We started OpenAI seven ago because we felt like something really interesting was happening AI and we wanted to help steer it in a positive direction. It’s just really 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. We hear 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 period right now where we as a world are going to define a that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to show you current state of that technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. we have a new 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 and a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to in any situation. And this, I think, shows a way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the one who spells out every sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will happen to us. But let’s take 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 you need. And the that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, 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, is also a very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the if we want to. And so after this talk, you be able to access this yourself. And there we go. Cool. Thank you, everyone.
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So we’ll cut to the slides. Now, the important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, what we even want it to do when we ask very high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that 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 unsupervised process. We just show it the whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But we actually have do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And when 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 their room, if all you’re doing is inspecting the floor, you don’t 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 of reasoning to AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale our ability to supervise the as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can actually its own 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 tool where the 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 just going to search for this and it does the search. It then it finds the publication date and the 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 a thing that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so to me about this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to more useful to a human. And I think this really shows the shape of something that 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 how we want to solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let show you the ChatGPT take on how to analyze a data 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 a data scientist would. And you can just literally upload a file and ask questions about it. And very helpfully, you know, it the 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 name of the file, the column names like you saw and then actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these 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 with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know what I want. And the kind of has to infer what I might be in. And so it comes up with some good ideas, I think. a histogram of the number 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 go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. could be going on there? By the way, all this Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is the year is not over. So I’m going to 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 use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of machine here. I really wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing 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 title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This slide shows a parable of I think we … A vision of how we end up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the full 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 to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want 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 released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
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Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to 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 guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first one was just to reality as it lays. And that we just thought really hard about like: What it going to take to make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think the most important thing has been to get teams of people who are very different from each other work together harmoniously.
CA: Can we have 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 also just about fact that you saw something in these language models that meant that if you to invest in them and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always that was what we wanted to be, was a learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged 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 the riddle baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, you can try this in 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 how to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often get 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 the universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces 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 that actually, one of the things I think is very undersung in this is sort of engineering quality. Like, we had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you 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, performance 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 actually smooth scaling, 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, 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 huge of something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, if you look this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But 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 to the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. have to build up a track record with these that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: we’re going to hear later in this session, there critics who say that, you know, there’s no real inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t common sense and so forth. Is it your belief, Greg, it is true at any one moment, but that the expansion of the scale and the feedback that you talked about is basically going to take on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like 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 for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach has 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 we can move to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it out there in and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great 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 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 opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one thing I think 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 you supposed to do that, right? And that default plan being, well, you build in secret, you get this super thing, and then you figure out the safety of and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that I see, which is that you let reality hit you in the face. And I you do give people time to give input. You have, before these machines are perfect, before they are super powerful, you actually have the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t it that way. And honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all people having a good time. And you think about it for 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 it right, which do pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. brother was in the military at the time and like, he his life on the line in a much more real than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite playing the field it truly lies. Like, if you look at the whole of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think that if look at capability, it’s been quite smooth over time. And the history, I think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And my hope is that that continue to be the best path, but it’s so good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
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