We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people like Raymond are 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 we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to show you the state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is what it’s to build a tool for an AI rather than building it for a human. 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 things like ask, you know, suggest a nice post-TED meal 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 you get out of ChatGPT. And here we go, it’s not just the idea the 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 — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even know 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 can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here 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 and see that what it actually did was write a prompt like a human could. And so you sort of have ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and let me show you what it’s like to that information and to integrate with other applications too. You 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 out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great 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 to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to of take away all those details from you. So don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is 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 shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, 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 when ask these very 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 answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes 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 what comes next, that green nine up there, is to solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to feedback to the machine alongside our team. And over course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind like sending 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 that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going to search for and it actually does the search. It then it the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut 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 and an AI. a human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And I think this really the shape of something that we should expect to be much common in the future, where we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to 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 and trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every of how 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 that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with of intent behind it. But I don’t even know what I want. the AI kind of has to infer what I be interested in. And 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 cloud the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted of the machine here. I really wanted it to this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is what I wanted. But I inject 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. I didn’t for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how think we … A vision of how we may up using this technology in the future. A person his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for the rules of the road, for what an AI will won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. different 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. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having 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, guess my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices 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 it going to to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I think that most important thing has been to get teams of people who are very different from each other 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 that you saw something in these language models that that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And think 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 we wanted 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 in reviews, and he got a result where — this a syntactic process, you expect, you know, the model 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 review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just 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, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or city where 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 one moment for you when you saw just something that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means 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 plus 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had to our entire stack. When you 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 the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, the on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is 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 to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, if you at this talk, a lot of 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 at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step by step. And that say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up track record with these machines that they’re able to actually carry out intent. And I think we’re going to have to produce even better, 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, there critics who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth wisdom and so 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 that is where we’re headed. And I think that OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus or something like that is what you need. But I think that approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on 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 to do this is to it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all of humanity, like, 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 safety of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach the only other path that I see, which is you do let reality hit you in the face. And I you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely that’s going to give 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 the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box be five years away 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 five years away. But if it gets to be 500 years away and people get time to get it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his on the line in a much more real way than any us typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the whole history of computing, I really mean it 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 the pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans do. But 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 incrementally and you’ve got to figure how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And 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 all do literate in this technology, figure out how to provide 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 we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)