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