We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really 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, for so wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now where we as world are going to define a technology that will be so for our society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology and of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. let’s see what 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. that is something that really expands the power of what it can do on your 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 what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with 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 coming you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was write prompt just 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 let me show you what it’s like use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all details from you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as I said, is 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 a list 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 at 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, augmented to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the 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 when we ask these very high-level questions? And to do this, use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never seen before.” this process 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 up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to the AI what to do with those skills. And this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, whole 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 received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some feedback data. Sal 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 months we were able to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way that really 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 doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As move 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 us provide even better feedback and to scale our ability to supervise machine as time goes on. And 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 learning and learning human feedback. And 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 we actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out whole chain 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 search query. It’s going click into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And I think this shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and we want 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. together we’re able to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one Python 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 about it. very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information 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 what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 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 about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that this is what wanted. But I inject my 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 back to the slide again. This slide shows a parable how I think we … A vision of how may end up 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 not be here today he listened. In the meanwhile, he provided the blood test, like, full 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 and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we should all reflect on, think about as consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding 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 thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people 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 possibilities there. I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just 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 the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders 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 from the early days. And the first one was just confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think the most important thing has been to get teams people who are very different from each other to work together harmoniously.
CA: 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 also just about the fact that you something in these language models that meant that if you continue invest in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key idea of is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind 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 do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe in some level of confidence, but 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 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. so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, 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 to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to build up a track record these machines that they’re able to actually carry out intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the and the human feedback that you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the approach here has always been just like, let reality you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. 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 to to the limits of this technology to really see in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the 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. But … If, you know, bad things are 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 great sort of check on the companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails we die. You know, how do 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 to get it right. one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super powerful thing, then you figure out the safety of it and then you push “go,” and hope you got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do 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 seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one 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 open 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 actually told before, which that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in 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 five years away 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 get 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. brother 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 the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I 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 that there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)