We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so many wonderful things. hear from people who are excited, we hear from who are concerned, we hear from people who feel both emotions at 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 a technology that will be important for our society going forward. And I believe that we can manage for good.
So today, I want to show you current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you 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 things ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And we 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. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And 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 you can under the hood and see that what it actually was write a prompt just like a human could. And you sort of have this ability to inspect how machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it 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 way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and 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 able to sort of take away all those details from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted for review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able change the work of the AI if we want to. so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when we ask these 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 to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a problem, the 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 have to do a second step, too, which is teach the AI what to do with those skills. for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it will pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the machine alongside our team. over the course of a couple of months we able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is kind of sending up a bat 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 we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re 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 to AI. As move to harder tasks, we will have to scale our to provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our to supervise the machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a tedious 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 you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. 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 fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And I this really shows the shape of something that we should expect be much more common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this process right, will be able to solve impossible problems.
And to you a sense of just how impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me you the ChatGPT take on how to analyze a data 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. And so can just literally upload a file and ask questions about it. And very helpfully, know, it knows the name of 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 the file, the column names like you saw and the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these 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 is 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 I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really wanted it to this thing, maybe it’s a little bit of an overreach for it to sort of, inferred magically that this is what I wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but 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 very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the dog would 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 not a vet, need to talk to a professional, here are some hypotheses.” He brought that information a second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, that a human with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of that artificial general intelligence benefits all 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 that a large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the 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, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with 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 look at the compute progress, algorithmic progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And that just thought really hard about like: What is it going to to make progress here? We tried a lot of things that didn’t work, so you only see things that did. And I think that the most important has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have 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 that you saw in these language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. I think 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 we wanted to be, was deep learning lab, and exactly how to do it? I think that in early days, we didn’t know. We tried a lot things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are just like, on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that that 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. happens all the time, ant colonies, single ants run around, when you enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. But you grow 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 that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if you 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number pieces of text. And it is learning things that didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. now we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically 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: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in 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 are questions of degree and scale and timing. And I think one people miss, too, is sort of the integration with world is also this incredibly emergent, sort of, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing be that we take this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of 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 real understanding inside, the system going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that you about is basically going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field of broken promises, of all experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus one or something that is what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do this is to put it out there in and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it out there. So, you know, the original story that heard on OpenAI when you were founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That your of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to catch up. And some of their have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. And 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 how to build artificial general intelligence, actually have it benefit all humanity, like, how are you supposed to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and you figure out the safety of it and then you “go,” and you hope you got it right. I don’t know how to execute that plan. Maybe else does. But for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path that see, which is that you do let reality hit you in the face. And I you do give people time to give input. You do have, these machines are perfect, before they are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. believe that in that box 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 one percent thing in small print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be five years away. if it gets to be 500 years away and get more time to get it right, which do you pick? you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother in the military at the time and like, he puts his on the line in a much more real way than any us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole of computing, I really mean it when I say that this an industry-wide or even just 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 if you don’t them together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But I actually that if you look at capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to 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 literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that that continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)