We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted to help it in a positive direction. It’s honestly just really amazing to 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, others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right where we as a world are going to define a that will be so important for our society going forward. And I believe that we manage this for good.
So today, I want to show you current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to show you is what it’s to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of what can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. is all generated by 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 for later.” And the interesting thing about these tools is they’re very inspectable. So you get little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you can look under hood and see that what it actually did was write a prompt like a human could. And so you sort of have this ability to inspect the machine is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you 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 suggesting earlier.” make it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So you do make this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this language interface on top of tools, the AI is able sort of take away all those details from you. So you don’t have to the one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take look at the Instacart shopping list while we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And 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 the slides. Now, the important about how we build this, it’s not just about building these tools. It’s about teaching the how to use them. Like, what do we even want it to when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program answer to this. Instead, you can learn it. You could build a machine, like a human child, then teach it through feedback. Have a human teacher who provides rewards and punishments as it things out 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 Turing would have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only to actually complete that math problem, to say what 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 teach the AI what to do those 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 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 it teach, to sort of infer your intent and apply in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 to Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus equals three and run with it.” So we had collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were able to teach AI that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that is kind of like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, high-quality feedback is a hard thing. If you think about a kid to clean their room, if all you’re is inspecting the floor, you don’t know if you’re just teaching them stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to 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 better feedback to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are not 100-percent reliable, although they’re better every time we provide some feedback. But we can use the AI to fact-check. And it can actually check own 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 browsing tool where the model issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and it actually does search. It then it finds the publication date and the results. It then is issuing another search query. It’s to click into the blog post. And all of 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 be in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI to become more useful to human. And I think this really shows the shape of something that we should expect to be more common in the future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want to 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 trustworthy. And together we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we will be able to impossible problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really changed that much that time. And here is a specific spreadsheet of all the papers on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the data right here. But let me show you ChatGPT take on how to analyze a data set 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 data scientist would. And so can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse for you.” The only information here is the name of the file, column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together its world of knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s these things are and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now I don’t even know I want to 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 even know I want. And the AI kind of has to what I might be interested in. And so it comes with some good ideas, I think. So a histogram the number of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, it can actually do it. we go, a nice bell curve. You see that three is kind the most common. It’s going to then make this plot of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back 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 the cut-off date I believe. Can you use that make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was I 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 I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just code 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 the title. I didn’t for that, but it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of how I think we … A vision of how may end up using this technology in the future. person brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, said, “I am 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. You cannot overly rely on them. But story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how 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 thing to take away from this talk, it’s that this technology looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, need to 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, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google thousands of employees working on artificial intelligence. Why is it you who’s come 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. you look at the compute progress, the algorithmic progress, the data progress, of 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 thought really hard about like: What is it going take to make progress here? We tried a lot things that didn’t work, so you only see the things that did. And think that the most important thing has been to get teams of people who are very from each other 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 something also just about the fact that you saw in these language models that meant that if you to invest in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is illustrative, right? I think that high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, 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 a model to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, know, the model will predict where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So think this helps explain the riddle that baffles everyone at this, because these things are described as prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key idea emergence is that when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough of them together, you get these ant that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
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 really interesting thing actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get 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 there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this to adding numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look an incredible number of pieces of text. And it is learning things that 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 really get good at is predicting some of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single 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 you look our GPT-4 blog post, you can see all of these curves in there. now we’re starting to be able to predict. So were able to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 times smaller. And so there’s something about this that actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing miss, too, is sort of the integration with the world is also incredibly emergent, sort of, very powerful thing too. And so that’s of the reasons that we think it’s so important to deploy incrementally. so I think that what we kind of see now, if you look at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with these machines that they’re able to carry out our intent. And I think we’re going to to produce even better, more efficient, more reliable ways of scaling this, sort of like making the be aligned 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, the system is 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 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 sure 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 all these 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 might be right 70 years plus one or something like that is you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there in public and harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are going emerge, it 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 the great of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. know, how do you, like, make the case that what you done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one I think has been incredibly important, from the very beginning, when we were 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 build in secret, you get super powerful thing, and then you figure out the safety of it and then you “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this approach is the only other path that I see, is that you do let reality hit you in the face. And I think you do give time to give input. You do have, before these are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is that, there’s a very strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people having good time. And you think about it for a moment, if could choose 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 better have it be five years away. But if it gets be 500 years away and people get more time to it right, which do you pick? And you know, I just really felt it in moment. I was like, of course you do the 500 years. My brother was in the military the time and like, he puts his life on the line in a much more real than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on 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 together the pieces that there, right, we’re still making faster computers, we’re still 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 have very powerful thing, no one’s had any time to adjust, who knows what kind of safety precautions get. And so I think that one thing I take is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero to one, of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it to be wise and not to tear us down. Is that basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And my hope that that will continue to be the best path, but it’s so good we’re honestly this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)