We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just amazing to see how far this whole field has since then. And 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, we hear people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation and 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. 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 an image. And that is something that really expands the power of it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. is all generated by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually did was write a prompt like a human could. And so you sort of have this ability inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use information and to integrate with other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you make this wonderful, 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 in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away those details from you. So you don’t have to the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we a 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 still can click it and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher 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 Turing would called a child machine through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, which is teach the AI what to do with those skills. And this, we provide feedback. We have 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 that the AI said, but very importantly, the whole process the AI used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have 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 able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus one three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending 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 listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to supervise the as time goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. It then finds the publication date and the search results. It then is another search query. It’s going to click into the post. And all of this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think this really the shape of something that we should expect to much more common in the future, where we have and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able to impossible problems.
And to give 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 years with VisiCalc. I don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the data right here. let me show you the ChatGPT take on how to a data set like this.
So we can give access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI is to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. see that three 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 we were on an exponential and it dropped off the cliff. could be going on there? By the way, all this Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This slide shows a parable of 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 veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not happened otherwise. I think this is something we should reflect on, think about as we consider how to these 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 how we want it to slot in, that’s for the rules of the road, for 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 from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a 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 the way 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 things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up with this technology 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 compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really about like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only see the things did. And 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 need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models that meant that if you continue invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot of things, one person was working on training a model to the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time that you saw 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 this helps explain the that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But 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 mind that you just 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, means it’s really learned an internal circuit for how to 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 you can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece the 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 our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is sort of the integration the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we this step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. We have to build up a record with these machines that they’re able to actually carry out intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know 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 the expansion of the scale and the human feedback that you talked about basically going to take it on that journey of getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has 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 saying X is going to happen, is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one something like that is what you need. But I that our approach has always been, you’ve got to to the limits of this technology to really see it in action, because that you 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 quite a controversial you’ve taken, that the right way to do this is to put it out in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your 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 forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think that this approach is the only other path that I see, which is that you do let hit you in the face. And I think you give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box 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 one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll you a story 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 people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on line in a much more real way than any of us typing things in computers and developing technology 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 quite playing field as it truly lies. Like, if you look the whole history of computing, I really mean it when I say that is an industry-wide or even just 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 still making faster computers, we’re improving the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to tear us down. Is that basically 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 we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to 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 we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)