We OpenAI seven years ago because we felt like something really interesting was happening in 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. it’s really gratifying to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we a world are going to define a technology that will so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, 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 ChatGPT doesn’t just generate images in case — sorry, it 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 all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting just 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. you get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look under the hood and see that what it actually was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s for 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 was earlier.” And make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to of take away all those details from you. So you don’t have to the one who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at the Instacart shopping 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 really interesting is that traditional UI is still very valuable, right? If you look at this, you 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 build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we 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 high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually 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 us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal himself 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 to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make sure we’re something 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, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we 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 actually given AI a new tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It then it the publication date and the search results. It then is another search query. It’s going to click into the blog post. And of this you could do, but 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 manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll back to 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 human, using this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think this really shows the of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in 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. you can see there the data right here. But let me show you the take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, just a data 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, is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw and the actual data. And from that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, 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 to 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 the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three is kind of most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates 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 of how I we … A vision of how we may end using this technology in 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 dog 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 talk to a professional, here are some hypotheses.” He brought 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. this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way 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 guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, you only see the things that did. And I that the most important thing has been to get teams people who are very different from each other to work 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 something also just about the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, today we are 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 syntactic process. And there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as 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 did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these 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 to our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting 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 or 1,000 times smaller. so there’s something about this that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, if you look this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can 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 summarizing book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will be that we this step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able to actually carry out our intent. And I 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 in this session, are critics who say that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But I think that approach has always been, you’ve got to push to the of this technology to really see it in action, because tells 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 in public and harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about 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 were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so I think 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, these machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but 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. believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this could 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, is that shortly after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for 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 hand you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just really it in the moment. I was like, of course you do 500 years. My brother was in the military at the and like, he puts his life on the line in a much more real way any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I 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 of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means that someone does, or the moment that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s 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 to 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 superpowers take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today that we all do literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)