We started OpenAI seven ago because we felt like something really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing see how far this whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, for so many wonderful things. We hear people who are excited, we hear from people who concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define 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 you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show is what it’s like to build a tool for AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. And we go, it’s not just 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 generate in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This 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 looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” the interesting thing about 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, this coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was write a just like a human could. And so you sort of have ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a 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 ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t to be the one who spells out every single sort 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 list of 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 at this, you still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also a very thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of AI if 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 build this, it’s just about building these tools. It’s about teaching the AI how use them. Like, what do we even want it do when we ask these very high-level questions? And do this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have a human teacher who provides 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 what Turing would have a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that green nine up there, to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to produce that answer. And this it 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 received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If 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 very kind and offered 20 of his own time to provide feedback to the 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 improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up a bat to 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 to our and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model can issue search queries and into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going click into the blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want to do. It’s much more fun to be the driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months one week, that was 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 a human, using this fact-checking tool doing it in order to produce data for another AI become more useful to a human. And I think this really the shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I think that time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve been in 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 all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to run code, just like 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 the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names you saw and then the actual data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things are and that these are integer values and therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. the AI kind of has to infer what I might interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be pretty to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, problem is that the year is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it does 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 to slide again. This slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we 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 will and won’t do. And if there’s thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of 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 a feeling reeling. Like, I suspect that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to 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, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, the 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 lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. And that just thought really hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so only see the things that did. And I think the most important thing has been to get teams people who are very different from each other to together harmoniously.
CA: Can we 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 about the fact you saw something in these language models that meant that if you to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, didn’t know. We tried a lot of things, and person was working on training a model to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a 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 this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just stuff you showed us just now. And the key of emergence is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants 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. as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that 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 it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if have it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And 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 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 yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And to do that actually, of the things I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, tolerance has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If 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 predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, how do you know if book summary is any good? You have to read the whole book. No one wants to that.
(Laughter) And so I think that the important will be that we take this step by step. that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually out our intent. And I think we’re going to have to produce even better, efficient, more reliable ways of scaling this, sort of like making the machine be aligned 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 sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going take it on that journey of actually getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one or something like that is what need. But I think that our approach has always been, you’ve got to push to the limits of this to really see 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 controversial stance you’ve taken, that the right way to do this is to put out there in public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable was capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the 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 put this out here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and not reckless.
GB: Yeah, think about 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 been incredibly important, the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And 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 someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is you do let reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a time. And you think about it for a moment, if you could 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 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 felt it the moment. I was like, of course you do the 500 years. brother was in the military at the time and like, he puts life on the line in a much more real than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the history of computing, I really mean it when I that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces 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 put them together, get an overhang, which means that if someone does, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think if you look at capability, it’s been quite smooth 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 it for each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity a whole 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 think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get in this technology, figure 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 this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)