We started OpenAI seven years ago because we felt something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are the technology we are building, and others, for so many things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as world are going to define a technology that will be so important our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to you is what it’s like to build a 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 on your behalf. And you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that 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 live demo. This all generated by the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let me you what it’s like to use that information and to with other applications too. You can say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort of take all 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 a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click through it sort of modify the actual quantities. And that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we 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 back 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 we ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can it. You could build a machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it tries out and does things 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 the whole world, the whole internet and say, “Predict 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 math problem, the only to actually complete that math problem, to say what comes next, that green nine up there, is actually solve the math problem.
But we actually have do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and it in scenarios that 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 showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking a 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 the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised 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 better time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where 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 and it actually does the search. It then it the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s much fun to be in 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 can actually go and very easily verify any piece of this chain of reasoning. And it actually turns out two months 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 about this whole process is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order produce data for another AI to become more useful to human. And I think this really shows the shape something that we should expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I that over time, if we get this process right, we will able to solve impossible problems.
And to give you sense 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 been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me show you the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name 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 of the file, the column names like you saw 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 world knowledge of knowing that, “Oh yeah, arXiv is a 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 to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the kind of has to infer what I might be interested in. And 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 pretty interesting to see. And the great is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this look really bad. Of course, the problem is that year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable how I think we … A vision of how may end up using this technology in the future. person brought his very sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the 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 to a second vet who used it 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 a human with medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is something 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 is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, 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. I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. And I that the most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this 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 showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things that didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one 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, it’s capable of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these questions of degree and scale 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 the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, we move on to book summaries, we have to this task properly. We have to build 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 even better, more efficient, more reliable ways of scaling this, sort of like making the machine aligned 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 that it’s not generating errors, that 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 scale and the human feedback that you talked about basically going to take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going to happen, is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were 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 kind what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” you hope you got it right. I don’t know how to execute 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 other path that I see, which is that you do reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be five years away. if it gets to be 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, puts his life on the line in a much more real way than any of us things in computers and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I 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 just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that thing I take away is like, even you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of 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 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 a whole new place. is our collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. 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 it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)