We OpenAI seven years ago because we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s 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, we hear people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I to show you the current state of that technology and of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for to use on your behalf. And you can do things like ask, you know, a nice post-TED meal and 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 you that you get out of ChatGPT. And here we go, it’s just the idea 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 an image. that is something that really expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do this 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 any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this 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 be 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 sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to 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 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 answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re shown 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 do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer your 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 what you’d expect. 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. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like 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 building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. we move to 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 to 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 question this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the can issue search queries and click into web pages. it actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, 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 months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking is doing it in order to produce data for AI to become more useful to a human. And think this really shows the shape of something that we expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how fit into a problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able 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 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 some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name of 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 saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with of intent behind it. But I don’t even know what I want. And the AI kind of has infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number 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 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 of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off 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 things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little 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 hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of I think we … A vision of how we may up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. that’s for deciding how we 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 take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink the way that we 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 question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. 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 truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality as it lays. that we just thought really hard about like: What is it going take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important thing has been to get teams people who are very different from each other to together harmoniously.
CA: Can we 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 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, something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how do it? I think that in the early days, didn’t know. We tried a lot of things, and one was working on training a model to predict the character in Amazon reviews, and he got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, this sort of semantics 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 riddle baffles everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing of them 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 a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
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. the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an incredible number of pieces of text. it is learning things 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 some these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. 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 huge risk of truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, you can 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 to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I think the important thing will be that we take this by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. have to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re going have to produce 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 later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but that the of the scale and the human 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 be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field broken 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 work 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got to push the limits of this technology to really see it action, because that tells you then, oh, here’s how we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it out in public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going to build models that of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so 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, how you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and you push “go,” and you hope 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 that this alternative approach is the other path that I see, which is that you do let reality hit you in face. And I think you do give people time to input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment 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 to give gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. And you think it for a moment, if you could choose for that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, 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 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. My brother was in military at the time and like, he puts his life the line in a much more real way than of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like 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 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 thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed this child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)