We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now where as a world are going to define a technology that will be so for our society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than building 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 can do like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that expands the power of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a 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 looks wonderful.
(Applause)
I’m hungry just 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 tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually was write a prompt just like a human could. And you sort of have this ability to inspect how the machine using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have 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 the menus and all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it sort of modify the actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for 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 able to change work of the AI if we want to. And after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? And to do this, use an 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 could a 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 are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to solve the math problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out things, give us 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 that the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort 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 AI are not what you’d expect. For example, when first 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 three and run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course a couple of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s one way 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 think about asking a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models 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 AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m just going to search for this it actually does the search. It then it finds publication date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s 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 you can, if want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful a human. And I think this really shows the shape of that we should expect to be much more common in future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually 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 think we’re to be able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, 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 information is the name of the file, the column names 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, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy to with it.
Now I don’t even know what I want to ask. So fortunately, you can 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 want. And the AI kind of has to infer I might be interested in. And so it comes up some good ideas, I think. So a histogram of number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I 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 to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how I we … A vision of how we may end using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today he listened. In 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 who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come 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. If look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just to confront as it lays. And that we just thought really hard about like: What is going to take to make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these language models that meant that if you to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, 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 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 the next character Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was the first time that you this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a 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 things emerge. It all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop that just your 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 model do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so 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 to have something general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at incredible number of pieces of text. And it is things that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of 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 rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up track record with these machines that they’re able to actually out our intent. And I think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t common sense 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 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, think that the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been like, let reality 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 is how works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite 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, instead of just team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth all scrambling to catch up. And some of their have been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the that what you have done is responsible here and 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 right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got 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 only other that I see, which is that you do let reality you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, that you 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 people were going to with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. 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 out over this wonderful water, these people having a good time. And you think about it for a moment, you could choose 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 for you personally, it’s better to have it five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military the time and like, he puts his life on line in a much more real way than any of typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And 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. And you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does to connect to the circuit, then you 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 thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite 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 that you … the model you want us to have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)