We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering historic period right now where we as a world going to define a technology that will be so important for society going forward. And I believe that we can this for good.
So today, I want to show you the current of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to show you is what it’s to build a tool for an AI rather than building it a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, you know, suggest a 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 you get out of ChatGPT. And here we go, it’s not just the idea for 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 generates an image. And that something that really expands the power of what it can do on your behalf in terms of out your intent. And I’ll point out, this is a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did write a prompt just like a human could. And so you sort of this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know 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. So don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take 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 need. And the 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 shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it to do when we these very high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have human teacher 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 a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to actually complete math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI used to that answer. And 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 we have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback the machine alongside our team. And over the course a couple of months we were able to teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise 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 two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going to search for and it actually does the search. It then it finds publication date and the search results. It then is another search query. It’s going to click into the blog post. And all 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 work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and 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 really shows shape of something that we should expect to be more common in the future, where we have humans machines kind of very carefully and delicately designed in they fit into a problem and how we want to that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the name of file, the column names like you saw and then the data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer what might be interested in. And so it comes up some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind the 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 and dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. 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. course, the problem is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a projection? So 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 wanted to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how think we … A vision of how we may end up using technology in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that to a second vet who used it to save 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 professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we thought really hard about like: What is it going to take to make progress here? We tried lot of things that didn’t work, so you only the things that did. And I think that the important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you saw in these language models that meant that if you continue invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And key idea of 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 of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a houses 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 you just did not see coming.
GB: Yeah, well, so you try this 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 an internal circuit for how to do it. And really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you can see that it’s learning the process, but it 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 to have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth 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 to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 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 the big fears then, that arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that important thing 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 build up a track record with machines that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that 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 saying X is going to happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, well were there as the 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 capable slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make the that what you have done is responsible here and not reckless.
GB: Yeah, we 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 think has incredibly important, from 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? And that default of being, well, you build in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” you hope you got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach the only other path that I see, which is that you do let hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the 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 for that Pandora’s box to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five away. But if it gets to be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it in 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 way than any of us typing things in computers and this technology at 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, you look at the whole history of computing, I mean it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if does, or the moment that someone does manage to connect to the circuit, then suddenly have 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 of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: 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 we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)