We started seven years ago because we felt like something really interesting was in AI and we wanted to help steer it in a direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We from people who are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. honestly, that’s how 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 will be so important for our society going forward. And I believe that can manage this for good.
So today, I want to show you current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is what it’s to build a tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app for 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 of the, sort of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, 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. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about 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 was write prompt 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 me you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able 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 will to us. But let’s take a look at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you can click through it and sort of modify the quantities. And that’s something that I think shows that they’re going away, traditional UIs. It’s just we have a new, augmented way to them. And now we have a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after 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 important thing about we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even it to do when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and things that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to do with those skills. And this, we provide 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.” And reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that 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. one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So we had 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 couple of months we were able to the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to users and make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is 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 to all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But 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 can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. out come citations so you can actually go and very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI become more useful to a human. And I think this really shows the shape of that we should expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to give you sense of just 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 specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to a 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 like data scientist would. And so you can just literally upload file and ask 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 for you.” The only information here is the name of the file, the column names you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I want to ask. 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 behind it. I don’t even know what I want. And the AI kind of has to infer what I be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors paper, time series 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 actually do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in 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. What percentage of papers in 2022 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how think we … A vision of how we may end using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, that a human with a medical professional and with 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 into our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so we all to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can 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 feeling of reeling. Like, suspect that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty every 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 to the way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re 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 lot of very 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: What is going to take to make progress here? We tried lot of things that didn’t work, so you only see the that did. And I think that the most important has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in language models that meant that if you continue to invest them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, we always knew that was what we wanted to be, a deep learning lab, and exactly how to do it? think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a model to predict next character in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort semantics that emerged from this underlying syntactic process. And 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 prediction machines. yet, what we’re seeing 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 you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like 40-digit number plus 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 generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it had to have 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 here is you’ve allowed it to scale up and look at incredible number of pieces of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of the 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, every tolerance has to be tiny. Same is true in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can 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 summarizing a book, like, that’s a thing to supervise. Like, how do you know if book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going 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 the and the human feedback that you talked about is going to take it on that journey of actually getting things like 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 where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. 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 how can move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make case that what you have done is responsible here and 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. But one I think has been incredibly important, from the very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, then you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know how to that plan. Maybe someone else 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 I see, is that 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, we really were afraid that the number one people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also one 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 tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all people having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, 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 right, which do you pick? And you know, I just felt it in the moment. I was like, of course you do 500 years. My brother was in the military at the and like, he puts his life on the line in 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 approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look the whole history of computing, I really mean it I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that 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, which means that if does, or the 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 about development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort 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 has been, you’ve to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, decide what we want 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 if weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)