We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond 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 concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to a technology that will be so important for our society forward. And I believe that we can manage this for good.
So today, I want to show the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app 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 that you get out of ChatGPT. And here we go, it’s not just 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 in this — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This all generated by the AI as we speak. So 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 ChatGPT other tools too, for example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I 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 of take away all those details from you. So you don’t to be the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s 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 that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s been drafted for review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the 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 the slides. Now, the important thing 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? And to this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and things that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach the what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to 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 will happily pretend that plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of we were able to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is 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 to all the toys in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the 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 to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where the can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It it finds the publication date and the search results. then is issuing another search query. It’s going to click into the blog post. And all of 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 where you can, if you want, triple-check the work. And out come citations so can actually go and very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that 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 many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And I this really shows the shape of something that we expect to be much more common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we this process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names like you and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the kind of has to infer what I might be interested in. so it comes up with some good ideas, I think. So a of the number of authors per paper, time series papers per year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to 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 I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think we … A vision of we may end up using this technology in the future. A person his very sick dog to the vet, and the made 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 provided blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for how we want it to slot in, that’s for the rules of the road, for what an AI and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from anything had anticipated. And 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 OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the 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 my first question actually just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with 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 progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And we just thought really hard about like: What is it going 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 most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these language that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew was what we wanted to be, was a deep lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a of things, and one person was working on training a model to the next character in Amazon reviews, and he got result 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 got a state-of-the-art sentiment analysis classifier out of it. This model tell 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 to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just did 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 will do it, which means it’s learned an internal circuit for how to do it. the really interesting thing is actually, if you have 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 learning the process, but it hasn’t 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 to have learned something general, but that 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 is 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 was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is very in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that important thing will be that we take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually carry out intent. And I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the 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 one moment, but that the expansion of the scale and human feedback that you talked about is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to put out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, 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 the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, 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, sent such through the tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done responsible here and not 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 it right. But one thing I think has been important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I 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 time to give input. You do 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, try to 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 for 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 chance it’s something 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 small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the one hand you’re like, well, 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 time to 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 the military at the time and like, he his life on the line in a much more real way than any us typing things in computers and developing this technology at 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 as it truly lies. Like, if you look at the whole history of computing, I really mean 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 pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, 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 … the model you us to have is that we have birthed this child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I 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. And think it’s incredibly important today that we all do get literate in this technology, out 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 so good we’re honestly this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)