We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we hear people who are 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 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 show you the current state of that technology and some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like build a tool for an AI rather than building for a human. So we have a new DALL-E model, which images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what can do on your behalf in terms of carrying 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 we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look under the hood and see what it actually did was write a prompt just like 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 information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it 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 these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as long you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is to sort of take away all those details from you. you don’t have to be the 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 at it. And you can see we sent a 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 that they’re going away, traditional UIs. It’s just we have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI 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 thing about how we build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as it tries things out and does that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child through an unsupervised learning 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 complete math problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually have to a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this 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 some bad in there, it will happily pretend that one plus one equals three run with it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over course of a couple of months we were able to teach AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it can actually check its 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 tool where the model can issue search queries and click into web pages. And it writes out its whole chain of thought as it it. It says, I’m just going to search for this and actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s to click into the blog post. And all of this you do, but it’s a very tedious task. It’s not thing that 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, you want, triple-check the work. And out come citations so can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful to a human. And I think this shows the shape of something that we should expect to be more common in the future, where we have humans and kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI 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 take how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, like a data scientist would. And so you can literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of the file, the column names you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people submit 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 is happy to help with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be interested in. And so it up with some good ideas, I think. So a of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going push back on 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. Can you use that to make a projection? So we’ll see, this 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 for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how we may end up this technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” And the 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 need to to a professional, here are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we 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 one thing to take from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general 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 a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a lot of very 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 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 we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried lot of things, and one person was working on a model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that come from a prediction machine. Just the stuff you us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you 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 will do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often get 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 memorize the 40-digit table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to arbitrary numbers of 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 it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. 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 of the stack engineered properly, and you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s 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 as scale up, things emerge that you can maybe predict in 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 of these are 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 thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to 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 thing to supervise. Like, how do know if this book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I that the important thing will be that we take this by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And think we’re going 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 this session, there are critics who say that, know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true at any one moment, but that the of the scale and the human feedback that you about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think that approach has always been, you’ve got to push to the limits this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way to this is to put it out there in public and then all this, you know, instead of just your team 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 story I heard on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly evil with AI. And you were going to build models that of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” you hope you got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that you do reality hit you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on 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 you a story that 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 in the hotel room just looking out this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 away and people get more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, he puts life on the line in a much more real way than of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve to approach 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 it I say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all 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 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 you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also to say this 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 literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope is that that 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 coming to TED blowing our minds.
(Applause)