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 honestly really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now we as a world are going to define a technology that will be so for our society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology and some of underlying design principles that we hold dear.
So the first 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, generates images, and we 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 of the, sort of, ideation and creative 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 for the meal, but a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the hood and see that what it did was write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that and to 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 for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you of know the menus and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details from you. you don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And I said, this 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 list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way build them. And now we have a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use old idea. If you go back 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 human who provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine 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. have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his time to 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 back on humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so you do that, that’s one way that we really to our users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting 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 of reasoning applies to AI. we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning 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 every time we provide feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search queries and 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 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 to click into the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really want to do. It’s much more to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And come citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll 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 to produce data for another AI to become more to a human. And I think this really shows the shape something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give a sense of just 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 years ago 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 the arXiv for past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names like saw and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here we go, nice bell 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. Looks like were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is 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?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think … A vision of how we may end up using this technology in the future. person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting right is going to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and 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? thinks that they’re having to rethink the way that we 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 thousands of employees working on artificial intelligence. Why is you who’s come up with this 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 data progress, all of those really industry-wide. But 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 to take to make progress here? We tried a lot of things that didn’t work, so you 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: we have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these 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 high level, deep learning, like we always knew that 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, and one person was working training a model to predict the next character in reviews, and he got a result where — this is a process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale 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, we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It all the 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 and jams. Give me one moment for you when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing is actually, if you have it add 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 addition table, that’s more atoms than are in the universe. So it had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very powerful thing too. so 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 is providing really high-quality feedback. Today, the tasks that 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 the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take step by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up a track record with these that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take it on journey of actually getting to things like truth and and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that 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, reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying is going to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus or something like that is what you need. But I 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 move on 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 is to 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, bad 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 as great sort of check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s 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 to catch up. And of their criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has 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, you build 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 always terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which is that do let reality hit you in the face. And I think you do people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are 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 beautiful to your family and to everyone. But there’s actually a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 away and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. I was like, course you do the 500 years. My brother was in the military at the time and like, puts his life on the line in a much more way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to the feedback, decide what we want from it. And my hope that that will continue 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: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)