We started OpenAI years ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s really gratifying hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as a world are going to define a that will be so important for our society going forward. And I believe that we manage this for good.
So today, I want to show you the current state of that technology some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool for an AI rather 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. you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and 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 very, very detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can do on your in terms of carrying out your intent. And I’ll point out, this is all live demo. This is all 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 getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood see that what it actually did was write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, which allows us 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 the tasty I was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of 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 to happen.
And as I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? If look at this, you still can click through it and of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s drafted for our 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 to change the work of the AI 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, important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build machine, like a human child, and then teach it feedback. Have a 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 we train ChatGPT. It’s 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 what comes next in you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re a math problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, which is teach the AI what to do with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the whole that the AI 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 it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re 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 kind and offered 20 hours of his own time to provide feedback to the machine our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up 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 way that we really listen to our users 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 floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we will have to scale our ability to 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 me show what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m going to search for this and it actually does 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 you 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, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go and very easily any piece of 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. 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 for another AI to become more useful to a human. I think this really shows the shape of something we should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in a 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 process right, we will be able 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 almost every aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much 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 show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the of the file, the column names like you saw then the actual data. And from that it’s able infer 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 people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a of authors in 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 even know I want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t 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 of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting to see. the 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 to then make this plot 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 could be going there? By the way, all this is Python code, 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 about this 2023 thing. It makes this year look bad. Of course, the 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. percentage of papers in 2022 were even posted by 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 of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to this 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 my intent, I provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you 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 I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision how we may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s come up with this that shocked the 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, of 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 to confront as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot 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 from each other to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is pretty illustrative, right? I that high 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 of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. the 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 run around, when bring enough of them together, you get these ant colonies that show completely emergent, different behavior. a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to do it. the really interesting thing is actually, if you have it like a 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 fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that you didn’t know that was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to do actually, one of the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I think one thing people miss, too, is of the integration with the world is also this emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. And so I that what we kind of 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 do, you can inspect them, right? It’s very easy to look at math problem and be 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 know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be we take this step by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to up a track record with these machines that they’re able to carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going hear later in 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 to that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is at any one 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 getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit you in face, right? It’s like this field is the field broken promises, of all these experts saying X is to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable 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 the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the case that 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 we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you hope you got 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 think that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And I think you do people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give beautiful gifts to your family 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 could unleash evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you 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 we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the time like, he puts his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you at the whole history of computing, I really mean when 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 are there, right, we’re making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put together, you get an overhang, which means that if does, or the moment that someone does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one I take away is like, even you think about development of 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 over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to is that we have birthed this extraordinary child that may have that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And 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 from it. And my hope that that will continue to be the best path, but it’s 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 coming to TED blowing our minds.
(Applause)