We started OpenAI years ago because we felt like something really interesting was happening in AI and we wanted to 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 hear from people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as a are going to define a technology that will be so important 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 principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI 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 for to use on your behalf. And you can do 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 and creative back-and-forth taking care of the details for you that you get of 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 — sorry, it doesn’t generate text, it also generates an image. that is 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, this is a live demo. This is all generated by the AI as we speak. So 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 tools too, for 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, is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. And so 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 later, and let me show you what it’s like to that information and to integrate with other applications too. can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click 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. Always good be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will happen to us. But let’s take a 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 the thing that’s really interesting that the traditional UI is still very valuable, right? If you look at this, you still click through it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back 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 how to them. Like, what 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 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine 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. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just 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 infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the sort 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 happy help. It’s happy to help us provide even better feedback to scale 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 like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the says 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. we 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 tool where the model can issue search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It it finds the publication date and the search results. It then is another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I think this shows the shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem how we want to solve that problem. We make that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. And here is a 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 show you the ChatGPT take how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file 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 like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer what I might be in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision how we may 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 be here today had listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to professional, here are some hypotheses.” He brought that information to a second vet who used it to the 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 and ChatGPT as a brainstorming partner was able to achieve 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 is going require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at 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 from early days. And the first one was just to confront as it lays. And that we just thought really hard about like: What is going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I think the most important thing has been to get teams people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going 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 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, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and 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, we are just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this 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 circuit for how to do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so 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 than there in the universe. So it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. So we 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 scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, you 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 math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able actually carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. might be 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 push to the limits of this technology to really it in action, because that tells you then, oh, here’s how can move on to a new paradigm. And we just haven’t 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 and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path that I see, which is that do let reality 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 actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on 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 to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 years away, 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 it gets to be 500 years away and people get more time get it right, which do you pick? And you know, I really felt it in the moment. I was like, of you do the 500 years. My brother was in the military at time and like, he puts his life on the line a much more real way than 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 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 a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces that 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 them together, you get an overhang, which means that if someone does, the moment that someone 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 I 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 actually that if you look at capability, it’s been quite over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)