We started OpenAI seven ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where as a world are going to define a technology will be so important for our society going forward. And I believe that can manage this for good.
So today, I want show you the current state of that technology and of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very detailed spread. 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 an image. And that is something that really expands the power of it can do on your behalf in terms of out your intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can “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 ChatGPT users, over months. And you can look under the hood and that what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s a great experience within an as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able to sort of take away those details from you. So you don’t have to be one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is 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 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 cut back to slides. Now, the important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use an 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. could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to 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 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 try out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. 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 the we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when do 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. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI is 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. And let me show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two 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 time we provide some feedback. But we can actually the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue queries and click into web pages. And it actually 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. then it finds the publication date and the search results. It then is issuing another search query. It’s to click into the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI to become more useful a human. And I think this really shows the shape something that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able 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 ago VisiCalc. I don’t think they’ve really changed that much in time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw and then actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its 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,” like of that, that’s work for a human to do, and the AI is to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer I might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here go, a nice bell curve. You see that three is kind the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on an and 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 see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so 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 that, but it know what I want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of we may end up using this technology in the future. A brought his very sick 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 had he listened. In meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to an outcome 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 into our world.
And thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of the we released 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. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink the way that 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 first question actually is just how the hell have done 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 is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just 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 that didn’t work, you only see the things that did. And I think that the most important thing been to get teams of people who are very different from each other to work together harmoniously.
CA: we have the water, by the 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 that saw something in these language models that meant that if you continue invest in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, know, the model 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 you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how to do it. the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary 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 learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece the stack engineered properly, and then you can start 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 predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge that can maybe 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, I think all of these questions 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 too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at this talk, a lot of what I focus on is providing 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 correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. have to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s real 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 and so forth. Is it your belief, Greg, that 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 on journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I 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 going to happen, Y is how works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, you build in secret, get this super powerful thing, and then you figure the safety of it and then you push “go,” and hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in face. And I think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to 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 thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, you could choose for basically that Pandora’s box to five 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 have it be 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 was the military at the time and like, he puts life on the line in a much more real way than any of us things in computers and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing field as 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 just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this 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, figure how to provide the feedback, decide what 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 otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)