We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who are using the we are building, and others, for so many wonderful things. hear from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world are going define a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I to 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 to show you what it’s like to build a tool for an rather than building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the of what it can do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry 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 these tools is they’re very inspectable. So you get little pop up 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 under the and see that what it actually did was write a prompt just 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 information to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without me having to it explicitly which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience 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 good be polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all those details from you. you don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you at this, you still can click through it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now we 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 to inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to 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, what 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 an answer to this. Instead, you can it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is teach the AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one 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 all you’re doing inspecting the floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model can issue search queries and click web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for and it actually does the search. It then it the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful a human. And I think this really shows the shape of that we should expect to be 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 the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to actually create 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 you sense of 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 in 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 see there the data right here. But let me show you ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here 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 columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer I might be interested in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will 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 of most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of how we may end up this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a 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 full medical records, GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought that to a 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 partner was able to achieve outcome that would not have happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems our world.
And one thing I believe really deeply, that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to become literate. 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 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 viewing this, you look at that and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way 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 my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate from the early days. And the first one was to confront reality as it lays. And that we just thought really hard about like: What it going to take to make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think that most important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these models that meant that if you continue to invest them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and how to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training model to predict the 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 nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got 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 are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. to do that actually, one of the things I is very undersung in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we of 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 we do, can inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re to actually carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I 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 all these experts saying X going 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 maybe 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some of their criticisms have been, you are 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 responsible 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 to it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you to do 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 push “go,” and hope you got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you do let reality hit you in the face. And think you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people going to do with it was generate misinformation, try to 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 thought for you. Suppose you’re sitting in a room, there’s a box on the table. believe that in that box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time to get it right, do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he his life on the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, you 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 more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving 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 that someone does manage to connect the circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth 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: So what I’m is that you … the model you want us to is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that that will to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)