We started OpenAI 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 honestly just amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are to define a technology that will be so important for society going forward. And I believe that we can manage this good.
So today, I want to show you the current state that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what it can do your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. is all generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
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I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is these tools, which 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 to with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t to be the one who spells out every single sort little piece 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 take a look the Instacart shopping list while we’re at it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at 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 we a new, augmented way to build them. And now we have a that’s been 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 work of the AI if we want to. And so this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
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So we’ll cut back the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how to 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 build a machine, a human child, and then teach it through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are 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 machine through an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, to actually solve the math problem.
But we actually to do a second step, too, which is to teach the 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 a human 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 to produce that answer. And allows it to generalize. It allows it to teach, to sort infer 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 what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one three and run with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to the machine our team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots 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 an area of weakness where should gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback and to our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually 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 is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole of thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become useful to a human. And I think this really the shape of something that we should expect to much more common in the future, where we have and machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that 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 to actually create even more trustworthy machines. And I that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, like a data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information is the name 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, semantic information wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv 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 of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer what I might interested in. And so it comes up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice bell curve. You see that three is of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, 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 back the slide again. This slide shows a parable of how 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 bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In 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 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 a professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at that and you think, “Oh 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 that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot things that didn’t work, so you only see the things that did. And think that the most important thing has been to teams of people who 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 to it, it’s a dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person working on training a model to predict the next character Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we 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 got to scale thing, you’ve got to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. 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 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. one science that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are questions of degree and scale 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 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 of what I on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math problem be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book 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 take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of scale and the human feedback that you talked about basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our has always been, you’ve got to push to the limits of technology to really see it in action, because that tells 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 a stance you’ve taken, that the right way to do this is put it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so are all scrambling to catch up. And some of their have been, you are forcing us to put this out without proper guardrails or we die. You know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the 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, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out safety of it and then you push “go,” and you hope you got 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 that this alternative 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. do have, before these machines 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 were that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, number one 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. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away 500 years 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, just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at the time like, he puts his life on the line in a much more real way than any of us things in computers and developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole 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 together the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do 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 we have birthed this extraordinary child that may have that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And I it’s also 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 we all do get literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that that continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)