We started OpenAI seven years because we felt like something really interesting was happening in AI we wanted to help 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 are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel those 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 are going to define a technology that will be important for our society going forward. And I believe that can manage this for good.
So today, I want show you the current state of that technology and of the underlying design principles that we hold dear.
So the first thing I’m going to 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, we are exposing it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED meal draw a picture of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate 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 can do on your behalf in terms of carrying out your intent. And I’ll 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 going see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and see what it actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, allows us to 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 with applications too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want to how it tastes.
But you can see that ChatGPT is selecting all these different tools without me having tell it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s great experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we a tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re able to change the work the AI if we want to. And so after talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s not just building these tools. It’s about teaching the AI how to use them. Like, what we even want it to do when we ask these very high-level questions? And do this, we use an old idea. If you go back Alan Turing’s 1950 paper 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 human teacher who rewards and punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process imbues 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 what comes next, that green nine up there, to actually solve the math problem.
But we actually to do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback 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 teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s an area of weakness you should gather feedback.” And so when you do that, that’s one way that we really listen to our and make sure we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think asking a kid to clean their room, if all you’re doing is inspecting floor, you don’t know if you’re just teaching them to stuff all the toys in closet. This is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between two foundational blogs on unsupervised learning and learning from human feedback. And the says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. it can actually 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 thought as it does it. It says, I’m just going to search for and it actually does the search. It then it finds the publication and the search results. It then is issuing another query. It’s going to click into the blog post. And all this 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 position where can, if you want, triple-check the work. And out come citations so you can go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so interesting to me about this whole process that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to data for another AI to become more useful to human. And I think this really shows the shape of that we should expect to be much more common the future, where we have humans and machines kind very carefully and delicately 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, oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we this 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 interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that 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 of them. And 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 yet another tool, one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload file and ask questions about it. And very helpfully, you know, it knows the name of the 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 column names like you saw and then the data. And from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to sort of, put its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and that are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work for 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 can the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t know 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 number of authors per paper, series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here we go, a bell curve. You see that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these wonderful things that appear these titles.
But I’m pretty unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is 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 of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make fair projection? So we’ll see, this is the kind ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is what wanted. But I inject my intent, I provide this piece of, you know, guidance. And under the hood, the AI is just writing again, so if you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we end up using this technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog would not be here today had he listened. In meanwhile, he provided the 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 some hypotheses.” He brought that to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is getting AI right is going to require participation from everyone. And that’s for deciding how we it to slot in, that’s for setting the rules the road, for what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just looks different. Just different from anything had anticipated. And so 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 general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at and you think, “Oh my goodness, pretty much every thing about the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who that they’re having to rethink the way that we 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 first question actually is just how hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one just to confront reality as it lays. And that just thought really hard about like: What is it to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And I think the most important thing has been to get teams of people who are very different from each 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 a dry-mouth topic. But isn’t there also just about the fact that you saw something 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 is illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the early days, didn’t know. We tried a lot of things, and 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, model will predict where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you saw this emergence, this of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop that just blew your mind 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, means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s learning the process, but it hasn’t fully generalized, right? It’s like you can’t 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 it to scale up look at an incredible number of pieces of text. And it learning things that you didn’t know that it was to be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And to do that actually, one of 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 tiny. Same is true in machine learning. You have to get every single piece of stack engineered properly, and then you can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I think that what kind of see right now, if you look at this talk, a lot 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 at math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? have to read the 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. And we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re able to actually carry 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, there critics who say that, you know, there’s no real understanding inside, system is going to always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I 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 field the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that what you need. But I think that our approach has been, you’ve got to push to the limits of this technology really see it in action, because that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out there in public and harness all this, you know, instead of just your team giving feedback, the world is now feedback. But … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the companies doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That 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, are forcing us to put this out here without guardrails or we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, all the time. And I don’t think we’re always going get it right. But one thing I think has incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, get this super powerful thing, and then you figure the safety of it and then you push “go,” you hope you got it right. I don’t know 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 other path that see, which is that you do let reality hit in the face. And I think you do give people to give input. You do have, before these machines perfect, before they are super powerful, that you actually have ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one thing people going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So spam is bad, but there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You believe in that box is something that, there’s a very strong chance it’s something absolutely glorious that’s going to beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after started OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people a good time. And 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 to have it be five years away. But if gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the and like, he puts his life on the line in a much more real way than of us typing things in computers and 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 playing the as it truly lies. Like, if you look at the history of computing, I really mean it when I that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put 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 have this very powerful thing, no one’s had any time to adjust, who knows what kind safety precautions you get. And so I think that thing I take away is like, even you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you 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 that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have this extraordinary child that may have superpowers that take to a whole new place. It is our collective to provide the guardrails for this child to collectively it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my hope is that that will to be the best path, but it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)