We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where as a world are going to define a technology that will be so important our society going forward. And I believe that we can this for good.
So today, I want to show you the current of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to show you is what it’s like to build tool for an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you can do things 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 care the details for you that you get out of ChatGPT. And here go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going to get. ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save 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, all ChatGPT users, upcoming months. And you can look under the hood and see that it actually did was write a prompt just like a human could. And you sort of have this ability to inspect how the machine is using these tools, allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we 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 app as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all those details from you. you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional is still very 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. now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. so after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the important thing how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we use old idea. If you go back to Alan Turing’s 1950 paper the Turing test, he says, you’ll never program an 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 provides rewards and punishments as it tries things out 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 have called child machine through an unsupervised learning process. We just show the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to 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 AI are not 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 to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals and run with it.” So we had to collect some data. Sal Khan himself was very kind and offered 20 of his own time to provide feedback to the machine our team. And over the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so when do that, that’s one way that we really listen to users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just teaching them to 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 our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to the machine as time goes on. And let me show you what mean.
For example, you can ask GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. it can actually check its own work. You can say, fact-check this 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 and click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going to search for and it actually does the search. It then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a very tedious task. It’s not 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 where you can, you want, triple-check the work. And out come citations so you can actually go and very verify any piece of this whole chain of reasoning. it actually turns out two months was wrong. Two months and week, that was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me 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 AI to become more useful to a human. And think this really shows the shape of something that we should to be much more common in the future, where we have humans and machines kind of very carefully delicately designed in how they fit into a problem and we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re to actually create even more trustworthy machines. And I that over time, if we get this process right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, I think we’re to be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on to analyze a data set like this.
So we give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just upload a file and ask questions about it. And very helpfully, you know, knows the name of 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, the column like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are integer values so therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the is happy to help with it.
Now I don’t even know what I 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 of intent behind it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. So a histogram of 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 kind of the most common. It’s going to then this nice plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about this 2023 thing. It this year look really bad. Of course, the problem is the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you use that to make a projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this what I wanted. But I inject my intent, I this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so if 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 to the slide again. This slide shows parable of how I think we … A vision how we may end up using this technology in future. A person brought his very sick dog to the vet, the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would not be here today had listened. In the meanwhile, he provided the blood test, like, the full records, to GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” He that information to a second vet who used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, think, shows that a human with a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just different. Just different from anything people had anticipated. And so we have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within mind 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 way I work, I need to rethink.” Like, there’s just 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 guess my first question actually is just the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands employees working on artificial intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the 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 first one was just to confront reality as it lays. that we just thought really hard about like: What it going to take to make progress here? We a lot of things that didn’t work, so you only the things that did. And I think that the most important has been to get teams of people who are very different from each other to work 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 about the fact that you saw something in these language models that meant if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where the go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain riddle that baffles 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 you us just 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 of them together, you get these ant colonies that 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 and cultural centers and traffic jams. Give me one moment you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s learned an internal circuit for how to do it. And the really interesting thing actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here that you’ve allowed it to scale up and look at an incredible number of pieces text. And it is learning 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 good at is predicting some of these emergent capabilities. And to do that actually, one of the things think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. now we’re starting to be able to 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 that is 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, that as you scale up, emerge that you can maybe predict in some level of confidence, but it’s capable of you. Why isn’t there just a huge risk of truly terrible emerging?
GB: Well, I think all of are questions of degree and scale and timing. And think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see right now, if you look at talk, a lot of what I focus on is providing high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do know if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, as we on to book summaries, we have to supervise this properly. We have to build up a track record these machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real understanding inside, the system is going to — we’re never going to know that it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the scale and the human that you talked about is basically going to take on that journey of actually getting to things like truth and wisdom so forth, with a high degree of confidence. Can be 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 that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that what you need. But I think that our approach has been, you’ve got to push to the limits of technology to really see it in action, because that tells you then, oh, here’s how we move 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 to do this is to put it out in public and then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of what heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here without proper or we die. You know, how do you, like, make the case that what you have done 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 going to get right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how build artificial general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan being, well, you build in secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” and you hope 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 think that this alternative approach is the only other path that I see, which 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, before are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. Suppose you’re in 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 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 small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable on the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly we started OpenAI, I remember I was in Puerto for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these people having a good time. And you think about for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, for you personally, it’s better to have it be years away. But if it gets to be 500 years and people get more time to get it right, which do you pick? And you know, just really felt it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really 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 look the whole history of computing, I really mean it when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, 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 got 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 that we have birthed this extraordinary child that may have that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively it 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 we encounter it. I think it’s incredibly important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want from it. And hope is 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)