We started seven years ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come since then. it’s really gratifying to hear from people like Raymond who are using the we are building, and others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be important for our society going forward. And I believe we can manage this for good.
So today, I to show you the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI than building it for a human. So we have new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and care of the details for you that you get 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 to 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 of what it can do your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was write a just like a human could. And so you sort have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away 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 will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent list 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? you look at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented way 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,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing about how we 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? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does that are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it all 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, that green up there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to 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.” so when you do that, that’s one way that we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to clean their room, 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 DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for 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 queries and click into pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date and the search results. It is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process 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 for another AI to become more useful to a human. And I think this really shows shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names like you saw and then actual data. And from that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But 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 histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three is of the most common. It’s going to then make nice plot of the papers per year. Something crazy 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, all this is Python code, you inspect. And then we’ll see 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 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. percentage of papers in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A of how we may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to save the dog’s life. Now, systems, they’re not perfect. You cannot overly rely on them. But story, I think, shows that a human with a medical professional with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new 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 also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: is it going to take to make progress here? tried a lot of things that didn’t work, so you see the things that did. And I think that the most thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that saw something in these language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you us just 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 enough of them together, you these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind 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 it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but it hasn’t 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 learned something general, but it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So science that we’re starting to really get good at is predicting some 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, had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single of the stack engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. 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: here is, one of the big fears then, that 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. Why isn’t there just a risk of something 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 the world 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 I think that what we kind of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with these that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real 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 sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People been saying neural nets aren’t going 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 really it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is put it out there in public and then harness all this, you know, of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil with AI. And you were going to build models sort of, you know, somehow held them accountable and capable of slowing the 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 through the tech world that now Google and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done is responsible and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I think that this approach is the only other path that I see, which is that you do reality hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to with it was 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 are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually a one percent thing in the small print there that says: “Pandora.” And there’s a that this 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 a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 years and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the at the time and like, he puts his life on the line a much more real way than any of us typing in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if you look at the whole 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. the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this 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 I take away like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically the model?
GB: I think it’s true. And I 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 incredibly important that we all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and our minds.
(Applause)