We started OpenAI years ago because 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 this whole has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now where as a world are going to define a technology that will so important for our society going forward. And I believe we can manage this for good.
So today, I to show you the current state of that technology and some of the underlying design principles that we dear.
So the first thing I’m going to show you is what it’s like to build a for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t 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 it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” by the way, this is coming to you, all users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to with other applications too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take all those details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional UI is still valuable, right? If you look 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. now we have a tweet that’s been drafted for review, which is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we 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 on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things 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 whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what next, that green nine up there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what to do with skills. And for 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 better than that one.” And this not just the specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new 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 of thought as does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication and the search results. It then is issuing another query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting me 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 order to produce data 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 future, where we have humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here the name of the file, the column names like saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the AI kind of has infer what I 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 of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the 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 this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If 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 shows a parable of how I we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we how to integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe 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 feeling of reeling. Like, I suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. 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 a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And first 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 that didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models that meant that if you to invest in them and grow them, that something at some point 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 we always knew that what we wanted to be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot things, and one 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 model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this syntactic process. And 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 are as prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that just your mind that 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 is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize 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 number of of text. And it is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look 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, as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that we kind of see right now, if you look this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that problem 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 book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up track record with these machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there critics who say that, you know, there’s no real 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 belief, Greg, that it is true at any one moment, but that the expansion the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach always been, you’ve got to push to the limits this technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out in public and then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent 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 forcing us to put this out here without proper or we die. You know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of you do the 500 years. My brother was in military at the time and like, he puts his life on line in a much more real way than any of us typing in computers and developing this technology at the time. 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 truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more 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 these things, they are happening. And you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and not 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 step as we encounter it. I think it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)