We started OpenAI seven ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really amazing to see how 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, others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now we as a world are going to define a technology that be so important for our society going forward. And I that we can manage this for good.
So today, I want show you the current state of that technology and of the underlying design principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather building it for a human. So we have a DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, but a very, detailed spread. So 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 an image. And that is something 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 a demo. This is all generated by the AI as we speak. So actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what actually did was write a prompt just like a could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we click between them, 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 like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted for review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You build a machine, like a human child, and then it through feedback. Have a human teacher who provides and punishments as it tries things out and does that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with all sorts wonderful skills. For example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue search 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 for this and it actually does the search. It then it finds the date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, 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 expect to be much more in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going to be able to rethink almost every of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in time. And here is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and 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, and AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer I might be interested in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. didn’t ask for that, but it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A vision of how we may up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think as we consider how to integrate these systems into 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 slot in, that’s setting the rules of the road, for what an will and won’t do. And if there’s one thing take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that general intelligence benefits 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 suspect that a very large of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who 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 first question actually is just the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the 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 made a lot of 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 it to take to make progress here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing has been get teams of people who are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model to the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies that show 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 for you when you saw just something pop that just your 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 how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus 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. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy 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 know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the scale and the human feedback you talked about is basically going to take it that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit in the face, right? It’s like this field is the field broken promises, of all these experts saying X is to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach always been, you’ve got to push to the limits this technology to really see it in action, because that tells you then, oh, here’s how we can on to 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 all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big doing their 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, 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 world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to that, right? And that default plan of 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 always terrifying, it didn’t right. And so I think that this alternative approach is the only other that I see, which is that you do let reality hit you in the face. And I you do give people time to give input. You have, before these machines are perfect, before they are 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 to do it was generate misinformation, try to tip elections. Instead, 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 box on the table. You believe that in that is something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, remember I 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 a good time. And think about it for a moment, if you could choose for basically that Pandora’s box to five years away or 500 years away, which would pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line a much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m sold 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 industry-wide or even just almost like a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving 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 any time to adjust, who knows kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other 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 look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my hope is that that continue to be the best path, but it’s so good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)