We started OpenAI seven years ago because we felt something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field come since then. And it’s really gratifying to hear people like Raymond who are using the technology we are building, others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we from 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 we as a world are going to define a technology that will be important for our society going forward. And I believe we can manage this for good.
So today, I to show you the current state of that technology some of the underlying design principles that we hold dear.
So the first thing I’m to show you is what it’s like to build tool for an AI rather than building it for a human. So have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here we go, it’s just the idea for the 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 power what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is 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.
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I’m getting just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” 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, upcoming months. And you can look under the hood and see that what it actually did write a prompt just like a human could. And so 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 me show you what it’s like to use information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I suggesting earlier.” And make it a little tricky for AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to thinking of, well, we these apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away those details from you. So you don’t have to be the one who spells out every sort of little piece 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 a look at the Instacart shopping list we’re at it. And you can see we sent a of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort of the actual quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted our review, which is also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the work of the if we want to. And 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 thing about 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 it to do 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 build a machine, like a human child, and then teach it through feedback. Have human teacher who 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 Turing would have called a child machine through an learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to say what next, that green nine up there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better that one.” And this reinforces not just the specific thing that the said, but very importantly, the whole process that the used to produce that answer. And this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the 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 able to students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and run with it.” So we to collect some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide to the machine alongside our team. And over the course of a of months we were able to teach 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. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a bat signal to team to say, “Here’s an area of weakness where you should gather feedback.” so when 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 asking kid to clean their room, if all you’re doing inspecting the floor, you don’t know if you’re just them to stuff all the toys in the closet. is a nice DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI fact-check. And it can actually check its own work. You say, fact-check this for me.
Now, in this case, I’ve actually given 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 does it. It says, I’m just going to search for this and it actually the search. It then it finds the publication date and search results. It then is issuing another search query. It’s to click into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations so you can go and very easily verify any 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 to the side. And so thing that’s so interesting to me about whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI become more useful to a human. And I think this shows the shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed in how they into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, we get this process right, we will be able solve impossible problems.
And to give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect of how we 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 specific spreadsheet of all the AI papers on the for 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 how to analyze a data set this.
So we can give ChatGPT access to yet another tool, this one a Python interpreter, it’s able to run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the of the file, the column names like you saw then the actual data. And from that it’s able infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together world knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in 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 know what want to ask. So fortunately, you can ask the machine, “Can make some exploratory graphs?” And once again, this is a high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI kind of has infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like were on an exponential and it dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look bad. Of course, the problem is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more I wanted out the machine here. I really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, magically that this is what I wanted. But I my intent, I provide 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 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 back the slide again. This slide shows a parable of how I we … A vision of how we may end up using technology in the future. A person brought his very dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And 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, here some hypotheses.” He brought that information to a second vet who used 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 ChatGPT as a brainstorming partner was able to achieve 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 is to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting the rules of 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 looks different. Just from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, I believe 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 … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people this, you look at that and you think, “Oh my goodness, pretty much every single thing the way I work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks they’re having 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 you 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 the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you look at the progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we a lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And we just thought 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 only the things that did. And I think that the most important thing has to get teams of people who are very different each other to work together harmoniously.
CA: Can we the water, by the way, just brought here? I think we’re going to 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 that if you continue to invest in them and grow them, something at some point might emerge?
GB: Yes. And think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, didn’t know. We tried a lot of things, and one person was working on a model to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, model will predict where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model tell you if a review was positive or negative. mean, today we are just like, come on, anyone can that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant 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 and centers and traffic jams. Give me one moment for you when you saw just something pop that just your mind that you just did not see coming.
GB: Yeah, well, so can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will it, which means it’s really learned an internal circuit how to do it. And the really interesting thing is actually, if you it 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 this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible number pieces of text. And it is learning things that you didn’t that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to get good at is predicting some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort engineering quality. Like, we had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is true in learning. You have to get every single piece of the stack properly, and then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re starting be able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s something this that is actually smooth scaling, even though it’s still days.
CA: So here is, one of the big fears then, arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge 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 one of the reasons that think it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s very easy to look at math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you 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 the important thing will that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able actually carry out our intent. And I think we’re going to have to even better, more efficient, more reliable ways of scaling this, sort of like making the machine aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at any one moment, but the expansion of the scale and the human feedback you talked about is basically going to take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. you 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 think that OpenAI approach here 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 these experts saying X is going 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 always been, you’ve got to push the limits of this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the original story that I heard OpenAI when you were founded as a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some of their criticisms have been, 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 the time. Like, seriously all time. And I don’t think we’re always going to get right. But one thing I think has been incredibly important, from the very beginning, we were thinking about how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to that, right? And that default plan of being, well, build in secret, you get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. for me, that was always terrifying, it didn’t feel right. And so I think this alternative approach is the only other path that see, which is that you do let reality hit you the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought for you. Suppose you’re sitting in a room, there’s a box on the table. You believe that in box is something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing the small 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, not. I think you don’t do it that way. honestly, like, I’ll tell you a story that I haven’t actually before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if 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 to have be five years away. But if it gets to be 500 away and people get more time to get it right, do you pick? And you know, I just really it in the moment. I was like, of course do the 500 years. My brother was in the military at 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 the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look at whole history of computing, I really mean it when I say that this an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. if 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 this very powerful thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I think that one thing take away is like, even you think about development other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change in what could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to tear us all down. Is that basically model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take each step 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 is that that will 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 blowing our minds.
(Applause)