We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using the we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right 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, want to show you the current state of that and some of the underlying design principles that we dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the and see that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is using these tools, which allows 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.” make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all details from you. So you don’t have to be the one spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, you still click through it and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work the AI if we want 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 cut back the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first showed GPT-4 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 that one plus one equals three and run 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 our team. And over the course of a couple of months we were able to teach the 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 down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And me show you what I mean.
For example, you 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, these models not 100-percent reliable, although they’re getting better every time 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 for me.
Now, in this case, I’ve actually given the AI a new tool. This one is a tool where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and it does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in the driver’s seat, 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 of this 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 the side. And so thing that’s so interesting to me this whole process is 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 another AI to become more useful to a human. And I think this shows the shape of something that we should expect to much more common in the future, where we have and machines kind of very carefully and delicately designed in how 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 inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that 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 how analyze a data set like this.
So we can give ChatGPT access to 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 and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name the file, the column names like you saw and then the data. And from that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the 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 to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? the 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. makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under 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 the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A of how we may end up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, think about as we how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for the rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just 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 also 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 thousands of employees on artificial intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re 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 are industry-wide. But I think within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just confront reality 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 to get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I that in the early days, we didn’t know. We a lot of things, and one person was working on training a model to predict the next 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. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it 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, what we’re seeing of them feels … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you saw just pop that just blew your mind that you just did not 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 do it. And the really interesting thing is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to up and look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And so there’s something about 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. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I that what we kind of see right now, if look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. that we say, OK, as we move on to summaries, we have to supervise this task properly. We have build up 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 going hear later in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you about is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can 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 here has always been like, let reality hit you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y is how it works. have been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there as the great sort of on the big 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. at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some of 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 the time. Like, seriously all time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and you figure 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, 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 the face. I think you do give people time to give input. do have, before these machines are perfect, before they are powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box is that, there’s a very 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 thing the small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And you 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? the one hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years and people get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of you do the 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real way than any 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. I don’t think that’s quite playing the field as it truly lies. Like, if you look at whole history of computing, I really mean it when I say that this an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being 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 smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do it 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 … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise and not to tear us all down. that basically the model?
GB: I 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 that we do get literate in this technology, figure out how to the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)