We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help 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 we are building, and others, for so many wonderful things. We 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 how we feel. Above all, it like we’re entering an historic period right now where we as a are going to define a technology that will be important for our society going forward. And I believe that can manage this for good.
So today, I want show you the current state of that technology and some of underlying design principles that we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So 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, over upcoming months. And you can under the hood and see that what it actually did was write prompt just like a human could. And so you sort have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI 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 shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able change the 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.
(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 to use them. Like, what do we even want to do when we ask these very high-level questions? And 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 this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as 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 child through an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve 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 math problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios it 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 Khan Academy, said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push 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, that is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, the 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 let me you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for and it actually does the search. It then it finds the date and the search results. It then is issuing search query. It’s going to click into the blog post. And of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much 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 out citations 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. so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order 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 the future, where we humans and machines kind of very carefully and delicately designed in how fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take 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 just upload a file and ask questions about it. And 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 the name of 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 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 therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI is happy to 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 again, this is a super high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot the papers per year. Something 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 code, you can inspect. And then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It this 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 not fair!!! 2023 isn’t over. 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 the kind of ambitious one.
(Laughter)
So know, again, I 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, inferred magically this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. now, it 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 to the slide again. This slide shows a parable of how I we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect that a very large of people 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 new possibilities there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of 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 reality as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the 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, brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models that meant that if you continue to in them and grow them, that something at some point emerge?
GB: Yes. And I 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 to 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 of things, and one person was working on training a model to the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the model 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 if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for 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 add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And the really thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see 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 learned that, Oh, I can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, then you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge of 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, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to supervise task properly. We have to build up a track record with these machines that they’re able actually carry out our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is your belief, Greg, that it 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 actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like is what you need. But I think that our approach always been, you’ve got to push to the limits of technology to really see it in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it out there in and then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to 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 kind what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case what you have done is responsible here and not 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 incredibly important, from the very beginning, when we were thinking 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 build in secret, get this super powerful thing, and then you figure out the safety 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 for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path I see, which is that you do let reality you in the face. And 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 ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe that that box is 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 a chance that actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started 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, all these people a good time. And you think about it for moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time to it right, which do you pick? And you know, I really felt it in the moment. I was like, of you do 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 in computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve 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 history of computing, really mean it when I say that this is industry-wide or even 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 making computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And I think it’s 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 how to provide the feedback, decide what we want it. And my hope is that that will continue to be 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 coming to TED and blowing our minds.
(Applause)