We started seven years ago because we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s really gratifying to hear from 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 like we’re entering an historic period right now where we as a world going to define a technology that will be so important our society going forward. And I believe that we can manage for good.
So today, I want to show you current state of that technology and some of the design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool for an AI rather building it for a human. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of it can do on your behalf in terms of 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 what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that and to integrate with other 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, definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as kind of know the menus 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 tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? If look at this, you still can click through it and sort of the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented way build them. And now we have a tweet that’s been drafted for review, which is also a very important thing. We 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 we 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 back to the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a 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, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. 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 this reinforces not the specific thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the AI 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 able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way that really 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 kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the to 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 new tool. one is a browsing tool where the model can issue search queries and into web pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and the results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so can actually go and very easily verify any piece this whole 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. so thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I think this shows the shape of something that we should expect to be more common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And helpfully, you 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 is the name of the file, the column names like you saw and the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors in the paper,” like all of that, that’s for a human to do, and the AI is 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 don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the of 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 nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 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 one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, 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 think we … A vision of we may end up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here 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. cannot overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every thing about 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, I guess first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront as it lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we 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 saw in these language models that meant that if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the 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 the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often 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 addition table, that’s more than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at 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 one that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then you 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 all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, if you at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able 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 like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and forth. Is it your 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 of actually getting to like truth and wisdom and so forth, with a high degree of confidence. you 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 approach has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be 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 see it in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has been important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how 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 then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone 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 you do let reality hit you in the face. And think you do give people time to give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but 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 that in box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing the small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is shortly after we 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 time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, for you 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 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 brother was in the at the time and like, he puts his life on the line in a much real way than any of us typing things in and developing this technology at 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 playing the field as it lies. Like, if you look at the whole history of computing, I really mean when I say that this is an industry-wide or 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, all 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 to the circuit, then you suddenly have this very powerful thing, no one’s any time 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 of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And 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 what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise not to tear us all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to 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 it. And my hope is that that will continue to be the path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)