We started OpenAI seven years ago because we felt something really interesting was happening 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 come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as a are going to define a technology that will be important for our society going forward. And I believe that we can this for good.
So today, I want to show the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, you know, a nice post-TED meal and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. And that is something that expands the power of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is a live demo. This is all generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt just like a could. And so you sort of have this ability inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s great experience within an app as long as you of know the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort of take all those details from you. So you don’t have be the one who spells out every single sort little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping list while we’re it. And you can see we sent a list of to Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something that I 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 been for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of AI if we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back 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 them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach through feedback. Have a human teacher who provides rewards and punishments 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 produce what Turing have called a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to do a step, too, which is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. this allows it 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 what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals and run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an of 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 something that’s useful for 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 the floor, you don’t know if you’re just teaching them to stuff all toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two passed. But is it 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 case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search queries and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for this it actually does the search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more to a human. And I think this really shows the shape something that we should expect to be much more in the future, where we have humans and machines kind of very and delicately designed in how they fit into a and how we want to solve that problem. We make sure the humans are providing 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 even more trustworthy machines. And I think over time, if we get this process right, we will be able 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, think about spreadsheets. They’ve been in 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 is a specific spreadsheet all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, just like a data would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the of the file, the column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing 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 it’s a number of authors in the paper,” like all of that, that’s work for a to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can 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 be interested in. And so it comes up with some good ideas, I think. So histogram of the number of authors per paper, time series of papers per year, word cloud 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, nice bell curve. You see that three is kind the most common. It’s going to then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential it 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 appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how we may end up this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet who used 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 with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we how to integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And if there’s 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 OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much single thing 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 the 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 the hell have you 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 the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of 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 think within OpenAI, we made a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: What is going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who very different from each other to work together harmoniously.
CA: we have the 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 saw something in these language models that meant that you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I 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 we wanted to be, was a deep learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot things, and one person was working on training a model to predict the next character in Amazon reviews, he got a result where — this is a syntactic process, you expect, you know, the will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time that saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so 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 in the universe. So it to have learned something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if look at this talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that math problem be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this properly. We have to build up a track record with machines that they’re able to actually carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be 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, system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it true at any one moment, but that the expansion of scale and the human feedback that you talked about basically going to take it on that journey of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that OpenAI approach here has always been just like, let 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 how it works. People have saying neural nets aren’t going to work for 70 years. haven’t been right yet. They might be right maybe 70 years plus one or like that is what you need. But I think that our approach has been, you’ve got to push to the limits of technology to really see it in action, because that you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is put it out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to build models that of, you know, somehow held them 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, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that default of being, well, you build in secret, you get this super powerful thing, and you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know how to that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other that I see, which is that you do 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 powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box on the table. You believe that that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also a one thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get more time get it right, which do you pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on the line a much more real way than any of us typing things in computers and 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 look at the whole history of computing, I really mean it when I that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, 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 the history, I think, every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that 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 to be the best 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 coming to TED and our minds.
(Applause)