We started OpenAI seven ago because we felt like something really interesting was happening in AI and we wanted help steer it in a positive 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 are building, and others, for so many wonderful things. We from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where as a world are going to define a technology that will be important for our society going forward. And I believe that we manage this for good.
So today, I want to you the current state of that technology and some of underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to build a tool for an AI rather than it for a human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, ideation creative 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 for the meal, but a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the 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 by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a just like a human could. And so you sort have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within app as long as you kind of know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of take away all details from you. So you don’t have to be the who spells out every single sort of little piece 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 shopping list we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. the thing that’s really 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 think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can “run,” and there we 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 able to this 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 the AI how to use them. Like, what do we want it 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 learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments as it things out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have called child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But actually have to do a second step, too, which to teach the AI what to do with those skills. And for this, provide feedback. We have the AI 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 the specific thing that the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So we had collect some feedback data. Sal 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 to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. 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 a browsing tool where the model can issue search and click into web pages. And it actually writes out its chain of thought as it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. out come citations so you can actually go and easily verify any 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 cut back to side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able to rethink every aspect 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 think they’ve really changed that much 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 right here. But me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column names you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of to infer what I might be interested in. And so it comes up with some ideas, I think. So a histogram of the number authors 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 great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be going on there? By the way, all is Python code, you can 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 bad. Of course, the problem is that the year not over. So I’m going to push 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 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. now, it does the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision how we may end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way that 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 is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. the 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 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 teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that saw something in these language models that meant that you continue to invest in them and grow them, 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 that high level, deep learning, like we knew that was what we wanted to be, was deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot 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 predict 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 positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this syntactic 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, these things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, you can try this 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 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 table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t fully yet learned 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 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 more nuanced, too. So one science we’re starting to really get good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung in this is sort of engineering 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 get every single piece of the stack engineered properly, then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level 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 I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very powerful 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 of see now, if you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at 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 have to the whole book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re to actually carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later 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 one moment, but that the of the scale and the human feedback that you about is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the 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 you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in public and harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well 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 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 I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and so are all scrambling to catch up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, is that you do let reality hit you in face. And I think you do give people time to give input. do have, before these machines are perfect, before they super powerful, that you actually have the ability to see in action. And we’ve seen it 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 one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe that in that box something that, there’s a 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 that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this wonderful water, all these having a 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 hand you’re like, well, maybe for personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real way than any of us things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at whole 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 more that you sort of, don’t put together the that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which means that someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people talk about being like a zero 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 history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to tear us all down. Is 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 as we encounter it. And think it’s incredibly important today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And 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 it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)