We started OpenAI years ago because we felt like something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using the technology we 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 feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define a technology will be so important for our society going forward. I believe that we can manage this for good.
So today, I to show you the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that it actually did was write a prompt just like a could. And 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 me show what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in situation. And this, I 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 within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, you still can click 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 a 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 click “run,” there we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
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So we’ll back to the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how to them. Like, what do we even want it to when we ask these very high-level questions? And to this, we use an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments as it tries things out and things that are either good or bad.
And this exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what 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, only 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 is teach the AI what to do with those skills. for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus equals three and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And 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 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 question like this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use the 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 the AI a new tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out citations so you can actually go and very easily any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to 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 is doing it in order to produce for another AI to become more useful to a human. I think this really shows the shape of something that we expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed how they fit into a problem and how we to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that time, if we get this process right, we will be able to solve problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here is a specific of 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 this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column names like you saw and then the data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these are and that these are integer values and so it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know I want to ask. So fortunately, you can ask 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 know what want. And the AI kind of has to infer what I might be interested in. so it comes up with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this 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. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it to notice this thing, it’s a little 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 the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide shows a of how I think we … A vision of how we end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided the blood test, like, 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, think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, is that getting AI right 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, for what an AI and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of 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 number of people viewing this, look at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we do things? Yeah, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can we have the water, 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 fact that you saw something in these language models that that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the 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, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
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. 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 you can that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is 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 capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can see all of these 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 look at some that are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, if look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy 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 to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that we take this step step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there critics who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback that you talked about is basically going take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X 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 one or like that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology really see it in action, because that tells 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 controversial stance you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial 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 get this super powerful thing, and then figure out the safety of it and then you push “go,” you hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path I see, which is that you do let reality hit you in face. And I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. 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 the hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to 500 years away and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, he his life on the line in a much more way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you at the whole history of computing, I really mean 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 are there, right, we’re making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect to the circuit, you suddenly have 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 of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, in what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)