We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since 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 excited, we hear from people who are concerned, we from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going define a technology that will be so important for society going forward. And I believe that we can this for good.
So today, I want to show you the current of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of 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 just the idea 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, also generates an image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re to see. This 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 for later.” And the thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually was write a prompt just like a human could. And so sort of have this ability to inspect how the is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use in situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to of take away all those details from you. So don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you at this, you still can click 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 we have a new, augmented way to them. And now we have a tweet that’s been drafted for review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. so after this talk, you will be able to access this yourself. And 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 just about these tools. It’s about teaching the AI how to them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that AI used to produce that answer. And this allows 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 received feedback.
Now, sometimes things we have to teach the AI are not 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 wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over course of a couple of months we were able teach 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 thumbs down in ChatGPT, that actually is kind of like sending up bat signal 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 listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think 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 stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised 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 can actually use the AI fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where the model can issue search queries and click web pages. And it actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I think this really shows the shape of that we should expect to be much more common in the future, where have humans and machines kind of very carefully and designed in how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of how we interact 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 much in that time. And here is a specific spreadsheet of the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see 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 another tool, this one a interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its 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 values and 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 with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” 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 has to infer I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see these wonderful things that appear in these 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 to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of 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 to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how we may end using this technology in the future. A person brought very sick dog to the vet, and the veterinarian 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 the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So 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 few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building 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. I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality it lays. And 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 teams of people who are very different from each other to work together harmoniously.
CA: Can 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 invest in and grow them, that something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working on training a model to the next character in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough of them together, you these ant colonies 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 suburbs and cultural and traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did not see coming.
GB: Yeah, well, so you can this in ChatGPT, if 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 to it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually 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 as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the important will be that we take this step by step. that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s 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 true at any moment, but that the expansion of the scale and the human that you talked about is basically going to take on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit 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 have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And just haven’t exhausted 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. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil with AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build in secret, you this super powerful 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, 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 you in the face. And I think you do give time to give input. You do have, before these are perfect, before 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 were that 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 that are worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also one percent thing 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 that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a good time. And you think about for a moment, if you 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 to it be five years away. But if it gets to be 500 years away 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 a much more way than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look at whole history of 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 there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not tear 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 think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)