We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we hear from who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as world are going 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 the current of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show is what it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a nice post-TED meal 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 here we go, it’s just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something that really expands the power what it can do on your behalf in terms carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here that says “use DALL-E app.” And by 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 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 provide to them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. can 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, I want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to be the one who out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you will be 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 about teaching AI how to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we have to do a second 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 then a human them, says “This one’s better than that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows to generalize. It allows it 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 teach the AI not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we really to our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we 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 for me.
Now, this 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 actually out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does the search. It then it the publication date and the search results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, it’s a very tedious 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 and very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more to a human. And I think this really shows the shape something that we should expect to be much more in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. 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 specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on 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 like a data scientist would. And you can just literally upload a file and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy to with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction with of intent behind it. But I don’t even know I want. And the AI kind of has to what I might be interested in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting to see. And great 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 to then make this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of we may end up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And 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 not a vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and with ChatGPT a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is getting 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 rules of road, for what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you 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 right? Who 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 first question is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you saw something these language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could tell you if a review was or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you 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 and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the interesting thing is actually, if you have it 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 process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here 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 that it 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 of these emergent capabilities. to do that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build 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 better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the expansion of the and the human feedback that you talked about is going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high of confidence. Can you be 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. I think that the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve got to push to the limits of 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 fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way to do this to put it out there in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are forcing us to put this out here proper guardrails or we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you push “go,” you hope you 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 alternative is the only other path that I see, which is that you do let hit you in the face. And I think you do people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You that in that box is something that, there’s a very strong chance it’s something glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a time. And you think about it for a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But it gets to be 500 years away and people more time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the time and like, he his life on the line in a much more real than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the whole of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one I take away is like, even you think about 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 actually think that if you look at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will 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 much for to TED and blowing our minds.
(Applause)