We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole has 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 wonderful things. We from people who are excited, we hear from people who concerned, we hear from people who feel both those emotions at once. And honestly, that’s 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 will be so for our society going forward. And I believe that 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 show you is what it’s like to build a for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all 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 the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to 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 tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, I like 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 spells out every single sort of piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still very valuable, right? If look at this, you still can click through it and of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just 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 important thing. We can “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing about how build 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 to do when we ask very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as tries things out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, 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 actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out 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, but very importantly, the process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to 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 teach the AI are what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over course of a couple of months we were able to teach AI 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 push that thumbs down in ChatGPT, that is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And so you do that, that’s one way that we 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. If you about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the 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 two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going search for this and it actually does the search. It then finds the publication date and the search results. It then is issuing 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 thing humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of 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 me this whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And I this really shows the shape of something that we expect to be much more common in the future, where we have humans and kind of very carefully and delicately designed in how they fit into a and how we want to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And 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 going to be able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to a data set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and ask questions 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 the of the file, the column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now don’t even know what 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 kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the is not over. So I’m going to push back on the machine. [Waitttt that’s 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 that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to 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 to take away from talk, it’s that this technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I 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 reeling. Like, I suspect that 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 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, 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 have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial 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. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront as it lays. And that we just thought really about like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only see things that did. And I think that the most thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a result where — this is 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 could tell you if review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was first time that you 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 to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one moment for 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, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often get 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 are in the universe. So it had to have something general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I think is very undersung 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 incredibly tiny. Same true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the 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 these questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if 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 will be we take this step by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up a record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback you 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 be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way do this is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the world is now giving feedback. … 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, well you there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all time. And 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 it benefit all of humanity, like, are you supposed to do that, right? And that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the safety it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that you do let hit you in the face. And I think you do give people time to give input. do have, before these machines are perfect, before they are super powerful, that you actually have 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 to do with it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things 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 that box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the 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 story that haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand 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 you do the 500 years. My brother was in military at the time and like, he puts his life on the in a much more real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at 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. And the more that sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I take away like, even you think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in humans could do. But I actually think that if you look at capability, it’s quite 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 to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)