We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for so many wonderful things. We from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where we a world are going to define 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 show 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 like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just idea 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. And that is something really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a just like a human could. And so you sort of this ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away all those details from you. you don’t have to be the one who spells every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able change the work 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 cut back 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 do even want it to do when we ask these high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we have to do a second step, too, which is to the AI what to do with those skills. And for this, we provide feedback. We the 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 the AI said, but very importantly, the process that the AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer 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 able to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a bat signal to 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 to our users make sure we’re building something that’s more useful for 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 the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy 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, can ask 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. is it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. then it finds the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration between a human 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 something that we should expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. make sure that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every 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. And here a specific spreadsheet of all the AI papers on 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 the ChatGPT take on how to analyze a data set like this.
So we can give access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is the name the file, the column names like you saw and then 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 its world knowledge of that, “Oh yeah, arXiv is a site that people submit and therefore that’s what 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 human to do, and the AI 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 a 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 it up with some good ideas, I think. So a histogram of number of authors per paper, time series of 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 curve. You see that three is kind of the common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could be going on there? By way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of 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. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted of the machine here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, magically that 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 you want to inspect what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If 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. This shows a parable of how I think we … vision of how we may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it save the 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 an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s 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. if there’s one thing to take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general 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 that a very large number of people viewing this, you look 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 right? Who thinks that they’re having to rethink the that we 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 first question is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands 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 giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And we just thought really hard about like: What is going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I think that most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you something in these language models that meant that if you continue to invest them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is 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 lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he 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 out of it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. 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 can try this in ChatGPT, if you add 40-digit —
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 can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And it 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 science that we’re starting to really get good at predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild our 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 these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration the world is also 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 see right now, if you at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the answer. But even summarizing 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. No wants to do that.
(Laughter) And so I think that the important thing will be we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine 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 common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think that the approach here has always been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts X is going to happen, Y is how it works. 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 plus or something like that is what you need. But think that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we 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 harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, evil thing 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 least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through 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 forcing to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then you push “go,” and hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is you do let reality hit you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to with it was 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 sitting in room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could for basically that Pandora’s box to be five years away 500 years away, which 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 it right, which you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a much real way than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I mean it 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 together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this child to collectively teach 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 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 this technology, figure out how to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)