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 just really amazing to how far this whole field has come since then. And it’s really to hear from people like Raymond who are using the technology are building, and others, for so many wonderful things. We from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world are going to define a technology will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the 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 build a tool for an AI rather than building it a human. So we 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 like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. So let’s see what we’re to get. But 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 it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know 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 the interesting thing about tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming 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 you sort of this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, meal, I definitely 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 ones use in any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to sort of take away all those from you. So you don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, so sometimes the will happen to us. But let’s take a look the 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 the traditional UI is still very valuable, right? If you look at this, you still can click through and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. 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 to change the of the AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do even want it to do when we ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text 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 the math problem.
But we have to do a second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have the AI try multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of infer intent and apply it in scenarios that it hasn’t seen before, 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 showed GPT-4 to Khan Academy, 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. there’s some bad math in there, it will happily pretend that one plus equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually made lots and lots of to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s one way that we listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to harder tasks, 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 to scale our ability to supervise the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries and into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just to search for this and it actually does the search. It it finds the publication date and the search results. then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to 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 verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is it’s this many-step collaboration between a human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to become more useful a human. And I think this really shows the shape of something that we should to 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 how we want to solve that problem. We make sure the humans 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 actually create even more trustworthy machines. And I think that over time, we get this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago 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 on the arXiv the past 30 years. There’s about 167,000 of them. And you can see 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 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 about it. And very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, 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 instruction with lots 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 comes with some good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem 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 papers 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there more I wanted out of the machine here. I really 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. But I inject intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. This slide shows a parable how I think we … A vision of how we may end up using this technology the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of 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 that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on artificial intelligence. Why is it you who’s come 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 compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made 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 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 people who very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. 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, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, 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 a 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 result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and verbs are. But 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, today we are just like, come on, anyone can that. But this was the first time that you saw emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens 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. But as you grow the of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop that just blew your mind that you 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 add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like you can’t the 40-digit addition table, that’s more atoms than there in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And it learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s early days.
CA: So here is, one of the fears then, that arises from this. If it’s fundamental what’s happening here, that 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 of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of the integration with the world also this incredibly 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, lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think the important thing will be that we take this by 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 machines that they’re able to actually out our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with 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 is to always — we’re never going to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it 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, short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is 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 work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve got to 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 exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to this is to put it out there in public then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is 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 great sort of on the big companies doing their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of slowing the field down, need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so I think that this approach is the only 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 are perfect, before are super powerful, that you actually have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe in that box is something that, there’s a very 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 the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 years away and people get more to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do 500 years. My brother was in the military at time and like, he puts his life on the in a much more real way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster 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, 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 what kind of safety precautions you get. And so I think one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think that 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 to do it incrementally and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure how to provide the feedback, decide what we want it. And my hope is that that will continue be the best path, but it’s so good we’re honestly having this debate we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)