We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are the technology we are building, and 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 we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will so important for our society going forward. And I that we can manage this for good.
So today, I to show you the current state of that technology and of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for AI rather than building it for a human. So have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
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I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And 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 by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under the hood and see that what actually did was write a prompt just like a could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it out 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 that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the 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 great within an app as long as you kind of the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort modify the actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of the if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show it the whole world, whole 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 that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. this 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 it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a couple 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 in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This one is browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to into the blog post. And all of this you do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in to produce data for another AI to become more to a human. And I think 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 fit into a problem and how want to solve that 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 even more trustworthy machines. And 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, think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names you saw and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people 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,” all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. 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 this year look really bad. Of course, the problem is that 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?] April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect 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 know I want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how we end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He brought that information a second vet 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 human with a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate systems into our world.
And one 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 road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial general intelligence all 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 number of 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. I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, 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 of employees on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very choices from the 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? We tried a of things that didn’t work, so you only see things that did. And I think that the most thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in 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, think the story there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. the stuff you showed us 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, colonies, single ants run around, when you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. so you can 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 there are in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort of generalize this to 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 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 science that we’re to really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is 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 to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and you can start 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 see all of these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback you talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. But I that our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way to do this is to put out there in public and then harness all this, you know, instead of just your team 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 founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are 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 case that what you have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the 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. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only path that I see, which is that you do let reality hit in the face. And I think you do give time to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve 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 thing was Viagra 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 box on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in the small print 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 I haven’t actually told before, which that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, these people having a good time. And you think about for a moment, if you could choose for basically 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 be 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on the line a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look at the history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. the more that 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, are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I think that if you look at capability, it’s been quite smooth time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this child that may have superpowers that take humanity to whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)