We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where as a world are going to define a technology that will be important for our society going forward. And I believe that we manage this for good.
So today, I want to show you the state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an rather than building it for a human. So we a 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 like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation 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 just idea for the meal, but a very, very detailed spread. So let’s see 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 really expands the power of what it can do your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually was write a prompt just like a human could. And so you sort of have this ability to how the machine is using these tools, which allows 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 too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details from you. you don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still can click through it and of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to 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 this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process imbues it 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 actually the math problem.
But we actually have to do a step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the course a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and click web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going to search for this and it actually the search. It then 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 could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually and very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting 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 tool is doing it in to produce data for another AI to become more useful to human. And I think this really shows the shape something that we should expect to be much more common in the future, where we have humans and kind of very carefully and delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I think that time, if we get this process right, we will able to solve impossible problems.
And to give you a sense just how impossible 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 form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed 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 you the ChatGPT take on how to analyze a 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. And so can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here the name of the file, the column names like saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that are integer values and 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 to with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots intent behind it. But I don’t even know what want. And the AI kind of has to infer I might be interested in. And so it 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 interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it 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 code again, so if you want to what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even 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 a parable of how I think we … A vision how we may end up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional with ChatGPT as 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 systems into our world.
And one thing I believe deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that 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 about 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 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, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to 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 people are very different from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these models that meant that if you continue to invest them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and how to do it? I think that in the days, we didn’t know. We tried a lot of things, and person was working on training a model to predict the character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive negative. I mean, today we are just like, come on, can do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so can see 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 than there are in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And it is learning that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very undersung in this field is sort 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. You have to get single piece of the stack engineered properly, and then can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And 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 what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I 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 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 on providing really high-quality feedback. Today, the tasks 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 to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take step by step. And that we say, OK, as move on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned 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 going to know that it’s not errors, that it 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 the human feedback you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been 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 happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to push to the of this technology 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 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 giving feedback. … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I 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 were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to catch up. And some of their have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to 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 default of being, well, you build in secret, you get this super thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. 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 I that this alternative approach is the only other path I see, which is that you do let reality you in the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are 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 box is that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do it 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 I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you could choose for that Pandora’s box to be 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 to have it be five years away. But if gets to be 500 years away and people get more time to get it right, which you pick? And you know, I just really felt in the moment. I was like, of course you do 500 years. My brother was in the military at time and like, he puts his life on the line in a much more way than any of us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the 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 the algorithms, all of things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I it’s also important to 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 literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)