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