We started seven years ago because we felt like something really was happening 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 come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic right now where we as a world are going to a technology that will be so important for our going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do 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 creative back-and-forth and taking care the details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to 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.” the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all 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. And so you sort have this ability to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty thing I was 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, wonderful meal, I want to know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste them, and usually 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 all those details from you. So you don’t have be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you can 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 have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s teaching 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 use an 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. You could build machine, like a human child, and then teach it through feedback. Have a human teacher who provides and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to 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, “This one’s better than that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it 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 the things we have to teach AI are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they 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. If there’s some bad math in there, it will 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 offered 20 of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area of weakness where 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 more for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search this and it actually does the search. It then finds the publication date and the search results. It then is issuing 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 that humans really want to do. It’s much more fun to be in 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 go and very easily verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to a human. I think this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very and delicately designed in how they fit into a problem and how want to solve that problem. We make sure that the humans are 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 create even 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 going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT access to another 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 file and ask questions about it. And very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, column names like you saw and then the actual data. And from that it’s able to infer what 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 what these things are and that these are integer 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 is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And AI kind of has to infer what I might be in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud of 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. You see that three is of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we may end using this technology in the future. A person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought 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. But story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to 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 my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just confront reality as it lays. And that we just really hard about like: What is it going to to make progress here? We tried a lot of things 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 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 there also just about the fact that you saw something in these language models that that if you continue to invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We a lot of things, and one person was working on training model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that that could come from 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 different things emerge. It happens the time, ant 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 few 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 mind that you just did not see coming.
GB: Yeah, well, so you can 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 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 often get wrong. And so you can see that it’s really learning the process, 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 universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a 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 look at that 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 know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the 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 be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think that our approach has been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do 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, bad things are to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through 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 without proper guardrails we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, 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 one thing I think 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, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path that I see, is that you do let reality hit you in 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 seen from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So spam 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 a box on the table. You believe in that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your 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. think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to be 500 years and people get more time to get it right, which do pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on the line in a much more way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. 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 algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that 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 to one, sort of, change in what humans could do. I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have is we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to be 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 each as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)