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