We started OpenAI seven ago because we felt like something really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where as a world are going to define a technology will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” And 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 is coming you, all ChatGPT users, over upcoming months. And you can look under the hood and see that what actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is using these tools, 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 integrate with other applications too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But can see 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 of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of away all those details from you. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, will be able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and things that are either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole process the AI 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 scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you think about 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 same sort of reasoning to AI. As we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model issue search queries and click into web pages. And actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be in 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 piece of this whole 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 me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. I think this really shows the shape of something we should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a specific spreadsheet of the AI papers 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 how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. 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 it for you.” The information here is the name of the file, the column like you saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer what might be interested in. And so it comes up with good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So 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 the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by 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 there was more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if want to 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 ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how we end 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 the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see things that did. And I think that the most important thing has been to get teams people who are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these language that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time that 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 this helps the riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a 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 me moment for you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you 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 interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually 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 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 of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise this task properly. have to build up a track record with these machines that they’re able to actually out our 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 hear later in this session, there are critics who say that, you know, there’s no real understanding inside, 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, it is true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically going to it on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, 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 this field is the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really see in action, because that tells you then, oh, here’s how 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 way 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, bad things are to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google 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 or we die. know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which that you do let reality hit you in the face. And think you do give people time to give input. do have, before these machines are perfect, before they super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought 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 very strong 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 actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking out over 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 to be 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 have it be five years away. But if it gets be 500 years away and people get more time get it 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. My brother in the military at the time and like, he puts his life on the line in a much real way than any of us typing things in computers and developing this technology at the time. 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 truly lies. Like, you look at the whole history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that thing I take 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. I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)