We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s really gratifying hear 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 feel both those emotions at once. And honestly, that’s we 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 be so important for our society going forward. And believe that we 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 thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll out, this is all 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, example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like use that information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details from you. So you don’t to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that 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 think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there 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 be able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s about teaching the 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 you go to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in 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 math problem, to say comes next, that green nine up there, is to solve 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. have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to 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 plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should feedback.” And so 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 is the floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And let 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 human feedback. And 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. But can 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 given the AI a new tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, to be in this manager’s 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. it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s 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 humans and machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a 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 it you.” The only information here is the name of the file, the column names like you 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 to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I want 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 it. But I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even 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, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred 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 is writing code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A of how we may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I 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. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re to rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we thought really hard about like: What is it going take to make progress here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing has been to get teams people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there 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? think that in the early days, we didn’t know. We a lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this explain 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 feels impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can 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 are 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 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 pieces of text. it is learning things that you didn’t know that it going to 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 predicting some of emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the that 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 answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this step step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort like 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 to — we’re never going to know that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true any one moment, but that the expansion of the scale and the feedback that you talked about is basically going to it on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this is 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 been right yet. might be right maybe 70 years plus one or like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells you then, oh, here’s how we can on to a new 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 to do this is to it out there in public and then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, you know, somehow held 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 the opposite. That your 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 of their criticisms have been, you 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 time. Like, seriously 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 were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is only other path that I see, which is that you let reality hit you in the face. And I think you do people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family 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 could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely 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 that shortly after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets be 500 years away and people get more time to get it right, which do you pick? And know, I just really felt it in the moment. was like, of course you do the 500 years. My brother in the military at the time and like, he puts his life on line in a much more real way than any of typing things in computers and developing this technology at time. And so, yeah, I’m really sold 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 just almost like a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. And so 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 want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)