We started OpenAI seven years ago because felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to from people like 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 feel both those emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as world are going to define a technology that will so important for our society going forward. And I believe we can manage this for good.
So today, I to show you the current state of that technology and of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this all a live demo. This is all generated by AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and that what it actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so 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 know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all those details from you. you don’t have to be the one who spells every single sort of little piece of 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 the that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have a 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 work of the AI if we want to. And after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an old idea. If you go back Alan Turing’s 1950 paper 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 through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is actually solve the math problem.
But we actually have to do a second step, too, 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 rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, 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 some bad math in there, it will pretend that one plus one equals three and run it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s 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 think asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our ability to 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 ability to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it 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 pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and actually does the search. It then it finds the date and the search results. It then is issuing search query. It’s going to click into the blog post. And all of 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 where you can, you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so 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 is doing in order to produce data for another AI to more useful to a human. And I think this shows the shape of something that we should expect to much more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that time, if we get this process right, we will able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names like saw and then the actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of course, the 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?] April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is kind of ambitious 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 an overreach for it to have of, inferred magically that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the 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 correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I we … A vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about we consider how to integrate 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 how we want it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything 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 achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s 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, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you 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 technology shocked the world?
Greg Brockman: I mean, the truth is, we’re 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 within OpenAI, we made a lot of very deliberate choices from the early days. the first one was 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 of things that didn’t work, so you see the things that did. And I think that the important thing has been to get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in these language that meant that if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, one person was 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 commas go, where nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just 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 mind that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal for how to do 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 learning process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there in the 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 is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single of the stack 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 of these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, 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 you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in session, there 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, that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of these experts saying X is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. I think that our approach has always been, you’ve got to push to the of this technology to really see 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 a controversial stance you’ve taken, that the right way to do this to put it out there in public and then harness this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well you were there as great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only path that I see, which is that you do reality hit you in the face. And I think do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was 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 the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at the time and like, he his life on the line in a much more real 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 think that’s playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think if you look at capability, it’s been quite smooth over time. so the history, I think, of every 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 you us to have is that we have birthed this child that may have superpowers that take humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)