We started OpenAI seven years because we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using the we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where as a world are going to define a technology that will so 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 principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. 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 these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the 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 like to use that and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all the options. Yes, 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 of take all those details from you. So you don’t have to the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s you need. And the thing 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. And that’s something that I shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now 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 manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then 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 train ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI 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 thing that the AI said, but very importantly, the process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to 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 to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math 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 and 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s one way we really listen to our users and make 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, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time 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 search and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going search for this and it actually does the search. then 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 you could do, it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, be in 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 any piece this whole chain of reasoning. And it actually turns out two months 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 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 think this really shows the shape of that we should expect to be much more common in the future, where we have humans and kind of very carefully and delicately designed in how they into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access 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 helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of file, the 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 information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, is a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of to infer what I might be interested in. And it comes up with some good ideas, I think. a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped the cliff. What could be going on there? By way, all this is Python code, you can inspect. 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 is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And 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 correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This slide shows a parable of I think we … A vision of how we may end using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second vet who used to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows 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 on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, is that getting AI right is going 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 what an AI will and won’t do. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general 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 feeling reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality as it lays. And that we thought really hard about like: What is it going to take to progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has been to get teams people who are very different from each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the 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 got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment 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 saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly 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 city where a few houses together, it’s just houses together. as you grow the 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 your mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have something general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of of text. And it is learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, the tasks that we do, 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 hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to do that.
(Laughter) And so I 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 this task properly. We have to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve to push to the limits of this 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 to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly 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 of what I heard. yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you forcing us to put this out here without proper or we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality 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 actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s a thought experiment 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 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 open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 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. if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life the line in a much more real way than any of typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as 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 that you sort of, don’t together the pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which means that if someone does, the 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 think that one thing I take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. I 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 has been, you’ve got to do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have that take humanity to 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 us all down. Is that basically model?
GB: I think it’s true. And I think it’s also 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 do get literate in this technology, figure out how to provide the feedback, decide what we from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)