We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted to help steer 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 people like Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we are exposing it as app for 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 all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. 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. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all live demo. This is all 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 hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the and see that what it actually did was write a prompt like a human could. And so you sort of have ability to inspect how the machine is using these tools, which 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 list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. We can “run,” and there we 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 this talk, you will be able to 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 not 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 use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like human child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out does things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we listen to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking 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 all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move 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 feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model says months passed. But is 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 its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I think this really shows the shape of that we should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in 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 here. But let me show you 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 able to code, just like a data scientist would. And so you can just upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the column names like you and then the actual data. And from that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer values and 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 know what I want to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And AI kind of has to infer what I might interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I 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 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 that, but it know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to 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. this 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 something we should all reflect on, about as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way 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 my first actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you look at compute 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 just confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? tried a lot of things 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 who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if review was positive or negative. I mean, today we are 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 we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: So I 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 a prediction machine. Just the stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where 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 you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 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 you can’t the 40-digit addition table, that’s more atoms than there are 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 arbitrary numbers of 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 going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to get good at is predicting some 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 our entire stack. When 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 doing predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And we’re starting to be able to predict. So we 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 this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to 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 there a huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if you at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true any one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it on journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts X is going to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach has been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to it out there in public and then harness all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so are all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think 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 think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got it right. 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 this alternative approach is the only other path that I see, which that you do let reality hit you in the face. I think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra 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 on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to 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 actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more time to it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts life on the line in a much more real way any of us typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making 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, which means that someone does, or the moment that someone does manage to connect the circuit, then you suddenly have this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what could do. But I actually think that if you look capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it 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 this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)