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 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 from people who are excited, we hear from people 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 define a technology that will be so important for society going forward. And I believe that we can manage for good.
So today, I want to show you the current state of that technology and some the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know what we’re going to see. This wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood see that what it actually did was write a prompt just like human could. And so you sort of have this ability inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great experience 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 by having unified language interface on top of tools, the AI is able to of take away all those details from you. So you don’t have to be one who spells out every single sort of little 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 list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click through it and sort of the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a 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 inspect, we’re able to change the work of the AI if we to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
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So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building these tools. It’s about teaching AI how to use 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. you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, a 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 is exactly we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this process 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 up 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. for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the 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 infer your intent apply it in scenarios that it hasn’t seen before, it hasn’t received 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 so great, We’re going 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 one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind 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 we were to teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really listen 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 think about asking kid to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for this it actually does the search. It then it finds 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 humans want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and easily verify any piece 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 to the side. And so thing that’s so interesting to me about this whole process 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 AI to become more useful to a human. And I think really shows the shape of something that we should to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able to solve problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. 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 much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And you can just literally 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 parse it you.” The only information here is the name of file, the column names like you saw and then the data. And from that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now I don’t even 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 of intent behind it. But I don’t even know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if you want to what 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, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk 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 rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the first one was just to reality as it lays. And that we just thought really hard about like: is it 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 that the important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models that meant that if continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what wanted to 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 model to predict the next character Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more 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 that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning 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 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 arbitrary numbers of arbitrary lengths.
CA: So what’s 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 was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of 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 so that’s one of reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if you look 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 problem and like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never 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 any one moment, that the expansion of the scale and the human feedback that you about is basically going to take it on that journey actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been just like, reality hit you in the 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 saying nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to push to the of this technology to really see it in action, because that 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 a controversial stance you’ve taken, the right way to do this is to put it out there in public then harness all this, you know, instead of just your giving feedback, the 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 you were founded a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if 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 through the tech world that now and Meta and so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that plan of being, well, you build in secret, you this super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path I see, which is that you do let reality hit in the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with it generate misinformation, try to 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 thought experiment for you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give 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 could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. 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 we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets be 500 years away and people get more time to it right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more real than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean when I say that 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 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 don’t put them together, you get an overhang, which means that 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 adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for 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 that take humanity a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)