We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a 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 Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from who 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 be so important for our society going forward. And I believe that we manage this for good.
So today, I want to show the current state of that technology and some of underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s not just the idea for the meal, 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 image. And that is something that really expands the power what it can do on your behalf in terms carrying out 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 know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was write a prompt like a human could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make it a little 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 it explicitly which ones to 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 between them, usually it’s a great experience within an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all details from you. So you don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. And now we 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 able to change work of the AI if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to use them. Like, what do we even it to do when we ask these very high-level questions? And to do this, we an old 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 a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to complete 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 to do with those skills. And this, we provide feedback. We have the AI try out multiple things, give us multiple suggestions, and 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 to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not 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 able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours 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 humans in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the 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 ability to supervise the as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and search results. It then is issuing another 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 in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s 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 another AI to become more useful to human. And I think this really shows the shape of something that we should expect to be much common in the future, where we have humans and kind of very carefully and delicately designed in how fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in a 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 process right, we will be able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And 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 on how to 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, like a data scientist would. And so you can just upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of file, the column names like you saw and then the 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, 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 in the paper,” like all of that, that’s work for human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You that three is kind 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 we were on an exponential and it dropped off 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 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 is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A of how we may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And dog 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, need to talk to a professional, here are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems 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 setting the rules of the road, for what an 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. 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 intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about way 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, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s up with this technology that shocked 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 those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really hard about like: What is going to 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 that the important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if you to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … just feels impossible that that could come from a 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, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if 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 to do it. the really interesting thing is actually, if you have it like a 40-digit number 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 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it 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 is predicting some of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we this step by step. And that we say, OK, we move on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, things are going 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 of check on the big companies doing their unknown, evil thing with AI. And you were going to build models that of, you know, somehow held them accountable and was capable of slowing field 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 the tech that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other that I see, which is that you do let reality you in the face. And I think you do give people time give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with it was misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but 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 is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in the 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 we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But it gets to be 500 years away and people get time to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you the 500 years. My brother was in the military at the time like, he puts his life on the line in a more real 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 this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the whole history of computing, I mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And 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 you don’t put together, you get an overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually that if you 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 each moment that you’re increasing it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)