We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear from who are 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 historic period right now where we a world are going to define a technology that be so important for our society going forward. And I believe we can manage this for good.
So today, I want to show you the state of that technology and 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 build a tool an AI rather than building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you that you out of ChatGPT. And here we go, it’s not just idea 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 this — sorry, it doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take all those details from you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so the unexpected 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 a of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? If look at this, you still can click through it sort 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. 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 to change the work of the AI if we 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 to 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 even want it to do when ask these very high-level questions? And to do this, we 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 human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, say what comes next, that green nine up there, to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we 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, doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually 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 weakness where you 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 about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching them to stuff all toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we actually use the AI to fact-check. And it can check its own work. You can say, fact-check this me.
Now, in 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 of thought it does it. It says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any piece 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 back to the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to data for another AI to become more useful to human. And I think this really shows the shape of something we should expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they into a problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we 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 able to rethink almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been 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 here is specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And you 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 for you.” The only here is the name of the file, the column names you saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. has to 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 AI is to help with it.
Now I don’t even know what I want ask. So fortunately, you can ask the 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 even know what want. And the AI kind of has to infer I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential 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 word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is 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 April 13?] So 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional piece of, you know, guidance. And the 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, even updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save 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 professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to 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 that this technology just looks different. different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons 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. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s also really scary. 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 who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the compute progress, algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to confront reality as it lays. And we just thought really hard about like: What is it going 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 thing has to get 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? I we’re going to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in these language that meant that if you continue to invest in them and grow them, that 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 level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, we didn’t know. We tried a of things, and one person was working on training a to predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive negative. I mean, today we are just like, come on, can do that. But this was the first time that you this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out 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 you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, means it’s really learned an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to have something general, but that it hasn’t really fully yet 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 scale and look at an incredible number of pieces of text. And it is learning things that didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we 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 have get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some 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, one of big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there just 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 world 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 incrementally. And so I think that what we kind of see right now, if you look at talk, a lot of what I focus on is providing really high-quality feedback. Today, 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 the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is any good? have to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this by step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, know, there’s no real understanding inside, the system is to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think that OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is 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 to work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or like that is what you need. But I think that approach has always been, you’ve got to push to the limits of this technology to see it in action, because that tells you then, oh, here’s how we can move to a 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 to do this to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is 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 the big doing their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable was capable of slowing the field down, if need be. at 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 catch up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about 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 thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is you do let reality hit you in the face. And think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over this water, all these people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On one hand 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 people get time to get it right, which do you pick? you know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the and like, he puts his life on the line in a much more way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, really mean it when I say that this is 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 pieces are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you 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 you suddenly this very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, in what humans could do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every we’ve developed has 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 have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to wise and not to tear us all down. Is that basically the model?
GB: 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 technology, figure out how to provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, you so much for coming to TED and blowing minds.
(Applause)