We started OpenAI seven years ago because we like something really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see how far this whole has come since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from people who feel those emotions at once. And honestly, that’s how we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define technology that will be so important for our society going forward. And I believe we can manage this for good.
So today, I want show you the current state of that technology and 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 a tool for an rather than building it for a human. So we have new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on behalf. And you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort 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 for the meal, but a very, very detailed spread. let’s see what 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 what can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This all generated by the AI as we speak. So actually don’t even know what 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, example, memory. You can say “save this for later.” And the interesting thing about these is they’re very inspectable. So you get this little pop up here that “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 see that what it actually did was write a prompt just a human could. And so you sort of have ability to inspect how the machine is using these tools, which allows us to feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make it a little for the AI. “And tweet it out for all the TED viewers there.”
(Laughter)
So if you do make this wonderful, wonderful meal, definitely want to know how it tastes.
But you can see that ChatGPT is 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 used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I would you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able sort of take away all those details from you. 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 live demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And can see we sent a list of ingredients to Instacart. Here’s you need. And 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 actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there 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. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do when we ask these high-level questions? And to do this, we use an old idea. If go back to Alan Turing’s 1950 paper on 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, and then it through feedback. Have a human teacher who provides rewards and punishments it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine an unsupervised learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this process imbues it all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We have the AI try out multiple things, give multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend that one plus equals three and run with it.” So we had to some feedback data. Sal Khan himself was very kind offered 20 hours of his own time to provide feedback to the alongside our team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of to the models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really listen our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a to clean their room, if all you’re doing is inspecting the floor, you don’t know if you’re teaching them to stuff all the toys in the closet. This a nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we will to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine as time goes on. And 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 unsupervised learning and learning from human feedback. And the model says two months passed. But it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s a thing that humans really want to do. It’s much 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 citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting 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 another to become more useful to a human. And I think this really 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, feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you sense of just how impossible I’m talking, I think we’re going to be able to almost every aspect 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. I don’t they’ve really changed that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. you can see there the 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 a Python interpreter, it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions about it. very helpfully, you know, it knows the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The information here is the name of the file, the column names you saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. has to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these are values and so therefore it’s a number of authors in the paper,” like all of that, that’s for a human to do, and the AI is 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 lots of intent behind it. But I don’t even what I want. And the AI kind of has to what I might be interested in. And so it comes up with some good ideas, I think. a histogram of the number of authors per paper, time of papers per year, word cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three is kind the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and it off the cliff. What could be going on there? By the way, all this is code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the is that the year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 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 ambitious one.
(Laughter)
So you know, again, I feel there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of an overreach it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI is writing code again, so if you want to inspect what it’s doing, it’s very possible. now, it does 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 to the slide again. This slide a parable of how I think we … A vision of how we may end up using technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just 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 need to talk to a professional, here are hypotheses.” He brought that information to a second vet who it to save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this something we should all reflect on, think about as consider how to integrate these systems into our world.
And one thing I believe really deeply, that getting AI right is going to require participation from everyone. And that’s deciding how we want it to slot in, that’s for setting the rules of the road, for what AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just looks different. different from anything people had anticipated. And so we all have to become literate. And that’s, honestly, of the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of 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 people this, you look at that and you think, “Oh goodness, pretty much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to 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, guess my first question actually is just how the have you done this?
(Laughter)
OpenAI has a few employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot of very deliberate choices from the days. And the first one was just to confront reality as lays. And that we just thought really hard about like: What is it to take to make progress here? We tried a lot things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always knew that was what wanted to be, was a deep learning lab, and exactly how to it? I think that in the early days, we didn’t know. We tried a of things, and one person was working on training model to predict the next character in Amazon reviews, he got a result where — this is a process, you expect, you know, the model will predict where the commas go, where the nouns verbs are. But he actually got a state-of-the-art sentiment analysis classifier of it. This model could tell you if a review was or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of them … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key idea of is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these ant colonies show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers and traffic jams. me one moment for you when you saw just something pop just blew your mind that you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like 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, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good at predicting some of these emergent capabilities. And to do actually, one of the things I think is very in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and 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 GPT-4 blog post, you can see all of these in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. We basically at some models that are 10,000 times or 1,000 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 arises from this. it’s fundamental to what’s happening here, that as you scale up, things that you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all of these are questions 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 thing too. And that’s one of the reasons that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you at this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build up a record with these machines that they’re able to actually out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort of making the machine be aligned with you.
CA: So we’re going hear later in this session, there are critics who that, you know, there’s no real understanding inside, the system going to always — we’re never going to know that it’s generating errors, that it doesn’t have common sense and so forth. it your belief, Greg, that it is true at one moment, but that the expansion of the scale and human feedback that you talked about is basically going to it on that journey of actually getting to things like and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short is yes, I believe that is where we’re headed. I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is to happen, Y is how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right maybe 70 years plus one something like that is what you need. But I think that our approach has always been, you’ve got push to the limits of this technology to really see it in action, that tells you then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s quite controversial stance you’ve taken, that the right way to do this is to put it out there public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil thing 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 that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, are forcing us to put this out here without guardrails or we die. You know, how do you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? that default plan of 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,” you hope you got it right. I don’t know how to execute plan. Maybe someone else does. But for me, that was terrifying, it didn’t feel right. And so I think that alternative approach is the only other path that I see, which is that you do let reality you in the face. And I think you do give time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability to see them 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 misinformation, try to tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on the table. You that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give gifts to your family and to everyone. But there’s actually also a one percent in the small print there that says: “Pandora.” And there’s chance that this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out over this wonderful water, all these people a good time. And you think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 years away, 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 away and people get more time to get it right, do you pick? And you know, I just really felt it in the moment. was like, of course you do the 500 years. My brother was in military at the time and like, he puts his life on the line in much more real way than any of us typing things in and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it when I say that this is industry-wide or even just almost like a human-development- of-technology-wide shift. And more that you sort of, don’t put together the pieces are 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 someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this powerful thing, no one’s had any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think that you look at capability, it’s been quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally 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 … model you want us to have is that we have this extraordinary child that may have superpowers that take humanity to a new place. It is our collective responsibility to provide guardrails for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I it’s true. And 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 important today that we all do get literate in this technology, figure how to provide the feedback, decide what we want it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you much for coming to TED and blowing our minds.
(Applause)