We started seven years ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that will be important for our society going forward. And I believe that 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 show you is what it’s like to build a for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you that 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 ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by AI as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was write a 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 feedback them.
Now it’s saved for later, and let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows new way of 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 kind of 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 of tools, the AI 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 supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional UI is still very valuable, right? If look at 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 going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what next, that green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI what do with those skills. And for 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 better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in that it 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 we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals 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 our team. And over the course of couple of months we were able to teach the AI that, “Hey, you should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when do that, that’s one way that we really listen our users and make sure 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 clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort 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 itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is 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 actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing another search query. It’s to click into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be 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 of this chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. And I think this really shows shape of something that we should expect to be much common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve been around 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 spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze 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 file and ask questions about it. And very helpfully, know, it 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 like saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” 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 what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it 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 on an exponential and it dropped off the cliff. could be going on there? By the way, all is Python code, you can inspect. And then we’ll 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 year look really bad. Of course, the problem is the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject my intent, I provide this 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, 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 very sick dog the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” brought that information to a second 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 able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having to rethink 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 just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What is it going to take to 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 different from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if you continue to in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict next character in Amazon reviews, and he got a result — this is 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 review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment you when you saw just something pop 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, model will do it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, you have it 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 the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is of engineering quality. Like, we had to rebuild our 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 engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. 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 we were to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so 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 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. But even summarizing a book, like, that’s a hard to 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) And I think that the important thing will be that we this step by step. And that we say, OK, we move on to book summaries, we have to supervise task properly. We have to build up a track record these machines that 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 this session, there are critics who say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not 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 human feedback that you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be 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 think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to push the limits of this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public 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 heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at that’s kind 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 that Google and Meta and so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this 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 that this alternative approach is the only other path that see, which is that you do let reality hit you the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually 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 do it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment 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 very strong it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And 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 in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 years away people get more time to get it right, which do pick? And you know, I just really felt it in the moment. I was like, of you do the 500 years. My brother was in the military at the time like, he puts his life on the line in a more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the field as truly 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 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 still improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and our minds.
(Applause)