We OpenAI seven years ago because we felt like something really interesting happening in AI and we wanted to help steer it 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 we are building, and others, for so many wonderful things. We hear from people who excited, we hear from people who are concerned, we hear from people feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering historic period right now where we as a world are going to define technology that will be so important for our society forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show is what it’s like to build a tool for an rather than building it for a human. So we 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 and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get out of ChatGPT. here we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, also generates an image. And that is something that really expands power of what it can do on your behalf in terms of out your intent. And I’ll point out, this is all live demo. This is all generated by the AI we speak. So I actually don’t even know what we’re to see. This looks wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended with other tools too, for example, memory. You can “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop up here says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can look under the hood and that what it actually did was write a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows us provide feedback to them.
Now it’s saved for later, and let me you what it’s like to use that information and to integrate with other too. You can say, “Now make a shopping list the tasty thing I was suggesting earlier.” And make it a little 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 it tastes.
But you can 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 of thinking the user interface. Like, we are so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a experience within an app as long as you kind know the menus and know all the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of away all those details from you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed to happen.
And as I said, this a live demo, so 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 sent a 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, still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have a tweet that’s been for 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 of the AI if we want to. And so after this talk, you will able to access this yourself. And there we go. Cool. 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 the 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 back to 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. Have a teacher who provides rewards and punishments as it tries things out and does things that are either or bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this process 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 comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, which is to teach AI what to do 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 than that one.” And this not just the specific thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows to generalize. It allows it to teach, to sort of infer your intent and apply it in that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one equals three run with it.” So we had to collect some feedback data. Sal Khan himself was kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the course of a couple of we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And the same sort of applies to AI. As we move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain thought as it does it. It says, I’m just going search for this 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 blog post. all of this 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 driver’s seat, to be in this manager’s position where can, if you want, triple-check the work. And out come so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back 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 really shows the shape of that we should expect to be much more common in the future, where we have humans and machines of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We sure that the humans are providing the management, the oversight, the feedback, and the machines are operating 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 we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access to yet tool, this one 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 questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information is the name of the file, the column names like saw and 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 papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, and the 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 a super high-level instruction with of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the number of authors per paper, time series papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make this nice plot of the papers year. Something crazy is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful 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 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 in 2022 were even posted by April 13?] So April 13 was the cut-off I believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s little bit of an overreach for 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 you want inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know what I want.
Now we’ll back to the slide again. This slide shows a parable of how I think we … A of how we may end up using this technology in the future. A brought his very sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the dog would not be here today had listened. In 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 are some hypotheses.” He that information to a second vet who used it 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 that would not have happened otherwise. think this is something we should all reflect on, think about as we consider how to integrate these into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s one thing take away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that artificial general intelligence 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 about way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred employees. Google has thousands of employees 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 all on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the one was just to confront reality as it lays. that we just thought really hard about like: What is it going to to make progress here? We 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, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried lot of things, and one person was working on training a model to predict the next character Amazon reviews, and he got a result where — this a syntactic process, you expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can do that. But this was the 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 it goes.
CA: I think this helps explain the riddle that baffles everyone looking at this, because these things are as prediction machines. And yet, what we’re seeing out of them feels … it just feels impossible 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, different things emerge. It happens all the time, ant colonies, single ants run around, when bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you when saw just something pop that just blew your mind that 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 do it, which means it’s really an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have learned general, but that it hasn’t really fully yet learned that, Oh, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it scale up and look at an incredible number of pieces of text. And it learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is predicting some of these emergent capabilities. 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 stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in learning. You have to get every single piece of stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able to predict. we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times 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 scale up, things emerge that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. I think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, powerful thing 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 on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book is any good? You have to read the whole book. one wants to do that.
(Laughter) And so I that the important thing will be that we take this step by step. And that say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up track record with these machines that they’re able to 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 be aligned with you.
CA: So we’re going to hear later in this session, there are critics 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, it doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically going take it on that journey of actually 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 think that the OpenAI, I mean, the answer is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, let reality hit you in face, right? It’s like this field is the field broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might right maybe 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got to push to 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. And we 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 out there in public and then harness all this, you know, of just your team giving feedback, the world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the sort of check on the big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, such shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails we die. You know, how do you, like, make case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re always going to it right. But one thing I think has been incredibly important, the very beginning, when we were thinking about how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? And that 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 push “go,” and hope you 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. so I think that this alternative approach is the other path that I see, which is that you do let reality you in the face. And I think you do give people time to input. You do have, before these machines are perfect, they 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 thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that much worse. Here’s a thought 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 your 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 evils 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 that I haven’t told before, which is that shortly after we started OpenAI, I remember was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a good time. you think about it for a 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, 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, I just felt it in the moment. I was like, of course you the 500 years. My brother was in the military at the time and like, puts his life on the line in a much more real than any of us typing things in computers and this technology 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 playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the more 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. if you don’t put them 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 have very powerful thing, no one’s had any time to adjust, 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 about nuclear weapons, people talk about like a zero to one, sort of, change in what humans could do. But actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do incrementally and you’ve got to figure out how to manage it for each 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 have superpowers that 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 all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important today that we do get literate in this technology, figure out how to provide the feedback, decide what we want it. And my hope is that that will continue to the best path, but it’s so good we’re honestly having this debate 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)