We started OpenAI years ago because we felt like something really interesting was in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now we as a world are going to define a technology will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal draw a picture 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 you get out ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually was write a prompt just like a human could. so you sort of have this ability to inspect how the is using these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like use that information and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s great 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 be the who spells out every single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching the how to use them. Like, what do we even want to do when we ask these very high-level questions? And to this, we use an old idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, the only way to actually complete math problem, to say what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, 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 math there, it will happily pretend that one plus one three and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his time 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 specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking kid to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI 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 and it actually does the search. It then it the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. 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 fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out come citations so can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I think this shows the shape of something that we should expect to be more common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve 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 we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information is the name of the file, the column names like saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an and it dropped off the cliff. What could be 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 things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under the hood, the is 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 the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to 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, think, shows that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at 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? thinks that they’re having to rethink the way that 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 question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with this that 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 the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just really hard about like: What is it going to to make progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the important thing has been to get teams of people who are very different from each to work together harmoniously.
CA: Can 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 just about the fact that you saw something in language models that meant that if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today we are 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 got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that just your 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, which it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really 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 are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And it is things that you 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 is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth 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 to predict, for example, the performance on coding problems. We look at some models that are 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 big then, that arises from this. If it’s fundamental 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 huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very 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 thing to supervise. Like, do you know if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have supervise this task properly. We have to build up a record with these machines that they’re able to actually 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 the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that 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, 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 hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going 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 might be right 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to to the limits of this technology to really see it in action, that tells 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 right to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build models that of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put this out without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going 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 of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this super powerful thing, and then figure out the safety of it and then you “go,” and you hope you got it right. I don’t 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 alternative approach is the only other path that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. do have, before 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 people were going to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely 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, I I was in Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to 500 years away and people get more time to get it right, do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line a much more real way than any of us typing in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it when say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that 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 put them together, get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of precautions you get. And so I think that one thing I away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in humans could do. But I actually think that if look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to out how to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively 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 say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want from it. And my 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 much for coming to TED and blowing our minds.
(Applause)