We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help 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 like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people 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. 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 important for our society 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 first thing I’m to show you is what it’s like to build a tool an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are 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 picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of ChatGPT. And we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in of carrying out your intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was a prompt just like a human could. And so you sort of have this ability to how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to integrate other applications too. You can say, “Now make a shopping list for tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out for all the viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you can see that ChatGPT selecting all these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all those details from you. So don’t have to be the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list of 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 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 if we want to. And so after this talk, you will able to 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 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 do this, we use an idea. If you go back to Alan Turing’s 1950 on the 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 things and does things that are either good or bad.
And this is exactly we train 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 the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to complete that math problem, to say what comes next, green nine up there, is to 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. have the AI try out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, but 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 your intent and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that 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 to the machine alongside our team. And over the course a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific of scenario.” And we’ve actually made lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way that we really listen to our users and sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean 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 harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s to help us provide even better feedback and to our ability to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a 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 getting better every time we provide some feedback. we can actually use the AI to fact-check. And it can check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain of thought as does it. It says, I’m just going to search for this and it actually does the search. It 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 this you do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much fun to be in the driver’s seat, to be in this manager’s where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking tool is doing it in order to produce data another AI to become more useful to a human. I think this really shows the shape of something we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately in how they fit into a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions it. And very helpfully, you know, it knows the of the 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 of the file, the column like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. It has to of, put together its 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 of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy help 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?” And once again, this is a super high-level with lots of intent behind it. But I don’t even what I 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, cloud of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by 13?] So April 13 was the cut-off date I believe. Can 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. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just 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 you noticed, it updates the title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how I think we … vision of how we may end up using this technology in the future. A person his very sick dog to the vet, and the made a bad call to say, “Let’s just wait see.” And the dog would not be here today he listened. In the meanwhile, he provided the blood test, like, full medical records, to GPT-4, which said, “I am not a vet, you need talk to a professional, here are some hypotheses.” He 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 a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we consider to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want to 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 technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can achieve OpenAI mission of ensuring that artificial general intelligence benefits of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that 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 that we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all building on 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 think within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to confront reality as it lays. And 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 things that did. And I think that the 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 the way, just 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 saw something in these models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, 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 in early days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model will where the commas go, where the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review was or 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 sort of semantics emerged from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a machine. Just the stuff you showed us just now. And the key of emergence is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where a few together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you just something pop that just blew your mind that just did not see coming.
GB: Yeah, well, so you can try 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 is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so 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 addition table, that’s more atoms than there in the universe. So it had to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this to 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 pieces of text. And it is learning things that you didn’t know it was going to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at is some 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, we had rebuild our entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have to get single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look 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 of big fears then, that arises from this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of reasons that we think it’s so important to deploy incrementally. And so I 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 tasks we do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We to build up a track 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 ways of 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 to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to things like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that the OpenAI approach has always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got 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 fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails or die. You know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we about these questions all the 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, from very beginning, when we were thinking about how to build 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 this super thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you let reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the number thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that actually could 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 story that I haven’t actually told before, which is that after we 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 you think about it for moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to 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 really felt it in the moment. I was like, of course you the 500 years. My brother was in the military the time and like, he puts his life on the line a much more real way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if you look at the whole of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they happening. And if you don’t put them together, you get an overhang, which means if someone does, or the moment that someone does to connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that one thing I take away is like, even you about development of other sort of technologies, think about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But I actually think if you look at capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to take each step as we encounter it. And think it’s incredibly important today that we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so for coming to TED and blowing our minds.
(Applause)