We started OpenAI seven years ago we felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole field come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people who are excited, we hear 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 like we’re entering historic period right now where we as a world are going to define technology that will be so important for our society going forward. And I that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which images, and we are exposing 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 draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. And here we go, it’s not the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of what can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that “use the DALL-E app.” And by the way, this is to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it actually did was write a 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 for later, and let me you what it’s like to use that information and to integrate other applications too. You can say, “Now make a 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 do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all different tools without me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the interface. Like, we are so used to thinking of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort take away all those details from you. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is live demo, so sometimes the unexpected will happen to us. But let’s take a look at Instacart shopping list while 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 click through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented way to build them. And now have a 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 to inspect, we’re able to change the work of AI if we want to. And so after this talk, will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll program an answer to this. Instead, you can learn it. You could build machine, like a human 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 how 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 show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI what to do with skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And this reinforces 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 teach the AI are what you’d expect. For example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this so 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 will pretend that one plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And the course of a couple of months we were to teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And when you push 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 users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise 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 two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can search queries and click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to for this and it actually does the search. It then finds the publication date and the search results. It then issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months 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 a human, using this fact-checking tool doing it in order to produce data for another to become more useful to a human. And I think really shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and we want to solve that problem. We make sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I that over time, if we get this process right, we be able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been around in some since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that 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 of them. And you see there the data right here. But let me show you ChatGPT take on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a scientist would. And so you can just literally upload a file and ask questions 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 you.” The only information here is the name of the file, the column names like saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what these are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work a human to do, and the AI is happy to help it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three is of the most common. It’s going to then make this nice plot of 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 way, all this is Python code, you can inspect. And then we’ll see cloud. So you can see all these wonderful things that in these titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were posted by April 13?] So April 13 was the cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like there was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit of an overreach for it to have of, inferred magically that this is what I wanted. But inject 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 what it’s doing, it’s very possible. And now, it does the 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 think we … A vision of how we may up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And the dog would not here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as brainstorming partner was able to achieve an outcome that would have happened otherwise. I think this is something we should all on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding how we it 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 from this talk, it’s that this technology just looks different. Just from 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 achieve the 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 every mind here there’s a feeling of reeling. Like, I suspect a very large number of people viewing this, you at that 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 right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is you who’s come up with this technology that shocked the world?
Greg Brockman: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we thought really hard about like: What is it going take to make progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has to get teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you something in these language models that meant that if you to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training a model to the next character in Amazon reviews, and he got result 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 are just like, come on, anyone can do that. But this was the time that you saw this emergence, this sort of semantics that emerged from this underlying 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 are described as prediction machines. And yet, what we’re out of them feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like suburbs and cultural and traffic jams. Give me one moment for you when you saw just something that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And the 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 learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So had to have learned something general, but that it 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 is that you’ve allowed it to scale up and at an incredible number of pieces of text. And it is things that you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good 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 entire stack. When you think about building a rocket, every tolerance has to incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, the on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one people 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 important to deploy incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what 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 that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a thing to supervise. Like, how do you know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And I think that the important thing will be that we take this by step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have 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 even better, more efficient, more reliable ways scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that 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 scale and the human feedback that you talked about is basically going take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, short 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 in the face, right? It’s like this field is field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be right maybe 70 plus one or something like that is what you need. I think that our approach has always 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 on to new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is to it out there in public and then harness all this, know, instead of just your team giving feedback, the world now giving feedback. But … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were going to build models that sort of, know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always to get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, how are you to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” and you you got it right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this approach is the only other path that I see, which is that do let reality hit you in the face. And I think you do give people time to input. You do have, before these machines are 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 people going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You that in that box is something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small there that says: “Pandora.” And there’s a chance that this could unleash unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Puerto Rico for AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having good time. And you think about it for a moment, you could choose for basically that Pandora’s box to five years away or 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be years away. But if it gets to be 500 years away and get more time to get it right, which do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the at the time and like, he puts his life on the in a much more real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still faster computers, we’re still improving the algorithms, all of these things, they are happening. And if don’t put them together, you get an overhang, which 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 adjust, who knows what kind of safety precautions you get. And so I think that one thing I take is like, even you think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, in what humans could do. But I 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 been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach to be wise and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)