We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to hear from people like 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 who are concerned, we from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering historic period right now where we as a world are going to define a that will be so important for our society going forward. I believe that we can manage this for 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 you is what it’s like to build a tool for an AI rather than building for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on your in terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going 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 “save this for later.” And the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was a prompt just like a human could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information and to with other applications too. You can say, “Now make a shopping list for 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, I definitely to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, the is able 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 what’s supposed to happen.
And as I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping 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, still can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, way to build them. And now we have a that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do second 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 rates them, says “This one’s better than that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals and run with it.” So we had to collect some feedback data. Sal Khan was very 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 teach 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 the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that really listen to our users and make sure we’re something that’s more useful 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 doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise the as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where the model can 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 going to search for this and it actually the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very verify any piece of this 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. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and 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 shows the shape of something that we expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in how they into 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 to create even more trustworthy machines. And I think that over time, if we this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I we’re going 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 with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value 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 from it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the thing 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 nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that 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 machine here. I really wanted it to notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, it updates 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 how I think we … A vision of how may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made a bad call 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, to GPT-4, which said, “I not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second vet who used to save 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 medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits 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 a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. Am 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 is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, so only see the things that did. And I think the most important thing has been to get teams of people who are very different from each other work together harmoniously.
CA: Can 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 there also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person was on training a model to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today are just 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 syntactic 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 prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that just your mind that you just 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 plus a 35-digit number, it’ll get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of of text. And it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be 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 these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, even 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 can maybe predict in some level of confidence, 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 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 reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if you look at talk, a lot of what I focus on is providing 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 correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing will be that we take step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually out our intent. And I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the and the human feedback that you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I 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 how works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to this is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And were going to build models that sort 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, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit 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, and you figure out the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which that you do let reality hit you in the face. I think you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the ability 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 with 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 something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent in 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. think you don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to 500 years away and people get more time to get it right, do you pick? And you know, I just really felt it the moment. I was like, of course you do 500 years. My brother was in the military at time and like, he puts his life on the line in a much more real way any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s quite smooth over time. And so 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 it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important that we all do get literate in this technology, out 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 this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)