We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. hear from people who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where as a world are going to define a technology will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED meal 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 get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in terms of carrying out intent. And I’ll point 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 see. This looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here that says “use the DALL-E app.” 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 could. And so you sort of have this ability to how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details from you. So don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s a look 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 the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we have 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 we are, we’re manager, we’re able to inspect, we’re able to change work of the AI if we want to. And so after this talk, you will be able to 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 how to use them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human 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 Turing would called a child machine through an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach the what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so 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 math in there, it will happily pretend that plus one equals three and run with it.” So we had to collect feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine 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 lots and of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check its work. You can 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 search queries and click into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and actually does the search. It then it finds the date and the search 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, to in this manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any piece of whole 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. so thing 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 to produce data for another AI become more useful to a human. And I think really shows the shape of something that we should expect be much more common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is a specific of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, 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 you saw and the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year 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 not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you 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 I out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know 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. person brought his very sick dog to the vet, 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 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 a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we 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 one thing take away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence benefits 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 the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one just to confront reality as it lays. And that we just thought really hard about like: is it going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I think that the important thing has been to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in language models that meant that if you continue to 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? think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got result where — this is a 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 tell you if a was positive or negative. I mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number 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 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, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had have learned something general, but that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this 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 all these 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 to be able to predict. So we were able predict, for example, the performance on coding problems. We basically 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 here is, one of the 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 predict in some level confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at talk, a lot of what I focus on is 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 correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this properly. We have to build up a track record with these 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 in session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback you talked about is basically going to take it that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach 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 X is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to put it there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you 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 know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which is that do let reality hit you in the face. And I think you do people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in the small print there 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 you don’t do it that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets to 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, he puts his life the line in a much more real way than any of us typing things in computers and developing technology at 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, if look at the whole history of computing, I really it when I say that this 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 the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if don’t put them together, you 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 to adjust, who knows what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development 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 at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)