We started seven years ago because we felt like something really interesting happening in AI and we wanted to help steer in a positive 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 people like Raymond who are using the we are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear 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 a world are going to define a technology that will be so for our society going forward. And I believe that 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 going show you is what it’s like to build a for an AI rather than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is something that really the power of what it can do on your in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did write a prompt just like a human could. And so sort of 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 what it’s like to use that information and to with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these different tools without me having to it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know menus and know all the options. Yes, I would you 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 the one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look 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. the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And so this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper 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 punishments as it tries things out does things that are either good or bad.
And this is exactly how 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 it the whole world, whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to what comes next, that green nine up there, is 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 provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it to generalize. It allows it 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 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. If there’s some bad in there, it will happily pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so 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 everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says 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 can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This one is browsing tool where the model can issue search queries and click into web pages. it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date and the search results. It then is another search query. It’s going to click into the blog post. And all of this 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. And out citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so 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 is doing it in order to produce data for another AI to more useful to a human. And I think this really shows shape of something that we should expect to be more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to 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 just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact 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 is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the column names like 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 to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and 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 help with it.
Now I don’t know what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what I might interested in. And so it comes up with some ideas, I think. So a histogram of the number authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three is kind of most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? 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. It makes this look really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect 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 the slide again. This slide shows a parable of how think we … A vision of how we may up using this technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, you need to talk a professional, here are some hypotheses.” He brought that information to a second 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 professional and with ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just different from 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 can achieve OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, 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, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, truth is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? We tried a lot of that didn’t work, so you only see the things did. And I think that the most important thing has been to get of people who are very different from each other work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there 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 some might emerge?
GB: Yes. And I think that, I mean, honestly, think 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, exactly how to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training a model to predict the next in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if review was positive or negative. I mean, today we just 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 we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible 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. happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. it had to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. 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 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 we move on to summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways of scaling this, sort like making the machine 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 going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I think that the OpenAI approach here has been just like, let reality hit you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits of this to really see it in action, because that tells then, oh, here’s how we can move on to a new paradigm. 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 put it there in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held accountable and 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 tech world that now Google and 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 done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when we were thinking about how build 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 in secret, you get this powerful thing, and then you figure out the safety it and then you push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only other path that see, which is that you do let reality hit you 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 to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it was 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 a room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” 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 don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get more time to get it right, which you pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the military 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. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing the field as it lies. Like, if you look at the whole history of computing, I really it when I say that this is an industry-wide 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 if you don’t put them together, you get overhang, which means that if someone does, or the that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing 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 provide the guardrails for this 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 step as we encounter it. And I think it’s incredibly important today that 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 to be the path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)