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 far this whole field has since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right where we as 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 show you 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 tool for an rather than building it for a human. So we a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all live demo. This is all generated by the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this 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 human could. so you sort of have this ability to inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate with other applications too. can say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so 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 as you kind of know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these very high-level questions? to do this, we use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a machine through an unsupervised learning process. We just show it the whole world, the internet and 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 math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t 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 GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to able to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the course of a 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 of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when you do that, that’s one that 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, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys 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 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 to 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, of how time 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 feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to into the blog post. And all of this you could do, 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, if want, triple-check the work. And out come citations so you can actually go and very easily any 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 so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order 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 much more in the future, where we have humans and machines of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, we be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able to rethink almost aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the of the file, the column names like you saw then the actual data. And from that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is site 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 work for a human do, and the AI is happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And the AI of has to infer what I might be interested in. And it comes up with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it 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. So you can see all wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year really bad. Of course, the problem is that the is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how think we … A vision of how we may end up using technology in the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI 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, one the reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having rethink the 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 my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we thought really hard about like: What is it going to take make progress here? We tried a lot of things didn’t work, so you only see the things that did. And I think that the most important thing has to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I 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 how to it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could tell you if review was positive or negative. I 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 this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that baffles everyone looking at this, 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 key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens all the time, ant colonies, single ants 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 number of houses, emerge, like suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the 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 number a 35-digit number, it’ll often get it wrong. And so you can 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 than there are in the universe. So it to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an number of pieces of text. And it is learning things that you didn’t know 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 get good at is predicting some these emergent capabilities. And to do that actually, one the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And so 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, the 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 answer. But 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) And I think that the important thing will be that we this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never 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 one moment, but that the expansion of the scale the human feedback that you talked about is basically going to it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. But I think that our approach has 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 can move on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you going to build 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 what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only path that I see, which is that you do let hit you in the face. And I think you do give people to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, he puts his on the line in a much more real way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at whole history of computing, I really mean it when I say that is an 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 improving 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, or moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. And so I think one thing I take away is like, even you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure out to 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 child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I think it’s important today that we all do get literate in technology, figure out how to provide the feedback, decide 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 so much for coming to TED and blowing minds.
(Applause)