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 to see how far this whole field has come since then. it’s really gratifying to hear from people 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 a world are going to define a technology that be so important for our society going forward. And believe that we can manage this for good.
So today, I to show you the current state of that technology and some the underlying design principles that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI than building it for a human. So we have a DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. let’s see what we’re going to 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 as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
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I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save for later.” And the interesting thing about these tools they’re very inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually did was write a prompt like a human could. And so you sort of this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and integrate with other applications too. You can say, “Now a 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 viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an as 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 having this unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to be the one who spells out 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 the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still 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 think 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 our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work 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.
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So we’ll cut back to the slides. Now, important thing about how we build this, it’s not just about building tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we ask very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, you can learn it. You build a machine, like a human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what would have called a child machine through an unsupervised process. We just show it the whole world, the whole internet and say, “Predict what 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 actually have to do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It it 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 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 teach wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the machine alongside our team. And over course of a couple of months we were able to the AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team to say, “Here’s an area of where you should gather feedback.” And so when you 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 a kid to clean their room, if all you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to the machine as time goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these 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, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check 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. it actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does the search. It then finds the publication date and the search results. It then is issuing another search query. It’s going click into the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go 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. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more to a human. And I think this really shows the shape of something we should expect to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they fit into a problem how we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And so you can just upload a file and ask questions about it. And helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of file, the column names like you saw and then the data. And from that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in the paper,” like 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 some graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know what want. And the 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 of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? So we’ll see, this is kind of 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 an overreach for it to have of, inferred magically that this is what I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut back to the slide again. This slide a parable of how I think we … A vision how we may end up using this technology in the future. A 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 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 to talk to professional, here are some hypotheses.” He brought that information to a second vet who used to save the 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 a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going to require from everyone. And that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And if there’s one thing to take from this talk, it’s that this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. I right? Who thinks that they’re having to rethink the way that 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 the hell have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: is it going to take to make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think 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 way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was on training a model to predict the next character Amazon reviews, and he got a result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive negative. I mean, today we are just like, come on, anyone can do that. this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or a city where a houses together, it’s just houses together. But as you grow the of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop just blew 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 learned an internal circuit for how to do it. And the really interesting thing is actually, if 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 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 there in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can sort generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that 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 more nuanced, too. So science that we’re starting to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of the stack engineered properly, and 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 to be able to predict. So we were able predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, but it’s capable of 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 scale timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy look at that math problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a 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 the important thing will be that we take this by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways scaling this, sort of like making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let reality hit you the face, right? It’s like this field is the field of broken promises, of all these experts X is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. But I think that our has always been, you’ve got to push to the limits of this technology really see 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 fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way to do this is to it out there in public and then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is that you do reality hit you in the face. And I think you do people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe that in that box something that, there’s a very strong chance it’s something absolutely glorious that’s to give 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 unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets be 500 years away and people get more time to it right, which do 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 time and like, he puts his life on the line in a much real way than any of us typing things in computers and developing this technology at 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 as it truly lies. Like, if you look at the whole history of computing, I really mean when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think about development of sort of technologies, think 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 look capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you 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 guardrails for this child to collectively teach it to wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly today that we all do get literate in 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 so 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 TED and blowing our minds.
(Applause)