We started OpenAI years ago because we felt like something really interesting happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now where we as world are going to define a technology that will be important for our society going forward. And I believe that can manage this for good.
So today, I want to show you current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are exposing it as an app for to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, 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 meal, but a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save for later.” And 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 way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and 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, allows 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 for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of the menus and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language 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 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. But let’s a look at the Instacart shopping list while we’re at it. And you can see we sent a of ingredients 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 at this, you still can click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want to do when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and does that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We just show it the 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. For example, you’re shown a math problem, the only way to actually that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the AI what to 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 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 received feedback.
Now, sometimes the things we 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 great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one one equals three and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside team. And over the course of a couple of we were able to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, 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 AI itself is happy help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model 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 actually use the AI to fact-check. And can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and click into web pages. it actually writes out its whole chain of thought as it it. It says, I’m just going to search for and 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 click into the blog post. And all of this could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I this really shows the shape of something that we should to be much more common in the future, where we humans and machines kind of very carefully and delicately designed how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I that over time, if we get this process right, we be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around in 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 all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the 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 scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names like saw and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these are and that these are integer values and so it’s a number of authors in the paper,” like of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even know what want 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 intent 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. a histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something crazy is 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, you can inspect. And then we’ll see word cloud. 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 really bad. Of course, the is that the year is not over. So 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 posted by April 13?] So 13 was the cut-off date I believe. Can you 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 machine here. I wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we may up using this technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And the would not 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 to a professional, are some hypotheses.” He brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems 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 want it to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing to 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. 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 within mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s come up with this 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 OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront as it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been get teams of people who are very different from each other 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 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, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. But this was first time that you saw 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 see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that blew your mind that you just did 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 to 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 that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, 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 happened is that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, and it’s 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 quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re to be 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. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take step by step. And that we say, OK, as move on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re able to actually out our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the is 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 true any one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take it that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of broken promises, of these experts 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 right yet. They might be right maybe 70 years one or something like that is what you need. But I think that our has always been, you’ve got to push to the of this technology to really see it in action, because that 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 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, world 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 when you founded as a nonprofit, well you were there as the great sort of check the big 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 was capable of slowing the field down, if be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, and 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 else does. for me, that was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. I think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe that in that 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 also a one percent thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. But if gets to be 500 years away and people get 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. brother was in the military at the time and like, he puts his life on the 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 this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look the whole history of computing, I really mean it when say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change 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 every we’ve developed has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)