We started OpenAI seven years ago because felt like something really interesting was happening in AI and we 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 to hear from people like Raymond who are using the technology we are building, and others, 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 an historic period right now where we as a world going to define a technology that will be so for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology and some of 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 for AI rather than building it for a human. So we have a new DALL-E model, which images, and we are exposing it as an app ChatGPT to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and a picture of it.
(Laughter)
Now you get all the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see we’re going to get. But ChatGPT doesn’t just generate images in 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 terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by the AI as we speak. So actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop up that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look under the hood see that what it actually did was write a just like a human could. And so you sort of this ability to inspect 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 with applications too. You can say, “Now make a shopping list for the tasty thing I was earlier.” And make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely want know how it tastes.
But you can see that is selecting all these different tools without me having to it explicitly which ones to use in any situation. this, I think, shows a new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to of take away all those details from you. So you don’t to be the one who spells out every single sort of piece 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 a look at the Instacart shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, you still click through it and sort of modify the actual quantities. And that’s something that I think shows that they’re going away, traditional 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 AI if we want to. so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the how to use them. Like, what do we even want to do when we ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through an learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to the AI what to do with those skills. And 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 reinforces not just the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that 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, 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 will pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself was very kind and 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months we were able to the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind of like sending a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so 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 hard thing. If think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t know if you’re just teaching them to stuff the toys in the closet. This is a nice DALL-E-generated image, by the way. And the sort of reasoning applies to AI. As we move harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs unsupervised learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And actually writes out its whole chain of thought as does it. It says, I’m just going to search 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 the post. And all of this you could do, but it’s very tedious task. It’s not a thing that humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in to produce data for another AI to become more to a human. And I think this really shows the shape of that we should expect to be much more common in future, where we have humans and machines kind of carefully and delicately designed in how they fit into a and how we want to solve that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in a 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 this process right, will be able to solve impossible problems.
And to give you a of just how impossible I’m talking, I think we’re going to be able to rethink every aspect of how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But let me show you the ChatGPT take how to analyze a data set like this.
So we can give ChatGPT access to yet another tool, one a Python interpreter, so it’s able to run code, like a data scientist would. And so you can just literally upload a file 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 file, “I’ll parse it for you.” The only information is the name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what 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 work a human to do, and the AI is happy to help with it.
Now I don’t know what I want to ask. So fortunately, you can 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 know what I want. And AI kind of has to infer what I might interested in. And so it comes up with some good ideas, I think. So histogram of the number 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 actually do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential and it dropped off the cliff. What be going on there? By the way, all this is Python code, 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 year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by 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 really wanted it to this thing, maybe it’s a little bit of an for it to have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, if you 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 ask that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of how I we … A vision of how we may end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and 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 a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have otherwise. I think this is something we should all on, think about as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting rules of the road, for what an AI will and won’t do. And if there’s one thing to away from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that a very large of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to rethink the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of working on artificial intelligence. Why is it you who’s 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 look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just to confront as it lays. And that we just thought really hard about like: What is going to take to make progress here? We tried a lot of things didn’t work, so you only see the things that did. I think that the most important thing has been to teams of people who are very different from each other work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language models meant that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I think that high level, learning, like we always knew that 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 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, the model predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. this was the first time that you saw this emergence, this sort of semantics that from this underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles everyone looking at this, because these are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that come from 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 them together, get these ant colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment for you when you saw just something pop that blew your mind that you just did not see coming.
GB: Yeah, well, you 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 circuit for how do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more than there are in the universe. So it had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. And do that actually, one of the things I think is very undersung in this field is of engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece the stack engineered properly, and then you can start these predictions. There are all these incredibly smooth scaling curves. They you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you see all of these curves in there. And now we’re starting to be to predict. So we were able to predict, for example, the performance on 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 here is, one of big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge you 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 these are questions of degree and scale and timing. And I think one thing people miss, too, sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we kind of see right now, if look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this step step. And that we say, OK, as we move on book summaries, we have to supervise this task properly. We have to up a track record with these machines that they’re to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in session, there are 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 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 expansion of the scale and the human feedback that you talked about is going to take it on that journey of actually getting to like truth and wisdom and so forth, with a high of confidence. Can you be sure of that?
GB: Yeah, well, I think the OpenAI, I mean, the short answer is yes, 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 experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like that is what you need. I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can 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 and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, possibly evil with AI. And you were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, 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 of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and you figure out the safety of it and then push “go,” and you hope you got it right. I don’t know how execute that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the other path that I see, which is that you do let reality hit you the face. And I think you do give people time to input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your 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 box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about it for a moment, you could choose for basically that Pandora’s box to be five years away or 500 away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better have it be five years away. But if it to be 500 years away and people get more to get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a more real way than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing field as it truly lies. Like, if you look at the history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces that are there, right, we’re making faster computers, we’re still improving the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, which that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, even think 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 that if look at capability, it’s been quite smooth over time. And 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 to manage it each moment that you’re increasing it.
CA: So what I’m is that you … the model you want us to have is that have birthed this extraordinary child that may have 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 basically the 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 as we encounter it. And I it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. my hope is that that will continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and our minds.
(Applause)