We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as a world going to define a technology that will be so important for our going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology and some the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s 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 we are exposing it as app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands the power what it can do on your behalf in terms of out your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” the interesting 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 coming to you, all ChatGPT users, over upcoming months. And you can look under the hood and see what it actually did was write a prompt just a human could. And so you sort of have ability to inspect 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 other applications too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is all these different tools without me 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 of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as 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 able to sort of take away all those details you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think that they’re not going 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 important thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we want to. And so this talk, you will be able to access this yourself. And there 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 just about these tools. It’s about teaching the AI how to use them. Like, what do we even it to do when we ask these very high-level questions? And to do this, use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try multiple things, give 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 AI said, but importantly, the whole process that the AI used to that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to 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 to be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple of months we were able teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots and lots of improvements to the this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re building something that’s more useful 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. is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and it 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. And all this you 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 in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become more useful to a human. And think this really shows the shape of something that we should expect to be much more common in future, where we have humans and machines kind of carefully and delicately designed in how they fit into a and how we want to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be to solve impossible 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 of we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it 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 semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people 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 to help with it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know what I want. And the AI of has to infer what I might be interested in. so it comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be on there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want inspect what 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 cut back to slide again. This slide shows a parable of how I think we … A vision of we may end up using this technology in the future. person brought his 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 here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He brought that to 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, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits all 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 at that you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the 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, guess my first question actually is just how the 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 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 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 the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is it going to take 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 most important has been to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few 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 something that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, 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. the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often 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 the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible of pieces of text. And it is learning things that didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece the stack engineered 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 curves there. And now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one the 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 capable of you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that math problem and 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 the whole book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a track with these machines that they’re able to actually carry out intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the is going 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 one moment, but that the expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that 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 just like, let reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think that approach has always been, you’ve got to push to the of this technology to really see it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative is the only other path that I see, which is that you let reality 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, that 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 was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. think 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 wonderful water, these people having a good time. And you think about for a 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 one hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years and people get more time to get it right, which do pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I really mean it when say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means that if 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 you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually 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 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 we have birthed this extraordinary child that may have that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s 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 in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)