We started OpenAI years ago because we felt like something really interesting happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s gratifying 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 are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world going to define a technology that will be so for our society going forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this coming 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 like a human could. so you sort of have this ability to inspect the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to sort of take away all details from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live 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 see we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. And after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we an old 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, can learn it. You could build a machine, like a human child, 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 a two-step process. First, we produce 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 comes next text you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to 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 a 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, the whole process that the used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach 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 be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one one equals three and run with it.” So we had to collect some feedback data. Sal himself was very kind 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 should push back on humans in this specific kind 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 a bat signal to team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback to 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 this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the 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 actually the search. It then it finds the publication date and the search results. It then is 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 a thing 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 out come citations so you 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 to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And think this really shows the shape of something that we should to be much more common in the future, where we humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more 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 sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let me show the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run 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 the 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 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 in there. has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” 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 I want ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots 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 the number of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional of, you 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 updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This slide shows a parable of 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 made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet 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 with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we how to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology looks 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 that can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way 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, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that meant 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 story is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today are 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 knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And key 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, different behavior. a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try in 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 do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there 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 to arbitrary numbers of arbitrary lengths.
CA: So what’s happened here 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, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you 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 on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that actually 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 as you scale up, emerge that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale timing. And I think one thing people miss, too, sort of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of 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 problem and like, no, no, no, machine, seven was the correct answer. But even summarizing a book, like, that’s a 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 the important thing will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise 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 like making 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 understanding inside, system is going 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 human feedback that you talked about is basically going to take it on journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit you 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 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 or like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we can 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 the right way to this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you 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 models that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, 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, the very beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach the only other path that I see, which is you do let reality hit you in the face. And I think do give people time to give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for 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 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 you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. brother was in the military at the time and like, he his life on the line in a much more real 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 think that’s quite the field as it truly lies. Like, if you look at whole history of computing, I really mean it when I that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that we have this extraordinary 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. that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope that that will continue to be the best path, but it’s so good we’re honestly 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)