We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going define a technology that will be so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying design principles that 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. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But 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 can do on your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as we speak. So actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And 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 under the hood and see that what it actually was write a prompt just like a human could. so you sort of have this ability to inspect the machine is using these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able to sort of take away those details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping list 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 the traditional UI still very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And 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 slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s 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 go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things are either good or bad.
And this is exactly 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 whole internet say, “Predict what comes next in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is 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 equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when you 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 feedback is hard thing. If you think about asking a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time 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 foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date and the search results. It is issuing another search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very 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 whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And I think this really shows the shape of something we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give 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 that much in that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a data scientist would. 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 file, the column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy to help 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 a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has to what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we 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 within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s new 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 guess my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? We tried lot of things that didn’t work, so you only see things that did. And I think that the most important thing has been to get teams of who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something in these models that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I 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 of them feels … it just feels that that could come from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, of the things I think is very undersung in field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of 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 look at some models that are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you know this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we this step 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 I think we’re going to have to produce better, more efficient, more reliable ways of 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 understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in face, right? It’s like this field is the field broken promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our approach has always been, you’ve got to push the limits of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And 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 to it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth all scrambling to 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 case that what you have done is here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you give people time to give input. You do have, before machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically that Pandora’s box to five years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 away and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the in a much more real way than any of us typing things in computers developing this technology at 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 field as it truly lies. Like, if you look 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 that there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put 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 thing, no one’s had any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing is that you … the model you want us have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)