We started OpenAI seven years ago we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where as a world are going to define a technology that will be so 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 first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get 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 in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by AI as we speak. So I actually don’t even know what we’re going 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.” And the interesting about these 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 users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT is selecting all these different tools me having 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, we so used to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as as you kind of know 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 sort of take away all those details from you. you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still very valuable, right? If you look this, you still can click through it and sort of modify the actual quantities. And that’s something that think shows that 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 the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the how to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to solve the math problem.
But we actually have to a second step, too, which is to teach the AI to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re 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 that 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 to provide feedback to the machine alongside our team. over the course of a couple of months we were to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to the this way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching them 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. we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every 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, this case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as does it. It says, I’m just going to search this and it actually does the search. It then it finds the date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much more fun be 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 you can actually go and very easily verify any of this 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 back to side. And so thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And I think this really shows 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 fit into a problem and how we want to solve that problem. We sure that the humans 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 to actually even more trustworthy machines. And I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve 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 is a specific of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, it knows the name of file and 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, column names like you saw and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer I might be interested in. And so it comes with some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word 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 we go, a nice bell curve. see that three 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. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the year not over. So I’m going to push back on the machine. [Waitttt that’s 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? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit an overreach for it to have sort 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 very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a 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 say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate these systems into 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 what AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just looks different. Just different from 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 intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think 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 is it to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I think the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have 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 just about the fact that you something in these language models that meant that if you to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the model 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 could tell you if a review positive or negative. I mean, today we are just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw just pop that just blew your mind that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition 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 adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know 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 emergent capabilities. And to do that actually, one of things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves 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 are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track with these machines that they’re able to actually carry our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale and human feedback that you talked about is basically going take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. you 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. And think that the OpenAI approach 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 to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it there in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, 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 scrambling to catch up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I think do give people time to give input. You do have, before these are 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 with it 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 that are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that 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, all people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do 500 years. My brother was in the military at the and like, he puts his life on the line in a much real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that if 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, you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change in what humans do. But I actually think 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 increasing it.
CA: what I’m hearing is that you … the model 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 our collective to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that that will continue to be the path, but it’s so good 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 TED and blowing our minds.
(Applause)