We started seven years ago because we felt like something really was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so important for our going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated the 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 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 to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did write a prompt just like a human could. And so you sort of have this ability to how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you 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 little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great experience within app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. good to 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. So don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see 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 still very valuable, right? you look at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things that 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 machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, 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 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 it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI 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 show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. It then is another search query. It’s going to click into the blog post. And of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and 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 human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And think this really shows the shape of something that should expect to be much more common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into problem and how we want to solve that problem. make sure that the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a sense 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, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. 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 you can see there data right here. But let me show you the take on how to analyze a data set like this.
So we can give ChatGPT access yet another 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 a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here the name of the file, the column names like you and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, is a site that 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 ask the machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all 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 push 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 April 13 was cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this 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 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. This shows a parable of how I think we … A vision how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to save 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 able to achieve an outcome that 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 from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe 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 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 and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just 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 also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What is going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I think that most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you something in these language models that meant that if you continue to in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, think the story 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 do it? I think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a 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 semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these 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 showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind that you did 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 will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you 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 table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened 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 it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You have to get single piece 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. you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to 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 smaller. And so there’s something about this that is actually smooth scaling, even it’s still early days.
CA: So here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you 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 important to deploy incrementally. And so I that what we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the tasks we 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. But even a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to up a track record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to — we’re never 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 the expansion of the scale and the human feedback that you about is basically going to take it on that journey actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be of 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 the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the field broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I think that our has always been, you’ve got to push to the limits of this to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to it out there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech 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 this out here without proper guardrails or we die. You know, how you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all 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, from the beginning, when we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which is that do let reality hit you in the face. And think you 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 were that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are 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 box is 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. But there’s actually 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 that 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 looking out over this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets to 500 years away and people get more time to it right, which do you pick? And you know, I just felt it 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 any us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look the whole history of computing, I really mean it when say 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 are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that 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 of precautions you get. And so I think that one thing I away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, in 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 for each moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important to say may 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 from it. And my hope that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)