We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we 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, feels like we’re entering an historic period right now where as a world are going to define a technology will be so important for our society going forward. And believe that we can manage this for 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 going to you is what it’s like to build a tool for an rather than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED and draw a 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 you get out of ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it can do on your in terms of carrying out 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 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 about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood see that what it actually did was write a prompt just like a human could. And 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, and let show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking 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 as long as you kind of know the menus know all the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able to sort of take all those details from you. So you don’t have to the one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will 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 the UI is still very valuable, right? If you look this, you still can click through it and sort modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we 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 to the slides. Now, important thing about how we build this, it’s not just about building these tools. It’s 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 do this, we use old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program 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 who provides rewards and punishments as it tries things and does things that are either good or bad.
And this exactly how we train 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.” this process imbues it with all sorts of wonderful skills. For example, if you’re 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 problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so 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 there, it will happily pretend that one plus one equals and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” 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 is inspecting the floor, you don’t know if 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 sort reasoning applies to AI. As we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool where 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 actually does the search. then it finds the publication date and the search results. It is issuing another search query. It’s going to click into the blog post. And of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI become more useful to a human. And I think this shows the shape of something that we should expect be much more common in the future, where we humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, we 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 with computers. example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is a specific spreadsheet of the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names like you saw then the actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has to infer what I be interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the most common. It’s going to make this nice plot of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how may end up using this technology in the future. A person brought his very sick dog 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 medical records, GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, that getting AI right is 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. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything had anticipated. And so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much every single thing about the way I work, need to 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 really scary. let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have 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 are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the first one was just to confront reality it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we the 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 these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and one person was working on training 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 where the go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop just blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They you 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 to predict. So we were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even though it’s early 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 can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of see right now, if look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that 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 this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record these machines that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, the system 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 one moment, but that the expansion of scale and the 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 you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in public then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, 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 were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to 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. But one thing I think has incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which is that you do let hit you in the face. And I think you do give people time give 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 from GPT-3, right? GPT-3, we really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box something 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 also one percent thing in the small print there 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. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, he puts life on the line in a much more real way than any of us typing in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at whole history of computing, I really mean it when say that this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re still improving 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 moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. Is 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 all do get in this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)