We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, and others, so many 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 how we feel. Above all, feels like we’re entering an historic period right now where we a world are going to define a technology that will be so important for our going forward. And I believe that we can manage this for good.
So today, I want to you the current state of that technology and some the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a tool for an AI than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a 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 details for you that you get out of ChatGPT. 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 — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a just like a human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet 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 you see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to of take away all those details from you. So don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you can click through it and sort of modify the 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. now we have a tweet that’s been drafted for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the if we want to. And so after this talk, will 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 AI how use them. Like, what do we even want it to when we ask these very high-level questions? And to this, we use an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human teacher who provides and punishments as it tries things out and does things are either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And the course of a couple of months we were able teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing is inspecting the floor, 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. And same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale ability to supervise the machine as time goes on. let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models not 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 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 a browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as 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 post. And all of this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any of this whole 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. And thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an 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 really shows the shape something that we should expect to be much more in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And we’re able to actually 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 impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name the 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 has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of the here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here some 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 rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything 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. I mean … suspect that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every 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 rethink the that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, 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 reality as it lays. And that we just thought hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I we’re going to need it, it’s a 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, that at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person working on training a model to predict the next in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a review was positive or negative. mean, today we 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 underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for how to do it. the really interesting thing is actually, if you have it add like 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 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 hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 smaller. And so there’s something 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, emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly 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 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. And so I that what we kind of see right now, if look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. 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, we move on to book summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned 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 system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the feedback that you talked about is basically going to take on that journey of actually getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable was 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 the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I has 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 supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating 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 box on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. But there’s actually also one 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, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking over 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 years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have it be five years away. if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts life on the line in a much more real way than of us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, really mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing take 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 in humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed 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 you … the model you want us to have that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively teach it be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And think it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)