tried lot of things that didn’t work, so you only see things that did. And I think that the most important thing has been to get of people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you something in these language models that meant that if you continue to in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model to the 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 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 do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. the really interesting thing 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 that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And it learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to 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 this field 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. Same is in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re to be able to predict. So we were able predict, for 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 though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale 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 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 sort of the integration with world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I focus 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, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that we take step by step. And that we say, OK, as we move on 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 think we’re going to have to even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics say that, you 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 doesn’t have common sense and so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of scale and the human feedback that you talked about is going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is you need. But I think that our approach has been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to put it there in public and then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the story that 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 were going to build models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper or we die. You know, how do you, like, make case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the other path that 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 have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating 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 that that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time to get it right, do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother was in military at the time and like, he puts his life on the line in a much more way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole history of computing, really mean 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 put together the pieces are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)