We started OpenAI seven years because we felt like something really interesting was happening in and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really 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 important for our society going forward. And I believe that we can 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 to build a tool for AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, a nice 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 that you get out of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect how the machine is these 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 information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so used to thinking of, well, we have apps, we click 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 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 all those details from you. So you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? you look at this, you still can click through it and of modify the actual quantities. And that’s something that 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 click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, what do we even want to do when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But actually have to 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, then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing 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 it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be 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 over the course of a of months we were able to teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it writes out its whole chain of thought as it it. It says, I’m just going to search for this and actually does the search. It then it finds the date and the search results. It then is issuing search query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it actually out two 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 to me about this whole process is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. And I this really shows the shape of something that we should to be much more common in the future, where we have humans and machines of very carefully and delicately designed in how they into a problem and how we want to solve problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a that’s inspectable 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 able to solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you 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 information here is the name of the file, the names like you saw and then the actual data. from that it’s able 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 these things are and that these are integer values and therefore it’s a number of authors in the 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, is a 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 what I might interested in. And so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then make this nice plot the 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 way, all this is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you want to 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 I want.
Now we’ll cut to the slide again. This slide shows a parable of I think we … A vision of how we may up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general 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 of reeling. Like, suspect that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the 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 compute progress, the progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to reality as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a of things that didn’t work, so you only see the things did. And I think that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we 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 about the fact that you saw something in these language models that that if you continue to invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot of things, one person was working on training a model to predict next character in Amazon reviews, and he got a where — 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 just like, come on, can do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking at this, 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 you us just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give one moment for 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 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 get it wrong. And so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you 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 here is that you’ve allowed to scale up and look at an incredible number pieces of text. And it is learning things that 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 of these emergent capabilities. to do that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically 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 days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of you. Why 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. And I one thing people miss, too, is sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons that 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, a lot what I focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I think that the 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 with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can be sure 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 approach here has always been just like, let hit you in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y how 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 or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And just 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 then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were going to build models that of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us put this out here without 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 about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. 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 super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do give people time to input. You do have, before these machines are perfect, before they are super powerful, that you have the 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 do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something glorious that’s going to give beautiful 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 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 that shortly we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to five years away or 500 years away, which would pick, right? On the one 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 people get more to get it right, which do you pick? And you know, I really felt it 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 this technology at time. And so, yeah, I’m really sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more 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 them together, get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what 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 it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not 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 we encounter it. And I think it’s 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 be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)