We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to how far this 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 wonderful things. We hear from who are excited, we 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, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so for our society going forward. And I believe that can manage this for good.
So today, I want to you the current state of that technology and some of underlying design principles that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the AI as we speak. So I actually don’t even know 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 too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the 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 actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out all the TED 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 all these different tools without me having to tell it explicitly which ones to use in situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as long as you of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all details from you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s been for our review, which 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 change the work of the 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 the slides. Now, the important about how we build this, it’s not just about building these tools. It’s about teaching the AI 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 old idea. If you go 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, a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and 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 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 is of like sending up a bat signal to our team to say, “Here’s 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 hard thing. you think 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 stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is true? Like, these models are not 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 for me.
Now, in case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going search for this and it actually does the search. then it finds the 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 could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much more to be in the driver’s seat, to be in 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 turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about this 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 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 and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv the past 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 data like this.
So we can give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And very helpfully, you know, it knows name of 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 file, the column names like you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. 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 good ideas, I think. So a histogram of the number of 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 do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that 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 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 to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it does the 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 how we may end up using this technology in the future. person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In 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 a professional, 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 rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for the rules of the road, for what an AI will and won’t do. And 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 we all have to become literate. And that’s, honestly, one of the 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 within every 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 way I work, I 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, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with this technology 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, the progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I that the most important thing has been to get teams people who are very different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought 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 language that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. I 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 in the early 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, and he a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just pop that just blew your mind that you just 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 circuit for how to do it. And the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you can see it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of of text. And it is learning things 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 we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think is very undersung in this is sort of 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 machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just 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, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very to look at that math 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? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts X is going to happen, Y is how it works. People have been neural nets 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 what you need. But think that our approach has always been, you’ve got to push to the limits 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 quite a controversial stance you’ve taken, that the right to do this is to put it out there in and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, before they super powerful, that you actually 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 thing people were going to do with it was generate misinformation, to tip 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 sitting in room, there’s a box on the table. You believe that that box is something that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts his life on line in a much more real way than any of us typing things in 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 that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide even just almost like a 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 these things, they happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change what humans 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 technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out 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 extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be and not to tear us all down. Is that 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 step as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that continue to be the best path, but it’s so good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)