We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for 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. honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather building it for a human. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was write a prompt just like human could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it explicitly 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, and usually it’s a great experience within an 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 from you. So you don’t to be the one who spells out every single sort of little piece 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 the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, 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 way to build them. And we have a tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if we want to. so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. just 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 that math problem, to say what comes next, that green nine up there, to 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. 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 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 it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to 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 things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one one equals three and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside team. And over the course 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 actually lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually writes out its whole chain of 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 and search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut to the side. And 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 is doing it in order to produce data for another AI become more useful to a human. And I think this really shows the shape of that we should expect to be much more common in the future, where have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, we will able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT take on to analyze a 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 scientist would. And you can just literally upload a file and ask questions 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 information here is the name of the file, the names like you saw and then the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time 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 three is kind the most common. It’s going to 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 be going on there? the way, all this is Python 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 pretty unhappy about 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. percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so if you 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 know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I think … A 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 call to say, “Let’s just wait and see.” And 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 brought that information to second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having rethink the way that 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 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 is you who’s come up with this 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 look at the compute progress, algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people who are different from 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 about the fact that you saw something in these language models that meant that if continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person working on training a model to predict the next character in reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the 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 if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the 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. the stuff you showed us just now. And the key of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you 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 the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you look our 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, the on coding problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I think 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 of the reasons we think it’s so important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the important thing will be that we take this by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it 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 basically going to take on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the 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 is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our has always been, you’ve got to push to the of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put out there in public and then harness all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done 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 one thing think has been incredibly important, from the very beginning, when we were thinking about how 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 this super powerful thing, and then you figure out safety of it and then you push “go,” and you hope you it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only other that I see, which is that you do let reality hit you in the face. And I think do give people time to give input. You do have, before these machines are perfect, before are super powerful, that you 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 one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam 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 that box something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t it 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 out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 years 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 and like, he puts his life on the line a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at whole history of computing, I really mean it when say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of 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 moment that you’re it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we encounter it. And I think it’s important today that we all do get literate in technology, figure out how to provide the feedback, decide we want from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)