We started OpenAI seven years ago because we felt like really interesting was happening in 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 to hear from like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we 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 now where we as a are going to define a technology that will be important for our society going forward. And I believe we can manage this for good.
So today, I want show you the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it 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 you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see that it actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s 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 able to sort of take away all those details you. So you don’t have to be the one who out every single sort of little piece of what’s supposed happen.
And as I 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 Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? If you look at this, you still click through it and sort of modify the actual quantities. And that’s something that I think 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 been drafted for our review, is also 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 after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the 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 use them. Like, do we even want it to do when we ask very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer this. Instead, you 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 as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to machine alongside our team. And over the course of couple of months we were able to teach the that, “Hey, you really should push back on humans in this kind 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 sending up a bat signal to our 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. you think about asking a kid to clean their room, if all you’re doing 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 way. And the same sort of reasoning applies to AI. As we move to tasks, we will 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 the machine as time goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the 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 the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and the results. It then is issuing another search 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 be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data 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 and delicately designed in how they fit into a problem how 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 way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you saw and the actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” once 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 to infer what might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the year not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. And now, 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 a parable of how I think we … A vision of how we may up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional 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 these systems into world.
And one thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the reasons we 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 that 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 think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? 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, guess my first question actually is just how the hell have 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 shoulders of 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 a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do 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 predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell you if a review was or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 times 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, one the big fears then, that arises from 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 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 sort of the integration with world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a 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 will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of the scale and the human feedback that talked about is basically going to take it on journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, of these experts saying X is going to happen, Y is how it works. 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 one or something like that is what you need. But I think that our has always been, you’ve got to push to the limits of this to really see it in action, because that tells then, oh, here’s how we can 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 way do this is to put it out there in and then harness all this, you know, instead of just your giving 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 when were founded as a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the 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 proper guardrails or we die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which is that you let reality hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in 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 that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking out over this 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 or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it to be 500 years away and people get more time get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts his life on the 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 the field as it truly lies. Like, you look at the whole history of computing, I really mean it I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails 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 think it’s also important 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 how to provide the feedback, what we want from it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)