We started OpenAI seven years ago because felt like 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 to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a that will be so important for our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s to build a tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the for 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 it can do on your behalf in terms of carrying out your intent. And I’ll point out, is all 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. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other 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.” by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all those from you. So you don’t have to be the 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 will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional UI is still very valuable, right? If look at this, you still can click through it sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what we even want it to do when we ask 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 an to this. Instead, you can learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through 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 math problem.
But we actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that answer. And this allows it generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you do that, that’s one way we really listen to our users 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 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, will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, this 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 its whole chain of thought as it it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to 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 turns two months was wrong. Two months and one week, was 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 an AI. Because a human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to a human. And I think this really the shape of something that we should expect to be more common in the future, where we have humans and kind of very carefully and delicately designed in how they into a problem and how we want to solve problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating 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 this process right, we will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, think 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. here is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access yet 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 and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of file, the column names like you saw and then actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I want to ask. 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 the kind of has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it dropped off the cliff. could be going on there? By the way, all is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes 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 percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows a parable of how I think … A vision of how we may end up using this technology the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, 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 this story, think, shows that a human with a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling 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 way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having to 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, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take to make progress here? We tried lot of things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are very different from other to work together harmoniously.
CA: Can 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 saw something in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a of things, and one person was working on training a to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics emerged from this underlying syntactic 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 explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all 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 few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do 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 can 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 in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And it is learning that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. So we were able to predict, example, the performance on coding problems. We basically look at some 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 still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I that the important thing will be that we take this step by step. And that say, OK, as we move on to book summaries, have to supervise this task properly. We have to build 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 the machine be aligned with you.
CA: So we’re 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, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human 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 of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push the limits of this technology to really see it action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way to do this is put it out there in public and then harness this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, 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 their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at 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 through the tech world now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all of humanity, like, how are you 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 it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach the only other path that I see, which is that you let reality hit you in the face. And I think you do give people time to give input. 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 it from 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 one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a time. And you think about it for a moment, if could choose for 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, for you personally, it’s better to have it be five years away. if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, he puts his on the line in a much more real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I mean it when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I away is like, even you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change in what humans could do. I actually think that if you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want it. And my hope 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 blowing our minds.
(Applause)