We started OpenAI years ago because we felt like something really interesting happening in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far whole field 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 wonderful things. We hear from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels 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 that we can manage this for good.
So today, I want to you the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going show you is what it’s like to build a for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what it can on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This all generated by the AI as we speak. So actually don’t even know what we’re going to see. This looks wonderful.
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I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You 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, is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. so you sort of have this ability to inspect the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and to with other applications too. You can say, “Now make a shopping 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 is selecting all different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app as long you kind of know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected 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 thing that’s really interesting that the traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been for 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 change the work of the AI if we want to. And after this talk, you will be able to access 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 the AI how to use them. Like, what do even want it to do when we 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 machine, like a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it all 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, is to actually the math problem.
But we actually have to do a second step, too, which is to the AI what to do with those skills. And for this, we feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s 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 this allows to generalize. It allows it to teach, to sort infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we first showed GPT-4 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 with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine alongside our team. over the course of a couple of months we were able to the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we really listen our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually does the search. then it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And all of you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in 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 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 interesting to me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI to more useful to a human. And I think this really shows shape of something that we should expect to be more common in the future, where we have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show the ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, know, it knows the name of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here the name of the file, the column names like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are values and so therefore it’s a number of authors the paper,” like all 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 want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of to infer what I might be interested in. And so it comes up some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three is 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 an exponential and 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. you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out of machine here. I really wanted it to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how may end up using this technology in the future. A person brought his very sick to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to save 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 brainstorming was able to achieve an outcome that would not have happened otherwise. I think this something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. And that’s deciding 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 away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, much every single thing about 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 way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a 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: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from early days. And the first one was just to reality as it lays. And that we just thought really hard about like: What is it going take to make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think that the most thing has been to get teams of people who are very different from other to work together harmoniously.
CA: Can we have 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 the fact that you something in these language models that meant that if you continue to in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one person working on training a model to predict the next in Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the idea of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned 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. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you scale up, things 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 truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I one thing people miss, too, is sort of the integration with world is also 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 I 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 easy to at that math problem and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you know this book summary is any good? You have to read the 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 that we say, OK, we move on to book summaries, we have to supervise this task properly. have to build up a track record with these machines that they’re able to carry out our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and human feedback that you talked about is basically going to take on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short answer is yes, I believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit 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 work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of it and then you push “go,” and hope you got it right. I don’t know how to execute that plan. Maybe someone else does. for 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 that do let reality hit you in the face. And I think you do give people time give input. You do 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 from GPT-3, right? GPT-3, we really were that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful gifts to family 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 this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell you a story 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 over this wonderful water, all these people having a time. And you think about it for a moment, if you choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time to get right, which do you pick? And you know, I just felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, he puts his life on line in a much more real way than any us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I really mean it when I say this is 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 the moment that someone does manage to connect the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for child 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 shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do get literate in this technology, 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 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 blowing minds.
(Applause)