We started OpenAI seven years because we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we 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 feel both emotions at once. And honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a world going to define a technology that will be so important for our society forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like to a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all of the, of, ideation 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 for the meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just images in this case — sorry, it doesn’t generate text, it generates an image. And that is something that really expands the power of what it can do your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” 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 the way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood see that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how machine is using these tools, which allows us to provide feedback to them.
Now it’s saved for later, let me show 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 a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to in any situation. And this, I think, shows a new of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click between them, we copy/paste them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, I would 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 of take away all those details from you. So you don’t have to be the who spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart shopping list we’re at it. And you can see we sent a list 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 it and sort modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a very important thing. can click “run,” and there we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we to. And so after this talk, you will be able to this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it to do 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 answer to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a human teacher who provides rewards and punishments as tries things out 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 have called a child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what comes in text 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 way to actually that math problem, to say what comes next, that green nine there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it generalize. It allows 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 teach AI are not what you’d expect. For example, when we first 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 some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine alongside our team. And the course of a couple of months we were able to the AI that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you about asking a kid to clean their room, if all you’re is inspecting the floor, you 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 sort reasoning applies to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But for this, AI itself is happy to help. It’s happy to help us provide even better feedback and to our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models are 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 actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve given the AI a new tool. This one is a tool where the model can issue search queries and click into 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 the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into blog post. And all of this you could do, but it’s a 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 can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is 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 more useful a human. And I think this really shows the shape of that we should expect to be much more common in the future, where have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create even more machines. And I think that over time, if we get this 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 aspect of how we interact with computers. For example, think spreadsheets. They’ve been around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And you see there the data right here. But let me you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a and ask 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 parse it for you.” The information here is the name of the file, the names 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 to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has to infer I might be interested in. And so it comes up some good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the most common. It’s to then make this nice plot of the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off cliff. What could be going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem that the year is not over. So I’m going to push 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 cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. 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 back to slide again. This slide shows a parable of how I think … A vision of how we may end up using this technology in the future. A brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet who it 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 medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just different anything people had anticipated. And so we all have to become literate. that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general 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 a feeling reeling. Like, I suspect that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to the way 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 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 come up with this technology that the 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 those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the days. And the first one was just to confront reality as it lays. And that we just thought hard about like: What is it going to take to make here? We tried a lot of things that didn’t work, so you see the things that did. And I think that most important thing has been to get teams of people who are different from each other to work together harmoniously.
CA: Can we have the water, by the way, 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 meant that if you continue to in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we always knew was what we wanted to be, was a deep 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 working on training a model to predict the next in Amazon reviews, and he got a result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, anyone can do that. But was the first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got see where it goes.
CA: So I think this explain the riddle that baffles everyone 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 a prediction machine. Just the stuff you us just now. And the key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal for how to do it. And the really interesting is actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can of generalize 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 learning that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You have to get every single of the 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 at our GPT-4 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, the on coding problems. We basically look at some models that are 10,000 times or 1,000 times smaller. And there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, of the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that can maybe 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 of these are questions of degree and and timing. And I think one thing people miss, too, sort of the integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the reasons we think it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of 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 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, do you know if this book summary is any good? You have to read whole book. No one wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to supervise task properly. We have to build up a track record with these machines they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways of scaling this, of like making the machine be aligned with you.
CA: So we’re to 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, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can you be of that?
GB: Yeah, well, I think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. And I think that 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 these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work for 70 years. 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 to really see it in action, because that tells you then, oh, here’s how we move on 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 do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that of, you know, somehow held them accountable and was capable of slowing 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 tech world that now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you forcing us to put this out here without proper guardrails we die. You know, how do you, like, make case that what you have done is responsible here and not reckless.
GB: Yeah, we think these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been incredibly important, from 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 figure out the safety of and then you push “go,” and you hope you got it right. don’t know 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 only path that I see, which is that you do let reality hit you in the face. And think you do give people time to give input. do have, before these machines are perfect, before they are powerful, that you actually have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s thought experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that box is something that, there’s a strong chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that I haven’t told before, which is that shortly after we started OpenAI, I remember I was in 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 about it for a moment, if you could choose for that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to 500 years away and people get more time to it right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more real way than any us typing things in computers and developing this technology at time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite the field as it truly lies. Like, if you at the 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 the more you sort 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, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly this 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 thing take away is like, even you think about development of other sort of technologies, about nuclear weapons, people talk about being like a to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s been smooth over time. And so the history, I think, of every technology we’ve developed has been, you’ve to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child collectively teach it 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 important to this may shift, right? We’ve got to take each step we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that will continue to be the best path, but it’s so good we’re honestly having this because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)