We started OpenAI years ago because we felt like something really interesting was in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, for so many things. We hear 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 right where we as a world are going to define a technology that will be so important our society going forward. And I believe that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something really expands the power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI we speak. So I actually 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 tools too, for example, memory. can say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did write a prompt just like a human could. And so you sort have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s to use that information and to integrate with other 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 these different without me having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you kind know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t to be the one who spells out every single sort of little 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 look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s drafted for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And so after talk, you will be 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 how to use them. Like, what do we even it to do when we ask these very high-level questions? And do this, we use an old idea. If you go back to Alan Turing’s 1950 on the Turing test, he says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process 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 it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to 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 to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math there, it will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time to provide feedback the machine 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 on in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you 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 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 we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every we provide some feedback. But we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a tool where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually does the search. It it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And of 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 you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out 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 so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we expect to be much more common in the future, where we have humans and machines of very carefully and delicately designed in how they into a problem and how we want to solve 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 actually create even more trustworthy machines. And I think over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think 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 some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here a specific spreadsheet of all the AI papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let me you the ChatGPT take 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, just like a data scientist would. so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is name of the file, the column names like you and then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors the paper,” like all of that, that’s work for a to do, and the AI is happy to help it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory 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. the AI kind of has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all this Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this the kind 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 notice thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is just code again, so if you want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision of how we may up using this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I not 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 ChatGPT a 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 believe deeply, is that getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take away this talk, it’s that this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single 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 rethink the way 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 has thousands of working on artificial intelligence. Why is it 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 you look at the compute progress, the 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 one was just to reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? We a lot of things that didn’t work, so you only see the things that did. And I that the most important thing has been to get teams of who are very different from each other to work together harmoniously.
CA: Can we have water, by the 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 you saw something these language models that meant that if you continue invest in them and grow them, that something at some might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew that was what wanted to be, was a 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, one person was working on training a model to the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time that you saw emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s 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 see it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and look at incredible number 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, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And we’re starting to be able to predict. So we were able to predict, for example, the performance on 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 scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, things emerge that you can predict in some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind see right now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary is 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 say, OK, as we move on to book summaries, have to supervise this task properly. We have to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort 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 understanding inside, the system is going always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to it on that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure 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 think that the OpenAI approach here has been just like, let reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like is what you need. But I think that our has always been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s we can move on to a new paradigm. And we just haven’t the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it there in public and then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get super powerful thing, and 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 execute that plan. someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the other path that I see, which is that you do reality hit you in the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent in the 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. I think don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to 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 the 500 years. My brother was in the military at the time and like, he puts his life the line in a much more real way than any of typing things in computers and developing this technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But don’t think that’s quite playing the field as 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 computers, we’re still improving the algorithms, all of these things, they 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 to the circuit, then you 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 think about development of other of technologies, think about nuclear weapons, people talk about being a zero to 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 so the history, I think, 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 you us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to provide the feedback, decide what want from it. And my hope is that that will continue to be the path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)