We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for so many wonderful things. We from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering historic period right now where we as a world going to define a technology that will be so for our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated by AI 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 too, for example, memory. You can say “save this later.” And 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, all ChatGPT users, upcoming months. And you can look under the hood and see that what it actually was write a prompt just like a human could. 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 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 the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of little of what’s supposed to happen.
And as I said, 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 can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look this, you still can click through it and sort of the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if we want to. so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it to do when we these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this 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 green nine up there, to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And 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, to sort of your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes 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 wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s way that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, the way. And 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 provide even better feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m just going to search this and it actually does the search. It then finds the publication date and the search results. It then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really to do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful a human. And I think this really shows the of something that we should expect to be much more common in the future, where have humans and machines kind of very carefully and delicately designed in how they fit into a and how we want to solve that problem. We make that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of 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 that time. And here is a specific spreadsheet of the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, is 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 number of authors in paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction with lots intent behind it. But I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes 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 pretty interesting to see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped the cliff. What could be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide 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 very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I 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 veterinarian made a bad to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, I think, shows that a human a 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 we should all reflect on, think as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who 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 question actually just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why 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 you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What it going to take to make progress here? We a lot of things that didn’t work, so you only see things that did. And I think that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think that high level, deep learning, we always knew 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 in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when 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 me one moment you when you saw just something pop that just blew 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 it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 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 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 numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has 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. There all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. And so there’s 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 this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually out our intent. And I think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of scale and the human feedback that you talked about is basically to take it on that journey of actually getting to like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see it in action, that tells 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 way to do this is to put it out there public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper 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 think these questions all the time. Like, seriously all the time. And 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 about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this powerful 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 that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really 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 Viagra 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 a on the table. You believe that in that box is 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 actually also a one thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s to be five 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 it be five years away. But if it gets to be 500 years away people get more time to get it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, puts his life on the line in a much real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean when I say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think if you look at capability, it’s been quite smooth 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 that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)