We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that be so important for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of technology and some of the underlying design principles that we dear.
So the first thing I’m going to show you is what it’s like to a tool for an AI rather than building it a human. So we have a new DALL-E model, which images, and we 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 and draw a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But 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 by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
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I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just a human could. And so you sort of have ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all TED 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 use in any situation. this, I think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of take away all those from you. So you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected happen 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. And the thing that’s really 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 build them. And now we have a tweet that’s been for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And so this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these very high-level questions? And 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 answer this. Instead, you can learn it. You could build a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do a second step, too, which is to the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when 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 some bad in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard 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 to all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even better feedback to 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 on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain thought 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 and the search results. then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s 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. And out come so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns out two months was wrong. Two months one 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 is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to a human. I think this really shows the shape of something that 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 the are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And here is a specific 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 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 interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the names like you saw and then the 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 that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work a human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors 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 that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of 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, this is the of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second vet who used 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 human with a medical professional with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for setting 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 we all have to literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a 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 single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I 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 that most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict the next character Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just not 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 model will it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll get 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 table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to get good at is predicting some of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still 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, emerge that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy 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 thing to supervise. Like, how you know if this book summary is any good? You have to read the whole book. No one 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, we move on to book summaries, we have to this task properly. We have to build up a record with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion the scale and the human feedback that you talked is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these saying X is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 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 of technology to really see it in action, because that you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way to this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is now giving feedback. But … If, know, bad 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, well you there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one I 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 to do that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do give time to give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the 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. you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing in the small print there 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 it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to be 500 years and people get more time to get it right, which do you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look the whole history of computing, I really mean it I say that this is an industry-wide or even just 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 making computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even you think about 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 think that if look at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)