We started OpenAI seven years ago we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear from people like Raymond who 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, hear from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where as a world are going to define a technology that be so important for our society going forward. And believe that we can manage this for good.
So today, I want show you the current state of that technology and of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do things like ask, you know, a nice post-TED meal and draw 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 you out of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is generated by the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and see that what it actually did write a prompt just like a human could. And you sort of have this ability to inspect how the is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind of know menus and know all the options. Yes, I would like 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, 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. And you can we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still very valuable, right? If you look this, you still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we 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 able inspect, we’re able to change the work of the AI if want to. And so after this talk, you will able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI to do with those skills. And for this, we feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually the AI to fact-check. And it can actually 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 the can issue search queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to into the blog post. And 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 want, triple-check the work. And out come citations so can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. 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 we should expect to be much common in the future, where we have humans and kind of very carefully and delicately 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 machines operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be to solve impossible 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 interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can see there the data right here. But me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to another 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 is CSV,” comma-separated file, “I’ll parse it for you.” The only information here the name of the file, the column names like you saw and then actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s a number authors in the 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 ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level instruction with lots of intent 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 some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I 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 plot of the papers year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that 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. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision how we may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to save 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 medical professional and with ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is 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 going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons 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 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 the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, 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. And that we just thought hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing been to get teams of people who are very different each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these language that meant that if you continue to invest in them and grow them, something at 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 deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict 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. he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. this was the first time that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that 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 centers and jams. Give 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 this in ChatGPT, if 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 thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can see it’s really learning the process, but it hasn’t fully generalized, right? It’s like 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 that hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and at an incredible number of pieces of text. And 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 really get at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, 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 this that actually smooth 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, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of 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 the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a track record these machines that they’re able to actually carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. 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 is going to happen, Y is how it works. have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve to push to the limits of this technology to see it 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 here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put out there in public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, bad are 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 great of check on the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then you “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path I see, which is that you do let reality you in the face. And I think you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to with it 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 for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually 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 these having a good time. And you think about it for moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, would you pick, right? On the 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 away and people get time to get it right, which do you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother in the military at the time and like, he puts his on the line in a much more real way than any us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, you 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 making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in humans could do. But I actually think that if you at 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 each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)