We started OpenAI seven ago because we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from who are concerned, we hear from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, want to show you the current state of that technology and some of the design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do things like ask, you know, a 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 care of the details for you that you get out of ChatGPT. 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 this — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it can on your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was write a prompt just a human could. And so you sort of have this ability to how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to integrate other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, 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 long as you kind 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. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, still can click through it and sort of modify actual quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do a second step, too, which is to the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we first showed GPT-4 to 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 math in there, it will happily pretend that one plus equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind offered 20 hours 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 back on humans in this specific of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like up a bat signal 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 we really listen 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 think about asking a to clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same 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 is happy to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting better every time we provide some feedback. 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 this case, I’ve actually given the a new 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 thought as it does it. It says, I’m just going to search for this and it does the search. It then it finds the publication and the search results. It then is issuing another query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I think this really the shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and designed in how they fit into a problem and we want to solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even 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 think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around in some 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 on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, 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. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the column names like you and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what things are and that these are integer values and so it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI happy to help with 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 again, this is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI of has to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how we may end using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And 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, need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we consider to integrate these 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 how we it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the 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 … I suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just 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 just how the have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working 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 all on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing been to get teams of people who are very different each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that if you continue to invest them and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew was what we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, didn’t know. We tried a lot of things, and person was working 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, the model predict where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could tell you if a 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 underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, 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 the process, 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. So had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things that you didn’t know it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if you at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this step step. And that we say, OK, as we move on 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 carry out our intent. I think we’re going 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 going to hear later in this session, are critics who say that, you know, there’s no 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 any one moment, but the expansion of the scale and the human feedback you talked about is basically going to take it that journey of actually getting to things like truth and and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells then, oh, here’s how we can move on to 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, the is now giving feedback. But … If, you know, 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 of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this super powerful thing, and then you figure 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 does. But for me, that was always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I 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 to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid 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 spam is bad, but there are things that are 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 everyone. But there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do 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 was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a time. And you think about it for a moment, if could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get time to get it right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he puts his 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 sold on the you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the history of computing, I really mean it when I that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth over time. And so history, I think, of 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 each that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it to be and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)