We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so important for society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you 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 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 these tools, which allows to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort 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 is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do even want it to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then it through feedback. Have a human teacher who provides and punishments as it tries 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 a 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 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 math problem.
But we actually have do a second step, too, which is to teach the AI what do 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 than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, you really 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 you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to us provide even better feedback and to scale our to supervise the machine as time goes on. And let show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the 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 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 a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click the 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 this manager’s position you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for AI to become 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 a problem and how want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been 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 for past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names like you saw and the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI is to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI of has to infer what I might be interested in. And it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is over. So 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?] April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI just writing code 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, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut back 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 very sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought information to a second vet who used it to the 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 professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people anticipated. And so we all have to 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. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the 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 actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we thought really hard about like: What is it going take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I think the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can have 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 also just about the fact that you saw something in these 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 the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could 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 you saw 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 that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. 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 time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when you saw just pop that just blew your mind that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. the really 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 see that it’s really 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 it had to learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I think is very in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I think 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 to supervise task properly. We have to build up a track record with these machines that they’re able to carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have 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 take it that journey of actually getting to things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing with AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. You know, how do you, like, make case 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 think has been incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and you push “go,” and you hope 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 right. And so I think that this alternative approach is the only path that I see, which is that you do let reality hit you in face. And I think you do give people time to input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a 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 very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And know, I just really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts his life on line in a much more real way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us have is that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having 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)