We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be important for our society going forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for an AI than building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that something that really expands the power of what it can 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 we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save for 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 to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. And so you sort of have ability 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 use that information and integrate with other applications too. You can say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. 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 within an app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort take away all those details from you. So you don’t have be the one who spells out every single sort little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent list of ingredients to 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 of modify the actual quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, way to build them. And now we have 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 to change the work of the AI if we want to. And so this talk, you will be able to access this yourself. 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 about these tools. It’s about teaching the AI how to use them. Like, what do we want it to do when we ask these very high-level questions? And to do this, use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, 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 with those skills. And for 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 better than one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, 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 we showed GPT-4 to Khan Academy, they said, “Wow, this is 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 in there, it will pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the machine alongside our team. over the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move 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 happy to us provide even better feedback and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into web pages. it actually writes out its whole chain of thought 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 of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns out two was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I this really shows the shape of something that we should expect to be more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think over time, if we get this process right, we be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been 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 here is a specific spreadsheet of all AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And you can just literally upload a file and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is name of the file, the column names like you and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are 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 human to do, and the AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. I don’t even know what I want. And the kind of has to infer what I might be interested in. And so it comes up 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 that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and it dropped the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back 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 use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect 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 ask that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And dog would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will 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 anything had anticipated. And so we all have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new 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 really 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 has of employees working on artificial intelligence. Why is it you who’s up with this technology 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 the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it going take to make progress here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the most thing has been to get teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and how to 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 model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time that you 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: I 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 of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that you 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 do it, which means it’s learned an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize to 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 was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just a 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 integration with the world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to 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 to supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re to actually carry out our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to later in this session, there are critics who say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback you talked about is basically going to take it on 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, I mean, short answer is yes, I believe that is where we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. But I think that our has always been, you’ve got to push to the of this technology to really see it in action, because tells 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 quite a controversial you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and capable of slowing the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and so are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do give time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. I was like, of you do 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 of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole history of computing, I mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still improving 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 moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had 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 of technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, in what humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to tear all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide the feedback, decide what we want it. And my hope 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 if it weren’t there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)