We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world are going to define a technology that will so important for our society going forward. And I believe that we can manage for good.
So today, I want to show you the current of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use your behalf. And you can 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 creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed 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 generates an image. that is something that really expands the power of what it can do on your behalf terms 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 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 the interesting thing these tools is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually was write a prompt just like a human could. And so you sort of this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out for all the 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 which ones to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind of know the menus know all the options. Yes, I would like you to. Yes, please. good to 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 who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you 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, UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which is also a very 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 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 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 to use them. Like, do we even want it to do when we ask very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it things out and does things that are either good 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, the internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not what you’d expect. 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 problem, it 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 to collect some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time 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 two foundational blogs on unsupervised and learning from human feedback. And the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every 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 for me.
Now, in case, I’ve actually given the AI a new tool. This one is a browsing tool where the model issue search queries 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 and it actually does the search. It then it the publication date and the search results. It then is issuing another 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 thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more useful to a human. And think this really shows the shape of something that we should expect to much more common in the future, where we have and machines kind of very carefully and delicately designed how they fit into a problem and how we want solve that problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I 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 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 been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access 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 ask questions about it. very helpfully, you 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 here is the of the file, the column names like you saw and then actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what I might interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, 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. It makes this look really bad. Of course, the problem is that 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 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is writing code again, so if you want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision how we may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would not be here today had 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.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number people viewing 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 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 is how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What is going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that if you continue to invest them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, 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 the next character Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the 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 tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more 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 these colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you you saw just something pop that just blew your mind you just did 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 do it, which means it’s really learned an internal for how to do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces 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. one science that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true 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 about intelligence. If you look at our GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can 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 all these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s generating 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 the human feedback that talked about is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to this is to put it out there in public then harness 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 founded a nonprofit, well you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were going to 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 now and Meta and so forth are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here 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 get it right. But one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then 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. Maybe 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 I see, which is that you let reality hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, before they are super powerful, you actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a that this 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 a story I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out 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 be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if gets to be 500 years away and people get more time to it right, which do you pick? And you know, I really felt it in the moment. I was like, course you do the 500 years. My brother was in the military the time and like, he puts his life on the line a much more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what humans could do. But I actually think 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 to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not to tear us all down. that basically the 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 encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)