We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people are 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 an historic period right now where we as a world going to define a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I to show you the current state of that technology and some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for 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 images in case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of it can do on your behalf in terms of out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. So I actually don’t even what we’re going 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 can “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just like a human could. And you sort of have this ability to inspect how machine 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 applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to thinking of, well, we 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 you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all details from you. So 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 happen to us. let’s take a look at the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build 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 change the work of the AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how to use them. Like, what we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, we feedback. We have the AI try out multiple things, give multiple 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 process that the AI used to that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s one way that we really to our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And 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 for this, AI itself is happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually 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 date and the search results. It is issuing another search query. It’s going to click 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 to do. It’s more fun to be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two and one week, that was 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. Because human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. I think this really shows the shape of something that we expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they into a problem and how we want to solve problem. We make sure that the humans 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 to create even more trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I 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 in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI 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 intent behind it. But I 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 of the number of authors per paper, series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And 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 year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 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 there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, 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, it the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of we may end up using this technology in the future. person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we how to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do 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 question actually is just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one just to confront reality as it lays. And that we thought really hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so you only see the things did. And I think that the most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these language models that meant if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. this was the first time that you saw this emergence, 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 goes.
CA: So I think this helps explain the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for when you saw just something pop that just blew mind 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 will do it, which means it’s really learned an circuit for how to do it. And the really thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one the things I think is very undersung in this is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that is 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 here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, we have 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 think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real understanding inside, the is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically going to take on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push the limits of this technology to really see it action, because that tells you then, oh, here’s how can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all 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 you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, then you figure out the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you do let reality hit in the face. And I think you do give people to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do 500 years. My brother was in the military at the 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 sold on the you’ve got to approach this right. But don’t think 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. the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which means that someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do 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 … model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take 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 want from it. And my hope is that that continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)