We OpenAI seven years ago because we felt like something really interesting was in AI and we wanted to help steer it in 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 like Raymond who are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define technology that will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current state that technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it generates an image. And that is something that really the power of what it can do on your in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” And the thing about 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 coming you, all ChatGPT users, over upcoming months. And you can look under the hood see that what it actually did was write a prompt just like a could. And so you sort of have this ability inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, click between them, we copy/paste between 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 like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to sort of take away all those details you. So you don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build 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? to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually use the AI to fact-check. And 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 queries and click into web pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. then it finds the publication date and the search results. then is issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s this many-step collaboration between 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 human. And I think this really shows the shape of that we should expect to be much more common the future, where we have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And 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 to solve problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And very helpfully, you know, knows 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 saw and then the actual data. And from that it’s able 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 that people submit papers and therefore that’s what these things are and that these are 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 happy to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction with of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. of that, I 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 that is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could be going on 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. makes this year look really bad. Of course, the is 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 papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so 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 for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of we may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need 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 medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this 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, is that getting right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence all of 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, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to rethink the way 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 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 come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality as it lays. that we just thought really hard about like: What it 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 most important thing has been to get teams of people who are very 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 fact you saw something in these language models that meant if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that 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 and cultural 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, model will do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll often 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 to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look an incredible number of pieces of 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. So science that we’re starting to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very in this field is sort of engineering quality. Like, 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 get every single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see all of these in there. And 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 that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you can predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, think all of 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 this emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re going hear later 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 to know that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that talked about is basically going to take it on that journey 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, think that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the field of broken promises, of these experts saying X is 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 right yet. They be right maybe 70 years plus one or something that is what you need. But I think that our approach always been, you’ve got 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 on to a paradigm. And 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 is to put it out there in public and then all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly 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 I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some of their have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out safety of it and then you push “go,” and you hope got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. And I you do give people time to give input. You have, before these machines are perfect, before they are powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out over this wonderful water, these people having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother in the military at the time and like, he puts his life on line in a much more real way than any of typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is 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 making faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so think 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 zero 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 has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is that basically 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 we encounter it. And 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 good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)