We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re an historic period right now where we as a world are going to define technology that will be so important for our society going forward. And I believe that we can this for good.
So today, I want to show you the current 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 a for an AI rather than building it for a human. So we have 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 draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s just the 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 — 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 terms of carrying out your intent. 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 getting hungry 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 here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and that what it actually did was write a prompt just like a could. And so you sort of have this ability to how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a 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 want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without 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 are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of away all those details from you. So you don’t to be the one who spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something I think shows that they’re not going away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important 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 we even want it to do we ask these very high-level questions? And to do this, we use an old idea. If you go back Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher who provides rewards punishments as it tries things out and does things that either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process imbues it with sorts 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 have to do a second step, too, which is to teach the AI what to do with 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 that AI said, but very importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only 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 run it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a couple months we were able to 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 that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way we really 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 to clean 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 sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. let me show you what I 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 is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This one is a browsing tool the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as it it. It says, I’m just going to search for and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s to click into the blog post. And all of this you do, but it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data another AI to become more useful to a human. And I think this really shows the of something that we should expect to be much common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem and how want to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I think over time, if we get this process right, we be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think about spreadsheets. They’ve around 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 is a specific spreadsheet of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. And very helpfully, know, it knows the name of the file 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 actual data. And from that it’s able to 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 site that people submit papers and therefore that’s what things are and that these are integer values and so it’s a number of authors in the paper,” like of that, that’s work for a human to do, and AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so comes up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even 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 parable of how I think we … A vision of how we may end up using technology in the future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, you need 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 perfect. You cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going to participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that 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 my question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, 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 to confront reality as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, so you only the things that did. And I think that the important thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have 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 just about the fact that you saw something in language models that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the next in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the 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 tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
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. And the really thing is actually, if you have it add like 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at an incredible number of of text. And it is learning things that you didn’t know that it was 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 emergent capabilities. And to do actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild our entire stack. you think 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 engineered properly, and then you start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. 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. So were able to predict, for example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all 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 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 you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have 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 in this session, there are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human feedback that you about is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like this field is the field of broken promises, all these experts saying X is going to happen, Y is how it works. People been 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 something like that is what need. But I think that our approach has always been, you’ve got to push to limits of this 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 controversial stance you’ve taken, that the right way to do is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are all scrambling to catch up. some of 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 that what have done is responsible here and not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, 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 of being, well, you build secret, you get this 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 execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach the only other path that I see, which is that do let reality hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number thing people were going 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 for you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually also one percent 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 you don’t do that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the hotel room just looking over this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time to get right, which do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the military at the and like, he puts his life on the line in a more real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, if you look at the history of computing, I really mean it when I say that is an industry-wide or even just almost like a human-development- of-technology-wide shift. the more that you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, change in what humans could do. But I think that if you look at capability, it’s been quite smooth time. And so 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 it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that 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 it’s also important to say this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED blowing our minds.
(Applause)