We started seven 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. it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so important our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of technology and some of the underlying design principles that we hold dear.
So the first I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but 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, it generates an image. And that is something that really the power of what it can do on your in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by the as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting 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 “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under the hood and see that what actually did was write a prompt just like a human could. And so you sort of have this to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet out for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to 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 great experience within an app as long as you kind of know the menus know all the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And 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. that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we to. And so after this talk, you will be able to this yourself. And there we 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 building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then teach it through feedback. Have human teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what next, that green nine up there, is to actually solve the problem.
But we actually have to do a second step, too, which is teach the AI what to do with those skills. for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the specific that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time to provide to the machine alongside our team. And over the course of couple of months we were able to teach the that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just 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 to harder tasks, we will to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale 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 this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model two months passed. But is it true? Like, these are 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 can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing tool where the can issue search queries and click into web pages. it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become more to a human. And I think this really shows the shape of something that should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if get this process right, we will be able to impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around in some 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 on the arXiv for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me show you the take 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 so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of the file, the column names you saw and then the actual data. And from that it’s able to what these columns actually 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 and therefore that’s what these things and that these are integer values and so therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I to ask. So 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 what I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. could 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 wonderful things that appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. 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 like was more I wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, inferred that this 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 it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how think we … A vision of how we may end up using this in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He brought information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You overly rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s thing to take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also 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 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 of giants, right, there’s question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices from the early days. And the first one 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 things that didn’t work, you only see the things that did. And I that the most important thing has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these language that meant that if you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted 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 of things, and one person was working training a model to predict the next character in reviews, and he got a result where — this is a syntactic process, you expect, you know, the 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 you if a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you you saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to 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 it wrong. And so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to 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 going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. 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 some models 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 arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also this 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 think that what kind of see right now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the important thing will be that we this step by step. And that we say, OK, as we move to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are 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 and so forth. Is your belief, Greg, that it is true at any one moment, but that the of the scale and the human feedback that you talked is basically going to take it on that journey of getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the of broken promises, of all these experts saying X is 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 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 the of this technology to really see it in action, because tells you then, oh, here’s how we can move on to a new paradigm. And 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 to put out there in public and then harness all this, you know, instead just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly evil 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, need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of criticisms have 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 all the time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has been incredibly important, from very beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all 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 powerful thing, and you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the only other path that I see, which that you do let reality hit you in the face. And think you do give people time to give input. You do have, these machines are perfect, before 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 one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a experiment for 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 chance it’s something glorious that’s going to give beautiful gifts to your family to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a good time. And think about it for a moment, if you could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be five years away. But it gets to be 500 years away and people get time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he puts his life on line in a much more real way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold on the you’ve got approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole of computing, I really mean it when I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you have this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I think that one thing take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans do. But I actually think that if you look 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 manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)