We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people 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 hear from people who both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period now where we as a world are going to 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 current state of that technology some of the underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like build a tool for an AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app for to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal 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 idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. But doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And is something that really expands the 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 is generated by the AI as we speak. So I don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. 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 that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see that is selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified interface on top of tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really is that the traditional UI is still very valuable, right? you look at 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 for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to them. Like, what do we even want it to 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 on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does 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 called a child through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process it with all sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, is to teach the AI what to do with those skills. And for this, we feedback. We have the AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And 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 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 feedback.
Now, sometimes the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only 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 with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback is hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to help us provide even better feedback to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re 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, in this case, I’ve actually the AI a new tool. This one is a tool where the model can issue search queries and click web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just 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. And all of you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And out citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order to produce data for another AI to become useful to a human. And I think this really shows the shape 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 into a 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 to actually create even more machines. And I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of just 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 in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of all 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. let me show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here is the name the file, the column names like you saw and then the data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy help with it.
Now I don’t even know what I to ask. So fortunately, you can ask the machine, “Can you make some graphs?” And once again, this is a super high-level instruction lots of intent behind it. But I don’t even know what want. And the AI kind of has to infer what might be interested in. And so it comes up some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to make this nice plot of the papers per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … vision of how we may end up using this technology in 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 be here today had he listened. In the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not 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. cannot overly rely on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And we all 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 intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect a very 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 I right? thinks 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, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s up with this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was just to confront reality it lays. And 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 been to get teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the 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 are like, come on, anyone can do that. But this was the first 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 I think this explain the riddle that baffles everyone looking at this, because things are described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens all the time, ant colonies, single run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give one moment 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 try this 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 really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often it wrong. And 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 have learned something general, but that it hasn’t really fully yet learned that, Oh, I can sort of this to 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 of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look our GPT-4 blog post, you can see all of these curves in there. And 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 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 fundamental what’s happening here, that as you scale up, things emerge that you can predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the 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. And so think that what we kind of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the important thing will be that we this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. We to build up a track record with these machines they’re able to actually carry out our intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take on that journey of actually getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this is 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 is 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 can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out in public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and so forth all scrambling to catch up. And some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about 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 think has been important, from the very beginning, when we were thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and you figure out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute plan. Maybe someone else 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 hit you in the face. And I think you do people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re sitting in 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 glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually told before, is that shortly after we started OpenAI, I remember I in Puerto Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could for basically that Pandora’s box to be five years 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 be five years away. But if it gets to be 500 years away and people more time to get it right, which do you pick? And know, I just really felt it in the moment. I was like, of course do the 500 years. My brother was in the military at time and like, he puts his life on the line in a much more way than any of us typing things in computers developing this technology at the time. And so, yeah, I’m really on the 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 whole history of computing, I really mean it when I that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one I take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we birthed this extraordinary 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 to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as we encounter it. And I think it’s important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And hope is that that will continue to be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)