We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to see how far this field 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 wonderful things. We from people who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where we a world are going to define a technology that will be so important for our going forward. And I believe that we can manage for good.
So today, I want to show you the state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a picture 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 out of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it also generates an image. And that something that really expands the power of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. I actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did was write a prompt like a human could. And so you sort of have this ability inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all options. Yes, I 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 to be the one who spells out every single of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes unexpected will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? If you look this, you still can click through it and sort modify the actual 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 them. And now we have a tweet that’s been drafted our review, which is also a very important thing. 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 if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important about how we build this, it’s not just about building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when we ask very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher provides 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 Turing would called a child machine through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not 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 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 pretend that one one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a couple of months we able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually kind 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 do that, that’s one way that we really to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we have to scale our ability to provide high-quality feedback. But for this, the AI 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 you what I mean.
For example, you can ask GPT-4 a question this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model says two passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But we actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool the model can issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and it does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s to click into the blog post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you actually go and very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I think this really shows the shape of that 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 problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, and the machines are in a way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be able to rethink almost every of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file and questions about it. And 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, column names like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work a human to do, and the AI is happy to with it.
Now I don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And the AI kind of has to 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 of paper titles. All of that, I think, will be interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see these 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 that the year 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 the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of I think we … A vision of how we may end up using this technology in the future. 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 blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are 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 rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would not have otherwise. I think this is something we should all reflect on, think as we consider how to integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe we can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just possibilities there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate choices the early days. And the first one was just confront reality as it lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the that did. And I think that the most important thing been to get teams of people who are very from 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 the fact that you something in these language models that meant that if continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly 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 Amazon reviews, and he got a result where — this is syntactic process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was the first that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the riddle that baffles looking at this, because these things are described as machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. the key idea of emergence is that when you more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when bring enough of 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. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that blew your mind that you just did 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, the model do it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get it wrong. so you can see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding arbitrary numbers of lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. to do that actually, one of the things I think is very in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can see all of these curves there. And now we’re starting to be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about 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. If it’s fundamental what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And I one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons that we think it’s so to deploy incrementally. And so I 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 we do, can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if this summary is any good? You have to read the whole book. No one to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise task properly. We have to build up a track record with these machines that they’re to actually carry out our intent. And I think we’re going have to produce even better, more efficient, more reliable ways of scaling this, sort of like the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re going to know 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 one moment, but that expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always 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 happen, Y is 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 that is what you need. But I think that our has always been, you’ve got to push to the limits of this to 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 fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is put it out there in public and then harness all this, know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on OpenAI when you founded as a nonprofit, well you were there as great sort of check on the big companies doing unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them accountable and was capable of slowing field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, are forcing us to put this out here without proper or we die. You know, how do you, like, the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all 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, the very beginning, when we were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” and you hope got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only other path I see, which is that you do let reality hit you in the face. And think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it was generate misinformation, try to elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that much worse. Here’s a thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that is something that, there’s a very strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually a one percent thing in the small print there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you that box?
GB: Well, so, absolutely not. I think you don’t do it 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 was Puerto Rico for an AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And you think about it 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 to have it be five away. But if it gets to be 500 years and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in the military at the and like, he puts his life on the line in much more real way than any of us typing in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re still improving the algorithms, all these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, you think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got 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 to a whole new place. It is our collective to provide the guardrails for this child to collectively teach it to be wise not to tear us all down. Is that basically the model?
GB: I think it’s true. And I it’s also important to say this may shift, right? We’ve to take each step as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, figure out how provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)