We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as a are going to define a technology that will be important for our society going forward. And I believe that we can this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of it can do on your behalf in terms of out your intent. And I’ll point out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other 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 coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was write prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if do make 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 explicitly ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort take away all those details from you. So you don’t have be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is live demo, so sometimes the 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 sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the how to use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows 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 things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three and run 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 able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching to 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. As we to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and 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 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 can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where the can issue search queries and click into web pages. And it writes out its whole chain of thought as it it. It says, I’m just going to search for and it actually does the search. It then it the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. And all of this could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole 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 to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should expect be much more common in the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to solve that problem. We 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 able actually create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able 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. I don’t think they’ve really changed 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 you 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 another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name of file 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 actual data. And that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are 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. fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going push back on 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 is the kind ambitious one.
(Laughter)
So you know, again, I feel like was more 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 what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of I think we … A vision of how we may end up this technology in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to to a professional, here are some 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 human with a medical professional and with ChatGPT as a brainstorming partner was able to achieve outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that 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 the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What it going to take to make progress here? We tried a lot of that 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 are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you saw something in these language that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and person was working on training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there 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 as machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just 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 will do it, which means it’s really learned an internal for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus 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 like can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned general, but that it hasn’t really 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 it to scale up and look at an incredible number pieces of text. And it is learning things that didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. tell 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 coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look 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 you know if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take step by step. And that we say, OK, as we on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re going to hear later 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, but that the of the scale and the human feedback that you about is basically going to take it on that of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of 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. They haven’t been right yet. They be right maybe 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits of technology to really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through 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 to put out here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. one thing I think has been incredibly important, from very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that this alternative is the only other path that I see, which is that do let reality hit you in the face. And I you do give people time to give input. You have, before these machines are perfect, before they are 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, the one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it 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 an conference. I’m sitting in the hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets to 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, he puts his on the line in a much more real way any of us typing things in computers and developing this technology 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 at the whole history of computing, I really mean when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And if you don’t put together, you get 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 time to adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is 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 out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)