We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for so many wonderful things. We from people who 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. Above all, it feels like we’re entering historic period right now where we as a world 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 to show you the current state of that technology some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest nice post-TED meal and 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 ChatGPT. And here we go, it’s not just the idea the meal, but a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something really expands the power of what it can do on your behalf in terms of out your intent. And I’ll point out, this is a live demo. This is all generated by the as 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 ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was a prompt just like a human could. And so you sort of have this ability inspect how the machine is using these tools, which allows us to provide feedback 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. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make it 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 want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app as as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take all those details from you. So you don’t have be 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 sometimes the will happen to us. But let’s take a look at the shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank 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 tools. It’s about teaching the AI how to use them. Like, do we even 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 the Turing 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 and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this 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 up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach AI what to do with those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of weakness 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 everyone.
Now, providing high-quality feedback is a hard thing. If think about 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 in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing search query. It’s going to click into the blog post. all of this you could do, but it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations you can actually go and very easily verify any 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 the side. 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 a human. I think this really shows the shape of something that we should expect to be more common in the future, where we have humans and machines kind of very and delicately designed in how they fit into a and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this 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 be able to rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT on how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just literally upload a file 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 names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these are 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 want ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t even know what I want. the AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to have sort of, inferred that this is 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 again, so if you want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I think … A vision of how we may end up this technology in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet 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 as brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for the rules of the road, for what an AI and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind 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 thing about the I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to the way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just to confront as it lays. And that we just thought really hard about like: is it going to take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if you to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that from this underlying syntactic 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 the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one moment 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 will do it, which means it’s really learned an internal circuit for how to do it. the really interesting thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get 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 memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, that it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number pieces of text. And it is learning things that didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental 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 were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you 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 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 thing to supervise. Like, how do you know if this book summary any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And we say, OK, as we move on to book summaries, have to supervise this task properly. We have to up a track record with these machines that they’re able actually carry out our intent. And I think we’re 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 to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion the scale and the human feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. I think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s we can move on to 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 this is to put it out there in public and harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or least that’s kind of 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 that Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, 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 I don’t think we’re going to get it right. But one thing I has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? And 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 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 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. You have, before these machines are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to 500 years away and people get more time to get right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the military at the and like, he puts his life on the line a much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look the whole history of 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 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, they happening. And if you don’t put them together, you an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans do. But I actually think that if you look 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 incrementally and you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say this may shift, right? We’ve got to take step as we 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 want from it. And my is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)