We OpenAI seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people like Raymond who are the technology we are building, and others, for so many things. We hear from people who are excited, we hear from 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 now we as a world are going to define a technology that will be so important for society going forward. And I believe that we can manage this for good.
So today, I to show you the current state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m to show you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking care of the details you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands power of what it can do on your behalf terms of carrying out your intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that what it actually was write a prompt just like a human could. And so you sort of have this ability inspect how the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s to use that information and to integrate with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long as you kind of the menus and know all the options. Yes, I would like you to. Yes, please. Always to be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all details from you. So you don’t have to be one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the will happen to us. But let’s take a look at the Instacart shopping list we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of modify the actual quantities. And that’s that I think 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 drafted for our review, is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. 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 the slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what do even want it to do when we ask these high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and punishments as it tries things out does things that are either good or bad.
And this is how we 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, the internet and say, “Predict what comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to actually complete math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach AI what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able teach 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 equals three and run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of 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 our ability to supervise the machine as time on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning from human feedback. the model says two months 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 actually use the AI to fact-check. it 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 model can search queries and click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search this and it actually does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a very tedious task. It’s not thing that humans really 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 you want, triple-check the work. And out come so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll 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 a and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. make sure that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re able 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 you a sense just how impossible I’m talking, I think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve been around 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 the arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to 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 the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can you 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. And the AI of has to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number of authors paper, time series of papers per year, word cloud the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the 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 going on there? By the way, this is Python code, you can inspect. And then 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. course, the problem is that 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 posted by April 13?] So April 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the here. I really wanted it to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you want to what it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the again. This slide shows a parable of how I we … A vision of how we may end up using technology in the future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In 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 are hypotheses.” He brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a medical and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is we should all reflect on, think about as we consider how integrate these systems into our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what AI 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, of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, need to rethink.” Like, there’s just new possibilities there. Am right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And first one was just to confront reality as it lays. that we just thought really hard about like: What is it to take to make progress here? We tried a of things that didn’t work, so you only see the that did. And I think that the most important thing has to get teams of people who are very different from other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something these language models that meant that if you continue to invest them and grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, 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 working on training a model to predict the next in Amazon reviews, and he got a result where — this is a process, you expect, you know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment classifier out of it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the first time you saw this emergence, this sort 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 explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show emergent, different behavior. Or a city where a few houses together, it’s just houses together. But as you the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the interesting thing is actually, if you have it add 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 the 40-digit addition table, that’s atoms than there are in the universe. So it had have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces text. And it is learning things that you didn’t know that it going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is undersung in this field is sort of engineering 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 stack engineered properly, and then you can start doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, the performance on problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still days.
CA: So 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 that you can maybe in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these are questions of degree scale and timing. And I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if look at this talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you can them, right? It’s very 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 a hard thing to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. have to build up a track record with these that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to take on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field the field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like that what you need. But I think that our approach has been, you’ve got to push to the limits of this technology to see it in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial 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 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 original story that I heard on when you were founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and so are all scrambling to catch up. And some of their criticisms been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got right. I don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so I think that this approach is the only other path that I see, is that you do let reality hit you in the face. And I think you give people time to give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely 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 chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all people having a good time. And you think about it for moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 away and people get more time to get it right, which do you pick? And you know, I really felt it in the moment. I was like, course you do the 500 years. My brother was in military at the time and like, he puts his life on the in a much more real way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you look at the whole history 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 sort 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 are happening. if you don’t put them together, you get an overhang, which means that if does, or the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, talk 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 smooth over time. And so the history, I think, of every technology we’ve has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we have birthed this child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)