We started OpenAI years ago because we felt like something really interesting was happening in and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right now where 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 that and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to build a tool an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED and 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 of ChatGPT. And we go, it’s not just the idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what can do on your behalf in terms of carrying 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 looks wonderful.
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I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was a prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app as long you kind of know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the is able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, 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. Have human teacher who provides rewards and punishments as it tries things and does things that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, is to teach the AI what to do with those skills. for this, we provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down in ChatGPT, that is kind of like sending up 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 to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model 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. But we actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. It says, I’m just going to for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very tedious task. It’s not a 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 turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration 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 useful to human. And I think this really shows the shape something that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem and how want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of the AI 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 take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, it 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 names like you saw and the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has sort of, put 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 that these integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. 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 up with good ideas, I think. So a histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like 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 word cloud. So you can see all wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, it does correct 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 of how may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe deeply, is that getting AI right is 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 and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the 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 there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to the way that we do 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 how hell have you done this?
(Laughter)
OpenAI has 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 question. If you look at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just really hard 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 think that the important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we have 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 just about the fact that you saw something in these language models that meant that if continue to invest in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried a of things, and one person was working on training a to predict 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 model could tell you a review was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, this sort of 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: I think this helps explain the riddle that baffles looking at this, because these things are described as 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 run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you just something pop that just blew your mind that you just did 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 do it. the really interesting thing is actually, if you have 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 the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have learned 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 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 didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. We basically look at models 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 then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale timing. And I think one thing people miss, too, sort of the integration with the world is also this emergent, sort 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 that what we kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy 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 thing to supervise. Like, how do you know if book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. have to build up a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So 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 have sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I that 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 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 been right yet. They be right maybe 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that you then, oh, here’s how we can move on 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 way to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think been incredibly important, from the very beginning, when we were 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 of being, well, you in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and hope you got it right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other path that see, which is that you do let reality hit you in face. And I think you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to 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 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 of us typing things in computers and developing this technology 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 the field as it truly lies. Like, if you at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put together the 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 manage connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … model you want us to have is that we birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do literate in this technology, figure out how to provide feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)