We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world are going to a technology that will be so important for our going forward. And I believe that we can 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 first thing I’m going to show you is it’s like to build a tool for an AI rather than building it a 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 do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually was write a prompt just like a human could. so you sort of have this ability to inspect how machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for 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 is selecting all these different tools without me having to it explicitly which ones to use in any situation. And this, I think, shows a new way 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 it’s a great experience within an app as long as you kind of know menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able sort of take away all those details 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 I said, is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list 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 interesting is that traditional UI is still very valuable, right? If you at this, you still can click through it and sort of the actual quantities. And that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. so after this talk, you will be able to access this yourself. And 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 just building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when we ask these very high-level questions? to do this, we use an old idea. If you go 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 child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet 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 shown a math problem, the way to actually complete that 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 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, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve actually lots and lots of 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 an area of weakness where should gather feedback.” And so when you do that, that’s one that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale our to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 a question like this, of how 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, these are not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing where the model can issue search queries and click into web pages. it actually writes out its whole chain of thought as does it. It says, I’m just going to search this and it actually does the search. It then it the publication 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 a thing that really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out come citations so you can actually and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so to me about this whole process 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 order to produce data for another AI become more useful to a human. And I think this really shows the shape something that we should expect to be much more in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even 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 able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been in some 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 AI 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 analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so you can literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the names like you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that three is kind of the most common. It’s going 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 cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these 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 in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that make 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 I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I not a vet, you need 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. But story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial intelligence 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 a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single thing about the way I work, I 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, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually 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 with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it going take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important thing has been get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also about the fact that you saw something in these models that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is a syntactic process, you expect, know, the model 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 model could you if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the time 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 to see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop that just blew your mind that you did 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 model will do it, which means it’s really an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things you didn’t know that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply 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 look at models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you can 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 these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a track with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, there 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 sense and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback you talked about is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been 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 it works. People have been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. But I think our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way do this is to put it out there in public and then all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil with AI. And you were going to build models sort of, you know, somehow held them accountable and 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 shockwaves through the tech world that now Google and Meta and so forth are all scrambling catch up. And some 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 these questions the time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial 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, then you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that I see, which is that you let reality hit you in the face. And I think do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking 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, maybe you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts his on the line in a much more real way than any of us things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so think that one thing I take away is like, you think about 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 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 and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is we have birthed this extraordinary child that may have that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say this may shift, right? We’ve got to take 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 want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)