We started OpenAI seven ago because we felt like something really interesting was happening AI and we wanted to help steer it in a 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, for many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a that will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to you the current state of that technology and some of underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool for an AI than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But 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 power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely 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 are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s great experience within an app as long as you of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is to sort of take away all those details from you. you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes 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 list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And after 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 this, it’s not just about building these tools. It’s about 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 an old idea. If you go back to Alan Turing’s 1950 paper on 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 rewards and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. For example, if you’re shown a 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 AI what to do those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this 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, it will happily pretend that plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them to stuff all toys in the closet. This is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to supervise the as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just to search for this and it actually does the search. It it finds the publication date and the search results. It then is another search query. It’s going to click into the 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 in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come 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 so interesting to me 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 it in order to produce data for another AI to become more useful to a human. I think this really shows the shape of something we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to actually create even more 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 to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data scientist would. And so you 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 is the name of the file, the column names you saw 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 that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer what I be interested in. And so it comes up with some ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things 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 push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to a fair 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 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. But I inject my intent, provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it what I want.
Now we’ll cut 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 brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to to 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 overly rely on them. But story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits 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, much every single thing 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 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 actually just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees 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 building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think 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 hard about like: What it going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I think that the important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And think 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 to do it? I think in the early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, expect, you 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 tell you if review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a 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 happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few 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 one moment you when you saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means 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 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 it to have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at incredible number of pieces of text. And it is things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and 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 see all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And so think that what we 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 we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able to carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to 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 one moment, but that the expansion of the scale and the human feedback that you about is basically going to take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think that the OpenAI approach here has been just like, let reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts X is going to 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 that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in and then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there the great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which is that do let reality hit you in the face. And think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, would you pick, right? On 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 500 years away and people get more time to get it right, which do you pick? you know, I just really felt it in the moment. 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 more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, I really mean it when 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 are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that 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 safety precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is that basically the model?
GB: 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 encounter it. And I think it’s important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)