We OpenAI seven years ago because we felt like something really interesting was happening AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world going to define a technology that will be so for our society going forward. And I believe that we manage this for good.
So today, I want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of what it can on your behalf in terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry 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 tools 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 you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience 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 to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to of take away all those details from you. So you don’t have to the one who spells out every single sort of little of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also a 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 the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even 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 program an 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 provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. 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 it with sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI to produce that answer. And this 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, that hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, 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 bad math in there, it will happily pretend that one one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it writes out its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go and easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me this 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 another to become more useful to a human. And I think this shows the shape of something that we should expect be much more common in the future, where we have and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, 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 over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT on 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 a data scientist would. And so you can just literally a file and 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 for you.” The only information here is the name of the file, the column like you saw and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are 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 do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I 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. a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, it does 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 cut back to the slide again. slide shows a parable of how I think we … A vision of we may end up using this technology in the future. A person brought his very 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. the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we consider how integrate these systems into our world.
And one 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 for the rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on 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. But I think within OpenAI, made a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. that we just thought really hard about like: What is it going to take to make here? We tried a lot of things that didn’t work, you only see the things that did. And I that the most important thing has been to get teams of who are very different from each other to work together harmoniously.
CA: we have 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 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 emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if review was positive or negative. I mean, today we are just like, come on, anyone can do that. this was the first time that you saw this emergence, this sort of semantics that emerged from this 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 things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you saw just something 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 for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so 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 learned something general, that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, and it’s 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 engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one 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 in some level confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that we kind of see right now, if you look this talk, a lot of what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look 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 do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take step by step. And that we say, OK, as we move on to book summaries, we have supervise this 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 to have to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see it action, because that tells you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable 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 the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some their criticisms 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 what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and then you push “go,” and hope you got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. And I think 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 people time to 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, we really were afraid that the number one people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but 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. You that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it a moment, if you could choose for basically 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 be 500 years and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course you the 500 years. My brother was in the military the time and like, he puts his life on the line in a much more real than any of us typing things in computers and this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, really mean it when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s been quite smooth 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 us to have is that we have birthed this child that may have superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)