We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where as a world are going to define a technology that be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles 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 ChatGPT use on your behalf. And you can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
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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 these is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. you 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 ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use information and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see that ChatGPT is selecting these different tools without me having to 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 so used to thinking of, well, we have these apps, we between 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 all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is to sort of take away all 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 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 to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If look at this, you still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
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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 about teaching AI how 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 idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries things and 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 Turing would have called a machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, you’re shown a math problem, the 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 do with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, the whole process 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 scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is 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 some math in there, it will happily pretend that one one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we really to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback and to scale our to supervise the machine as time goes on. And me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this 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 queries and click web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to for this and it actually does the search. It then it finds 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 you could do, but it’s a very 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 in 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 it actually turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more useful to a human. I think this really shows the shape of something that we should expect to be more common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think over 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 rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file ask 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.” only information here is the name of the file, the column like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? 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. really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all 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, you look at that you think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that 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 thousands 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. you look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What it going to take to make progress here? We tried lot of things that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just 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 there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. this 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 to scale this thing, you’ve got to see 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 out of them feels … it just feels impossible 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 different emerge. It happens all the time, ant colonies, single ants run around, when you bring 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, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing 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 see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe 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, think 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 this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of see 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, can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing 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 think the important thing will be that we take this step by step. And that say, OK, as we 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. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re going to hear later in 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 that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true at any one moment, but that the of the scale and the human feedback that you talked is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let 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 yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial 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 is now 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 the great sort of check on the companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is that do let reality hit you in 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 ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a strong chance 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 thing in the small print 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, not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five 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 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My was in the military at the time and like, he his life on the line in a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still 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 does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s any time 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 development of other sort technologies, think about nuclear weapons, people talk about being a 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 over time. so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to tear us 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 to take each step as encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how provide the feedback, decide what we want from it. my hope is that that will continue to be the 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, you so much for coming to TED and blowing minds.
(Applause)