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