We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an period right now where we as a world are going to a technology that will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you current state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show you is what it’s to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands the power what it can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop here that says “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 prompt just like human could. And so you sort of have this to inspect how the machine is using these tools, which allows us provide 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. You say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of away all those details from you. So you don’t have be the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected 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 list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through it sort of modify the actual quantities. And that’s something that I 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 our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of 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 to the slides. Now, the thing about how we build this, it’s not just building these tools. It’s about teaching the AI how use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we use an 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 teach through feedback. Have a human teacher who provides rewards and punishments as it tries things out and does that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this not just 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. It allows it to teach, sort of infer your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have 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 to able to teach students wonderful things. Only one problem, 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 it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of where you 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, high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re doing is inspecting the floor, don’t know 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 same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. one is a browsing tool where the model can issue queries and click into web pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it 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 collaboration between a human an AI. Because a human, using this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I this really shows the shape of something that we should expect to be much common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this process right, we will 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 interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the data right here. let 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 run code, just like a data scientist would. And so 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 information here is the name of the file, the column names like you saw and then actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s for a human to do, and the AI is to help with it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots intent behind it. But I don’t even know what want. And the AI kind of has to infer I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of papers 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, a nice bell curve. You see three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What could be going on there? By the way, this is 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 unhappy about this 2023 thing. makes this year look really bad. Of course, the problem that the year is not over. So I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it 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 we … A vision of how we may end up using this technology the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that 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 reflect on, think about as we consider how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to literate. And that’s, honestly, one 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 mean … suspect that 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 you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the way that we 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 is just the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands 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 on shoulders of giants, right, there’s no question. If you look at the 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 the first one was just to confront reality as lays. And that 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 things that did. And I think that the most important has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these language models that that if you continue to invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just feels impossible that that 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 the time, ant colonies, single ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, 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 more atoms than are in the universe. So it had to have learned something general, but that it hasn’t fully 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 an incredible number of pieces of text. And it is learning things that you didn’t know 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 is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with machines that they’re able 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 like making the machine be aligned with you.
CA: we’re going to hear later in this session, there critics who say that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of the scale and the human feedback that you about is basically going to take it on that journey of actually getting to things like and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality hit in the face, right? It’s like this field is the field of broken promises, of all these saying X is going to happen, Y is how it works. have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach always been, you’ve got to push to the limits of technology to really see it in action, because that tells then, oh, here’s how we can move on to new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and forth are all scrambling to catch up. And some 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 you have done is responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always to get it right. But one thing I think been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” you hope you got it right. I don’t know how 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 reality hit you in the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen it 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 one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought for you. Suppose 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 chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically that Pandora’s box to be five away 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 five years away. But if gets to be 500 years away and people get more to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course do the 500 years. My brother was in the at the time and like, he puts his life 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 the you’ve got to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being like a 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. so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to 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 we birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for 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 to take each step as encounter it. And I think it’s incredibly important today that we all do get in 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 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)