We started OpenAI years 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 to how far this whole field has come since then. it’s really gratifying to hear from people like Raymond 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 hear from who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic right now where we as a world 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 to you the current state of that technology and some the 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 an AI rather than it for a human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So 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 behalf in terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. looks wonderful.
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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 these tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was a prompt just like a human could. And so you sort of have ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having 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 spells out every sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, you still can click 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 tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
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So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask these very high-level questions? And to this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things and does things that are either 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 machine an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with sorts of 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 second step, too, which is to the AI what to do with those skills. And this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going 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 one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to 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 humans in this specific kind of scenario.” And we’ve made lots and lots of improvements 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 to say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen 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 a kid to clean their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine time goes on. And let me show you what mean.
For example, you can 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 two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into blog post. And 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 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 actually go very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to more useful to a human. And I think this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very 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, and machines are operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, it’s able to run code, just like a data would. And so you can just literally upload a file ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is 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 actually mean. Like, that semantic wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, the 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, is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind has to infer what I might be interested in. And it comes up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of most common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI is writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A of how we may end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate these systems 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 want to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to rethink 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 hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building 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 very choices from the early days. And the first one just to confront reality as it lays. And that 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 that did. And I that the most important thing has been to get of people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot of things, and person was working on training a model to predict next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we are just like, 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 to scale this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking at this, because these things are described as 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 is that when get more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, if you have it like a 40-digit number 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 you can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting be able to predict. So we were able to predict, example, the performance on coding problems. We basically look at some models that 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: here is, one of the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if you look this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy 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 a hard to supervise. Like, how do you know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. have to build up a track record with these that they’re able to actually carry out our intent. And I think we’re going 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 session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of scale and the human feedback that you talked about is going to take it on that journey of actually to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, 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 I heard on OpenAI when were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously 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 about to build artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so think 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 have, before these machines are perfect, before they are super powerful, that actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on 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 haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years away or 500 years away, which you pick, right? On 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 to get it right, which you pick? And you know, I just really felt in the moment. I was like, of course you do 500 years. My brother was in the military at the time like, he puts his life on the line in much more real way than any of us typing things computers and developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look the whole 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. the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think one thing I take away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been smooth 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 got to out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this child that may have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get 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 if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)