We started OpenAI seven years ago we felt like something really interesting was happening in AI and wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are 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, it like we’re entering an historic period right now where we as a world are to define a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to show you current state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. 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 like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that is something that really the power of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write prompt just like a human could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way 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 of know menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this 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 of what’s supposed to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really 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, 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 a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do we even want to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Alan Turing’s 1950 paper the 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. Have human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete math problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a 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 multiple things, us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback to the machine alongside our team. over the course of a couple of months we able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an 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 for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to 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 like this, of how much time between these two 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 we provide some feedback. we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search this and it actually does the search. It then it the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And come citations so you can actually go and very verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI to become more to a human. And I think this really shows the shape of that we should expect to be much more common the future, where we have humans and machines kind of very and delicately designed in how they fit into a problem and how we to solve 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 create more trustworthy machines. And I think that over time, we 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 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 is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s 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 name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, column names 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 to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are integer values and therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, nice bell curve. You see that three is kind the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use that to make fair 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 really wanted it to notice this thing, it’s a little bit of an overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I think we … A of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who used 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 was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to in, that’s for setting the rules 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 become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink 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 just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And first one was just to confront reality as it lays. And we just thought really hard about like: What is going to take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important thing has 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 we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in language models that meant that if you continue to invest in and grow them, that something at some point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how do 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 got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you just 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 do it, which means it’s really learned an internal for how to do it. And the really interesting is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it 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 than there are in the universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I can sort of 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 of pieces text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild 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 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 about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, emerge that 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 all these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that math problem be like, no, no, no, machine, seven was the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take step by step. And that we say, OK, as we on to book summaries, we have to supervise this task properly. have to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common 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 things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, 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 the field of broken promises, of all these experts saying is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see 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 controversial you’ve taken, that the right way to do this is to put it out there in 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 there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, 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 of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to this out here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of and then you push “go,” and you hope you it right. I don’t know how to execute that plan. Maybe someone else does. But for me, that 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 you in 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, you actually have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you 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 AI conference. I’m sitting in hotel room just looking out over this wonderful water, all people having a good time. And you think about for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. if it gets to be 500 years away and people more time to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you the 500 years. My brother was in the military at time and like, he puts his life on the line in a much more real way than any us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the history of computing, I really mean it when I that this is an industry-wide or even just almost 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 still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety you get. And so I think that one thing I take away like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to 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 we encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that that continue to be the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)