We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying hear from 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 emotions once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world going to define a technology that will be so important for society going forward. And I believe 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 build a tool for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a 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 for you that you get of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something 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 going to see. 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 they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is coming 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 you sort of this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out for all TED 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 tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we 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 as long as you of know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the shopping list while we’re at it. And you can see sent a list of 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 it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we 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 able inspect, we’re able to change the work of the AI we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually the math problem.
But we actually have to do a step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t 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 Khan Academy, they said, “Wow, this is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a 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. the same sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model 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 we can use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This one 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. says, I’m just going to search for this and it actually does the search. It then finds the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. And all of this could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to human. And I think this really shows the shape of something that should expect to be much more common in the future, where we have humans and kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. make sure 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 able actually create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in 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 all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a data scientist would. And so can just 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 the name the file, the column names like you saw and then the actual data. And from that it’s to infer what these 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, is a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, and 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 make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer I might be interested in. And so it comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that the year not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you want 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 cut back to the slide again. This shows a parable of how I think we … A vision of how may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not be here today had listened. In the meanwhile, he 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 that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have to literate. And 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. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single 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 way that we 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 this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What is going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. I think that the most important thing has been get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you something in these language models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can that. But this was the first time that you 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 to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an circuit for how to do it. And the really thing is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can see that it’s 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 in the universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and at an incredible 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 that we’re starting to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able 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 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, arises from this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also this incredibly emergent, sort of, very thing 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 lot what I focus on is providing really high-quality feedback. Today, the tasks 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 the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the is going to always — we’re never going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is we’re headed. And 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 the field of broken promises, all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it there 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 story I heard on OpenAI when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it 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 super powerful thing, then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s box 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 beautiful to your family and to everyone. But there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a time. And you think about it for a moment, if could choose for basically 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, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 away and people get more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course you the 500 years. My brother was in the military at the time and like, he puts life on the line in a much more real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the that you sort 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 happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions you get. And so I that one 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 over time. And the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we 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 we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)