We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for so many things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as a world going to define a 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 underlying principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. So have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you that you get of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate in this 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 behalf in terms carrying out your intent. And I’ll point out, this is all a live demo. This all generated by 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 looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these is they’re very inspectable. So you get this little 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 that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it out for all 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 ones to use in any situation. And this, I think, shows 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 as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is able sort of take away all those details from you. So don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a 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 the that’s really interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted for 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 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 cut back to slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do we even want to do when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that either good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an learning process. We just show it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re 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 step, too, which is to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, sort 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. For example, when first showed GPT-4 to Khan 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 bad 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 kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the of a couple of months we were able to the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. It it finds the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s 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 position 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 back 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, using this fact-checking tool is doing it in to produce data for another AI to become more to a human. And I think this really shows shape of something that we should expect to be much common in the future, where we have humans and kind of very carefully and delicately designed in how they fit into a problem and how want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can literally upload a file and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is 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 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, arXiv is a site people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. 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, can inspect. And then we’ll see word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, 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, magically that this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT as a brainstorming 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 how to these systems into our world.
And one thing I believe really deeply, is that getting right 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 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 looks different. Just from anything people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. Google 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 building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What is it going to take to make progress here? 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 each other to work together harmoniously.
CA: Can we have 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 the fact that you saw something in these language models that meant that if you continue invest in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and 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, it’s more nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very 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 to be tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is 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 as scale up, things emerge that you can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the integration with the world is 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 think that what we kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing be that we take this step by step. And we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a 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, more efficient, more ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any one moment, but that the of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that is 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 all these experts X is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable was capable of slowing 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 criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But 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 do let reality hit you the face. And I think you do give people to give input. You do have, before these machines perfect, before they are 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 the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are 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 in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I you 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 conference. I’m sitting in the hotel room just looking out over this wonderful water, all people having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years 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 be five years away. But if it gets to be 500 away and people get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course you the 500 years. My brother was in the military at the time and like, he puts his life the line in a much more real way than any of typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look the whole history of computing, I really mean it when say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all of these things, they 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 to connect to circuit, then you suddenly have this very powerful thing, no one’s had any 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 smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure 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 extraordinary child may have superpowers that take humanity to a whole new place. It is our collective to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that that will continue to be the path, but 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 so for coming to TED and blowing our minds.
(Applause)