We started seven years ago because we felt like something really was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering historic period right now where we as a world going to define a technology that will be so important for our society going forward. And I believe we can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a 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 an app ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, 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 the idea for the meal, but a very, very spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI as we speak. So I don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback 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 list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you kind of the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you still click through it 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 a new, augmented way to build them. And now have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it do when we ask 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, he says, you’ll never an answer to this. Instead, you can learn it. could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And this 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 world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going 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, will happily pretend that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s way that we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise machine as time goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, 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 check its own work. You can say, fact-check this for me.
Now, 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 of 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 and the search results. It then is issuing another search query. It’s going 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 much more fun be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can actually and very easily verify any piece of this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to more useful to a human. And I think this really shows the shape of something 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 problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines operating in 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 we get process right, we will be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re 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 they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of the file, 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 a site that people submit papers and therefore that’s what things are and that these are integer values and therefore it’s a number of authors in the paper,” all 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 to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. So a of the number of authors per paper, time series papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it can 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. What could be on there? By the way, all this is Python code, you 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 look really bad. Of course, problem is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can 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 to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This slide shows a parable of I think we … A vision of how we end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full 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 it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require 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 will won’t do. And 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 have become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to the way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And we just thought really hard about like: What is it going to take to make progress here? We a lot of things that didn’t work, so you see the things that did. And I think that most important thing has been to get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, by way, just 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 in these language models that meant that if you continue to invest in them and grow them, 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 we wanted be, was a deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and person was working on training a model to predict next character in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, these things are described as prediction 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 you us just now. And the key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you 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 it, which means it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so you can 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. So had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. one science 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 very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece of the 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 all these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the big 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 some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and and timing. And I think one thing people miss, too, is 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 we think it’s so important to incrementally. And so I think that what we kind of see right now, if you look this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, 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. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked about is basically going take it on that journey of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think that the OpenAI approach here 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 experts X is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to push the limits of this technology to really see it 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 a controversial stance you’ve taken, that the right way to this is to put it out there in public and then 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, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and so are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. someone else does. But for me, that was 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 time give input. You do have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s box 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 beautiful gifts your family and to everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the 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 away and people get more time to get right, which do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. brother was in the military at the time and like, he puts life on the line in a much more real way than of 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 field as it truly lies. Like, if you look at the whole history of computing, I really mean when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all of these things, they 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 connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other sort 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 smooth over time. And so 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 to have that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to 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 this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)