We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. 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 we can manage this for good.
So today, I want to you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather than it 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 things like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is all 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 at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able sort of take away all those details from you. you don’t have to be the one who spells out 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 list while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, 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. now we have a tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, what do we want it to do when we ask these very high-level questions? to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised 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 skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it to generalize. It it 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, the things we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals and 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 the course of a of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” And when 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, providing high-quality is a hard thing. If you think about asking kid to clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff 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 our to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can actually use 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 browsing tool where the model can search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going to 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 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 be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns out two months wrong. Two months and one week, that was 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 this many-step collaboration between human and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI become more useful to a human. And I think this really the shape of something that we should expect to be more common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, we get this process right, we will be able to 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 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 of them. And you can there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then make this nice plot 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 going on there? By the way, all this is 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 this 2023 thing. It makes this year look really bad. Of course, the problem is that the is 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 even by April 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the 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 the title. I didn’t for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how I think we … A of how we may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think 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 for deciding how we 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 thing to take away from 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 a very number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell you done 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 technology 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 look the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the things did. And I think that the most important thing has been to teams of people who 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 going to it, it’s a dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model to the next character in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of semantics 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: So 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 of them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just pop that just blew your mind that you just did 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 will do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have something general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an number of pieces of text. And it is learning that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell 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 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 still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see now, if you look at this talk, a lot of what 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, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise task properly. We have to build up a track record with these that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things like and wisdom and so forth, with a high degree of confidence. Can you be 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 I think that OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the field of broken promises, of these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something that is what you need. But I think that approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells 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 a controversial stance you’ve taken, that the right way to do this is put it out there in public and then harness all this, know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly evil thing with AI. And you going 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. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is only other path that I see, which is that you do let reality hit you in the face. I think you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your 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. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My was in the military at the time and like, he puts 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 field as it truly lies. Like, you look at the whole history of computing, I really mean it when I say that 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 the algorithms, all of these things, they are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and our minds.
(Applause)