We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it like 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 can this for good.
So today, I want to show you the current state of that technology 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, and we are exposing it as app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is that really expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all a live demo. This is generated by the AI as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, 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 up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can look under the and see that what it actually did was write a prompt like a human could. And so you sort of 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 show you what it’s like to that information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s great experience within an app as long as 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 of tools, the AI is able to sort of away all those details from you. So you don’t have to be the 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 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 still very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut 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 use them. Like, what do even want it to do when 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 Turing test, he says, you’ll never program an answer to this. Instead, you learn 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 things out and does 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 unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all of 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 up there, is to actually 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 out things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when 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 some bad math there, it will happily pretend that one plus one equals three and run it.” So we had to collect 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 teach the that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re building something that’s 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 the floor, you don’t know if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool where the can issue search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just going to search this and it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s to click into the blog post. And all of you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s more fun to be in the driver’s seat, to in this manager’s position where 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 out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more to a human. And I think this really shows the shape of something that should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make that the humans are 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 over time, if get this process right, we will be able to impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT on how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, the column like you saw and then the actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s number of authors in 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 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 lots of intent behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. And it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it actually do it. Here we go, a nice bell curve. You see 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. could be going on there? By the way, all this is Python code, you inspect. And then 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 year look really bad. Of course, problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can 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 of the machine here. I really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, if you want to inspect what 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 for that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think we … A vision how we may end up using this technology in future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits all 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 number of people viewing this, you look at that and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. I think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, you only see the things that did. And I think the most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that if continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve to scale this thing, you’ve got to see where it goes.
CA: 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 that could from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you you saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but 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 something general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t know that it 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 predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at 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, example, the performance on coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that say, OK, as we move on to book summaries, have to supervise this task properly. We have to build a track record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and human feedback that you talked about is basically going take it on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. I think that our approach has always been, you’ve got push 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 just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance 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 your giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions the 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 the very beginning, when we were about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out the safety of and then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this alternative approach is the only path that I see, which is that you do let reality hit you in face. And I think you do give people time give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things 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 box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s also a one percent thing in the small 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 you don’t do it that way. And honestly, like, I’ll tell you a story that 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 over wonderful water, all these people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time to it right, which do you pick? And you know, just really felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time like, he puts his life on the line in a much real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the whole history 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. the more that 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 these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what 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 technologies, think about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be the best path, 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 coming to TED and blowing our minds.
(Applause)