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