We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We from people who are excited, we hear from people who concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, it feels like we’re entering an historic period now where we as a world are going to define a technology that will be so 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 some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s like to a tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and we are exposing it an app for ChatGPT to use on your behalf. And you can do things ask, you know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the for you that you get out of 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 in this case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of 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 AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can “save this for later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this is coming to you, 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 have this ability to inspect how the is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information and to with other applications too. You can say, “Now make shopping list for the tasty thing I was suggesting earlier.” And make a little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within an as long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away all those details you. So you don’t have to be the one who spells out every single sort little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can 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 still click through it and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able access this yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just building these tools. It’s about teaching the AI how use them. Like, what do we even want it to do when we these very high-level questions? And to do this, we use an old idea. If you go 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 a machine, a human child, and then teach it through feedback. a human teacher who provides rewards and punishments as it things out and does things that are either good 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 an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with all of wonderful skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually 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 multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the said, but very importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes things we have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very and offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple months we were able to teach the AI that, “Hey, you really push back on humans in this specific kind of scenario.” And we’ve actually made and lots of improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If think about asking a kid to clean their room, if you’re doing is inspecting the floor, you don’t know you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as it it. It says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a that humans really want to do. It’s much more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check work. And out come citations so you can actually go very easily verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so to me about this whole process is that it’s this many-step between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And think this really shows the shape of something that we expect to be much more common in the future, where we humans and machines kind of very 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 way that’s inspectable and trustworthy. And together we’re able to actually 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 sense just how impossible I’m talking, I think we’re going to be able to rethink almost every of how we interact with computers. For example, think about spreadsheets. They’ve been around some form since, we’ll say, 40 years ago with VisiCalc. don’t think they’ve really changed that much in that time. And here is a specific spreadsheet of 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 the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask about it. And very helpfully, you know, it knows the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, together its world knowledge of knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for a human do, and the AI is happy to help with it.
Now I don’t even what I want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And once again, this is a super high-level instruction with lots of intent it. But I don’t even know what I want. And AI kind of has to infer what I might be in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think … A vision of how we may end up using this technology in the future. A brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second vet who used it to save dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we should all reflect on, about as we consider how to integrate these systems into our world.
And one I believe really deeply, is that getting AI right is going to 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 AI and won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one just to confront reality as it lays. And that we just really hard about like: What is it going to take to make progress here? 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 work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there something also just about the fact that saw something in these language models that meant that if you to invest in them and grow them, that something some 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 deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried lot of things, and one person was working on training a model to the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, we’re seeing out of them feels … it just feels impossible that could come from a prediction machine. Just the stuff 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 the time, colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me moment for you when you saw just something pop just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if you add 40-digit —
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 have add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it to have learned something general, but that it hasn’t fully yet 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 at incredible number of pieces of text. And it is things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really get good at is predicting some of these capabilities. And to do that actually, one of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the stack engineered properly, and then can start doing these predictions. There are all these smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the 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 is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge that you can predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I think of these are questions of degree and scale and timing. And I think one thing people miss, too, is of the integration with the world is also this incredibly emergent, sort of, very powerful too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. But even a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. that we say, OK, as we move on to summaries, we have to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real inside, the system is going to always — we’re never going know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion the scale and the human feedback that you talked about basically going to take it on that journey of getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all experts saying X is going to happen, Y is 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 years 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 technology to really see it in action, because that tells 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 stance you’ve taken, the right way to do this is to put it out in public and then harness all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check on the big companies doing their unknown, possibly thing with AI. And you were going to build models that of, you know, somehow held them accountable and was capable slowing 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 of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I 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 were thinking about how to artificial general intelligence, actually have it benefit all of humanity, like, are you supposed 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 hope you it right. I don’t know how to execute that plan. Maybe someone does. But for 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, is that you do let reality hit you in the face. And I you do give people time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, really were afraid that the number one thing people were going do with it was generate misinformation, try to tip elections. Instead, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a experiment for you. Suppose you’re sitting in a room, there’s box on the table. You believe that in that is something that, there’s a very strong chance it’s absolutely glorious that’s going to give beautiful gifts to family and to everyone. But there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance that actually 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 tell you a story that 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 hotel room just looking out over this wonderful water, all these people a good time. And you think about it for moment, if you could choose for basically that Pandora’s to be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five 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 felt it in the moment. I was like, of you do the 500 years. My brother was in military at the time and like, he puts his on the line in a much more real way any of us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got 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 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 things, they are happening. And if you don’t put them together, you an overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly have this very powerful thing, no one’s had any to adjust, who knows what kind of safety precautions you get. And I think that one thing I take away is like, even think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. 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 has been, you’ve to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this 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 also to say 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 technology, figure out how to provide the feedback, decide what we want from it. And my hope is that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)