We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we from people who feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a are going to define a technology that will be so for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology and some of underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal 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. here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. you get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was write a prompt just like human could. And so you sort of have this to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different 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, we so used to thinking of, well, we have these apps, 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 the options. Yes, would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able to sort of take away all those from you. So you don’t have to be the who spells out every single sort of little piece what’s supposed to happen.
And as I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And thing that’s really interesting is that the traditional UI 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 that I shows that they’re not going away, traditional UIs. It’s just we have a new, way to build them. And now we have a tweet that’s been 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 the of the AI if we want to. And so 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 about teaching the AI to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, use an old idea. If you go back to Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and as it tries things out and does things that are good or 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 with sorts of wonderful skills. For example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually have 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 try out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this not just the specific thing that the AI said, but very importantly, the whole process that the used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one three and run with it.” So we had to collect some feedback data. Sal himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building something that’s more for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy help. It’s happy to help us provide even better and to scale our ability to supervise the machine time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how time 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 time we provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue queries and click into web pages. And it actually out its whole chain of thought as it does it. says, I’m just going to search for this and it does the search. It then it finds the publication date and the search results. then is issuing another search 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 not a that humans really want 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 very easily verify any piece of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another AI to become more to a human. And I think this really shows the of something that we should expect to be much more in the future, where we have humans and machines of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a way that’s 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 to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink 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. I don’t think they’ve changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me show you the take on how to analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And very helpfully, you know, knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” only information here is the name of the file, the column names like you saw then the actual data. And from that it’s able to infer what columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer what I be interested in. And so it comes up with good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What be going 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 unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even by April 13?] So April 13 was the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out of the machine here. I really it to 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 this additional piece of, you know, guidance. And under hood, the AI is just 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, it even the title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of how we may end up this technology in the future. A person brought his sick dog to the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he 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.” brought that information to a second vet who used 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 a human with a medical and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just 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 just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees 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 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 deliberate choices from the early days. And the one was just to confront reality as it lays. And that just thought really hard about like: What is it going to to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been to get teams of who are very different from each other to work together harmoniously.
CA: we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that you something in these language models that meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and how to do it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first that you saw this 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 looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of is that when you get more of a thing, suddenly different emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you the number 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 will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it had to learned something general, but that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible number pieces of text. And it 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 good at is predicting some of emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you about building a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all of these curves 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 or 1,000 times smaller. so there’s something about this that is 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 of surprising you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort of the integration with the is 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 I think that what we kind of right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very to look at that math problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we this step by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these saying X is going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be right 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 the of this technology to really see it in action, that tells you then, oh, here’s how we can move on to 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 do this is to put it out in public and then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you there as the great sort of check on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without proper or 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 all time. Like, seriously all 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 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 this super thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know to 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 you do let reality hit you the face. And I think you do give people time to give input. do have, before these machines are perfect, before they are powerful, that you actually have the ability to see them in action. we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going 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 a thought for you. Suppose you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s actually a one percent thing in the small print there 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 you don’t it that way. And honestly, like, I’ll tell you 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, if you could choose for basically Pandora’s box to be five years away or 500 years away, would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it 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 really it 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 things in computers and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more that 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 are happening. And 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 this very powerful thing, no one’s had any time to adjust, who knows what 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 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. so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child to collectively teach to be 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 this shift, right? We’ve got to take each step as we encounter it. And I think it’s important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. And hope is that that will continue to be the 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, you so much for coming to TED and blowing minds.
(Applause)