We started OpenAI seven years ago we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you current state of that technology and some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of 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 in this case — sorry, it doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this 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 hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this later.” And the interesting thing about 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, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually was write a prompt just like a human could. And so you of have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to 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, have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take away all those details from you. you don’t have to be the one who spells out every single sort of piece of 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 a look at the shopping list while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the if we want to. And so after this talk, you will be able to access this yourself. there 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 ask very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And 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 and say, “Predict what next in text you’ve never seen before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received 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 Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and lots of improvements to the 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 of weakness where you should gather feedback.” And so when you do that, that’s way that we really listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a 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 in the 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 this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question 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 not 100-percent reliable, they’re getting better every time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search queries and click into web pages. it actually writes out its whole chain of thought as it does it. It says, I’m just to search for this and it actually does the search. then it finds the publication date and the search results. then is issuing another search 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 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 can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months was wrong. Two months and 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 AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more to a human. And I think this really shows the shape of something that should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We make sure the humans are providing the 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 trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve 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 papers on the arXiv for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can 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 you can literally upload a file and ask questions about it. And very helpfully, you know, it knows name of the file and 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 you saw then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now 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 is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of 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 it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 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 back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off 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 was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred magically that this 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, so if want to 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 for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this something we should all reflect on, think about as consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding 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 one thing to away from this talk, it’s that this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, 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 really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot things that didn’t work, so you only see the things did. And I think that the most important thing has been to teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by 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 you saw something in these language models that meant that if you continue to invest them and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what 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 tried a of things, and one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible number of pieces 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 science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is 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 something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise 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 the machine aligned with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer 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 face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that 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 controversial stance 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 your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and 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 release 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 of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible 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 incredibly important, the very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and 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. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that you do let reality hit you in face. And I think you do give people time to input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was 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. you think about it for a moment, if you could choose for basically that Pandora’s box to five years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother 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 developing 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, if look at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being a zero to 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 it for each that you’re increasing it.
CA: So what I’m hearing is that you … the model want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. And think 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 this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)