We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear from people 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. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a are going to define a technology that will be so important for our society going forward. And I that we can manage this for good.
So today, I want to show the current state of that technology and some of underlying design principles that we hold dear.
So the first thing I’m to 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 as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And 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 get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what actually did was write a prompt just like a could. And so you sort of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like use that information and to integrate with other applications too. can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click between them, we copy/paste between them, usually it’s a great experience within an app as long as you kind know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re at it. And you see 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 look at this, you still can click it and sort of modify the actual quantities. And that’s something that I think that they’re not 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 click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after talk, you will be able to access this yourself. And 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 how to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and 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 Turing would have called a machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next text you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area weakness 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 for 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 inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where 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 to for this and it actually does the search. It then finds the publication date and the search results. It then issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. 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 cut back the side. And so thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And think this really shows the shape of something that we should expect to be much common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit into problem and how we want to 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. I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we 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 they’ve really changed that much in that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let me show the ChatGPT take on how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it the name of the file 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 data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these are and that these are integer values and so 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 help with it.
Now I don’t even know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even what I want. And the AI kind of has to what I might be interested in. And so it up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it 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. under the hood, the AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it 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 a parable of how I think we … A vision of how we end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would be here today had he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a partner was able to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take away 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 we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am right? Who 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 just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on 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 on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. And that we 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 that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can have the water, by 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 the fact that you saw something in these language that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the days, we didn’t know. We tried a lot of things, and person was working on training a model to predict next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, know, the model will predict where the commas go, where 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 was 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 from underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to adding arbitrary numbers arbitrary 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 learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at predicting some of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out 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 later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X going to happen, Y is 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 right maybe 70 plus one or something like that is what you need. I think that our approach has always been, you’ve 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 on to a 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 is to it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I you do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you choose for 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 away. But if it gets to be 500 years away and people get more time to it right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, puts his life on the line in a much more real than any of us typing things in computers and developing this technology the time. And so, yeah, I’m really sold on 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 at the history of computing, I 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 that are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero 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. And so the history, think, of every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And my hope is that that continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)