We started OpenAI years ago because we felt like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we building, and others, for so many wonderful things. We from people who are excited, we hear from people who are concerned, we from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we a world are going to define a technology that will be important for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current of that technology and some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to build a for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. And I’ll point out, is all a live demo. This is all generated by AI as 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.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is coming to you, all users, over upcoming months. And you can look under the hood 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, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s to use that information and to integrate with other too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I want to know how it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are so used thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as long as kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take all those details from you. So you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. let’s take a look at the Instacart shopping list we’re at it. And you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If look at this, you still can click through it and sort of modify the quantities. And 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 important thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI we want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching the how to use them. Like, what do we even it to do when we ask these very high-level questions? And to this, we use an old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, say what comes next, that green nine up there, is actually solve the math problem.
But we actually have do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, to sort of infer your intent apply it in 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 showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his 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 back on humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about asking kid to clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re 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 have to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback and scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can check its own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search queries and click into web pages. And it writes out its whole chain of thought as it does it. It says, I’m going to search for this and it actually does search. It then it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s position where can, if you 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 and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, using this fact-checking tool is doing in order to produce data for another AI to become more useful a human. And I think this really shows the of something that we should expect to be much more common in the future, 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. We make sure that 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 will be able solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to able to 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 with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. let me show you the ChatGPT take on how to 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. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the of the file, the column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that 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 and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you make 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 AI kind of has to infer what I might be interested in. so it comes up with 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 the thing is, it can actually do it. Here we go, a 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 on an exponential and it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. you can see all these wonderful 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 not over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … vision of how we may end up using this in the future. A person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to to a professional, here are some hypotheses.” He brought that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe really deeply, is that AI right is 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 an will and 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 so we have to become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that artificial general benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s a of reeling. Like, I suspect that a very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every single about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has 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, 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 progress, all of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was just to confront as it lays. And that we just thought really hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person working on training a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today are just like, come on, anyone can do that. this was the first time 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: So think this helps explain the riddle that baffles everyone looking at this, because these things are as prediction machines. And 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 us just now. And the idea of emergence is that when you get more 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 completely emergent, different behavior. a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one moment you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really an internal circuit for how to do it. And the really interesting thing is actually, you have 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 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 adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale up look at an incredible number of pieces of text. 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 of these emergent capabilities. And to do that actually, one of the I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. You have get every single piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. 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 there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, 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 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 emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one people miss, too, is sort of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And I think that what we kind of see right now, if look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at 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, do you know if this book summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that say, OK, as we move on to book summaries, we have to this task properly. We have to build up a record with these machines that they’re able to actually carry out intent. And 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 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. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the feedback that you talked about is basically going to it on that journey of actually getting to things like truth wisdom 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. And think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this field is the field of promises, of 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. They haven’t been right yet. They be right maybe 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve to push to the limits of this technology to really it in action, because that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it there 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 things are to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded as a nonprofit, well were there as the great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held accountable and was capable of 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. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and 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 that what you done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of and 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 right. And so I think that this alternative approach is the other path that I see, which is that you do let reality hit you in face. And I think you do give people time to give input. do have, before these machines are perfect, before they are super powerful, 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 one was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m in the hotel room just looking out over this water, all these people having a good time. And think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for 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 it right, which you pick? And you know, I just really felt in the moment. I was like, of course you the 500 years. My brother was in the military at the and like, he puts his life on the line in a much more real way than any of typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve got to this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look the whole history of computing, I really mean it when 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 that if someone does, or the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take away is like, you think about development of other sort of technologies, about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model want us to have is that we have birthed this extraordinary child that may have superpowers that humanity to a whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we all do get literate this technology, figure out how to provide the feedback, decide we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)