We OpenAI seven years ago because we felt like something really interesting was happening in AI and we to help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology are building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, we from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels we’re entering an historic period right now where we as a are going to define a technology that will be important for our society going forward. And I believe that we can this for good.
So today, I want to show the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an 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 of the, sort of, and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s not just the for the meal, but 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, it doesn’t generate text, it also generates an image. that is something that really expands the power of what it do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking 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 tools is they’re inspectable. So you get this 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 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 these tools, which allows us to provide feedback to them.
Now it’s saved for later, and me show you what it’s like to use that information and to integrate with other applications too. You say, “Now make a shopping list for the tasty 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 see that is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows new way of thinking about the user interface. Like, are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s a experience within 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 to be polite.
(Laughter)
And having this unified language interface on top of tools, AI is able to sort of take away all details from you. So you don’t have to be the one who spells out every single of little piece of what’s supposed to happen.
And as I said, is a live demo, so sometimes the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional UI 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 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 for review, which is also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to access yourself. And there we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do even want it to do when we ask these high-level questions? And to do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You build a machine, like a human child, and then it through feedback. Have a human teacher who provides and punishments as it tries things out and does things are either good or bad.
And this is exactly how we ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just show it the whole world, whole internet and say, “Predict what comes next in text you’ve seen before.” And this process imbues it with all sorts of wonderful skills. For example, you’re shown a math problem, the only way to complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But we actually to do a second step, too, which is to teach the what to do with those skills. And for this, we provide feedback. We the 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 AI said, but importantly, the whole process that the AI used to produce answer. And this allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the AI not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will 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 and offered 20 of his own time to provide feedback to the alongside our team. And over the course of a of months we were able to teach the AI that, “Hey, you really should back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs in ChatGPT, that actually is kind of like sending a bat 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 really listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the toys in the closet. This is nice DALL-E-generated image, by the way. And the same of reasoning applies to AI. As we move to tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is happy to help. It’s happy to us provide even better feedback and to scale our to supervise the machine as time goes on. And let show you what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says two passed. But is it true? Like, these models are 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually its own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given AI a new tool. This one is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and it actually does the search. It then it the publication date and the search results. It then is issuing another search query. It’s to click into the blog post. And all of 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 the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back the side. And so thing that’s so interesting to me this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using this fact-checking tool is doing it order to produce data for another AI to become more useful to a human. And I this really shows the shape of something that we should to be much more common in the future, where we have humans machines kind of very carefully and delicately designed in how they fit a problem and how we want to solve that problem. We make sure that humans are providing the management, the oversight, the feedback, and the machines are operating in a that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to give you a sense of just how I’m talking, I think we’re going to be able to rethink almost every aspect of we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with 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 arXiv for past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT on how to analyze a data set like this.
So can give ChatGPT access to yet another tool, this a Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and ask 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 information here is the name of the file, the names 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 its 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 paper,” like all of that, that’s work for a to do, and the AI 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 some exploratory graphs?” And once again, is a super high-level instruction with lots of intent it. But I don’t even know what I want. And the AI kind of has to what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number 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 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 the papers per year. Something crazy is happening in 2023, though. like we were on an exponential and it dropped off the cliff. What could be going there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can see all these wonderful things appear in these titles.
But I’m pretty unhappy about this 2023 thing. makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back on machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off date I believe. you use that to make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more I wanted of 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. under the 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 the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll back to the slide again. This slide shows a of how I think we … A vision of how may end up using this technology in the future. A person his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a vet who used 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 with a medical professional and with ChatGPT as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something we all reflect on, think about as we consider how to these systems into our world.
And one thing I really deeply, is that getting AI right is going to require participation everyone. And that’s for deciding how we want it to slot in, that’s for setting the of the road, for what an AI will and won’t do. And there’s one thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I … I suspect that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing the 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, mean, it’s amazing, but it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those really industry-wide. But I think within OpenAI, we made 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 going to take to make progress here? We tried a lot of things that didn’t work, so only see the things that did. And I think that the most important thing has been get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact that you saw something in these language that meant that if you continue to invest in them and them, that something at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, 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. model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that could come from a prediction machine. Just the stuff showed us just now. And the key idea of is that when you get more of a thing, suddenly different things emerge. It happens all time, ant colonies, single ants run around, when you bring enough them together, you get these ant colonies that show completely emergent, behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll get it wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like you can’t memorize the 40-digit addition table, that’s atoms than there are in the universe. So it to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up look at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re to really get good at is predicting some of these emergent capabilities. And to that actually, one of the things I think is very in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every single of the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell 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. we were able to predict, for example, the performance on coding problems. We basically look some models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you maybe predict in some level of confidence, but it’s capable surprising you. Why isn’t there just a huge risk of something terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people 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 that we think it’s so important to deploy incrementally. And so think that what we kind of see right now, if you look at this talk, a lot what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that math problem and 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 know this book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry out our intent. And I we’re going to have to produce even better, more efficient, more reliable ways of this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are critics who that, you know, there’s no real understanding inside, the is going to always — we’re never going to that it’s not generating errors, that 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 scale and the human feedback that you talked about is basically going to take it on journey of actually getting to things like truth and wisdom and forth, with a high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s this field is the field of broken promises, of these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what you need. But 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 a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it out there in and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, the original that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil thing with AI. And were 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 I heard. yet, what’s happened, arguably, is the opposite. That your release GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us to put this here without proper guardrails or we die. You know, how do you, like, make the case what you have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all time. And I don’t think we’re always going to it right. But one thing I think has been important, from the very beginning, when we were thinking about to build artificial general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out safety of it and then you push “go,” and you 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 that you do let reality hit you in the face. And I think do give people time to give input. You do have, before machines are perfect, before they are super powerful, that you actually have the ability to see 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, the one thing was generating Viagra spam.
(Laughter)
CA: So Viagra is bad, but there are things that are much worse. Here’s a thought experiment you. Suppose you’re sitting in a room, there’s a box the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going give beautiful gifts to your family and to everyone. But there’s also a one percent thing in the small print there that says: “Pandora.” And there’s a chance that actually could unleash unimaginable evils on the world. Do open that box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a 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 over this wonderful water, all these people having a good time. you think about it for a moment, if you choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets be 500 years away and people get more time to it right, which do you pick? And you know, just really felt it in the moment. I was like, course you do the 500 years. My brother was the military at the time and like, he puts life on the line in a much more real way than any of us things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. I don’t think that’s quite playing the field as it truly lies. Like, if you look at whole history of computing, I really mean it when I say this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re improving the algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, or moment that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is we have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to teach it to be wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out to provide the feedback, decide what we want from it. my hope is that that will continue to be best path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to TED and blowing minds.
(Applause)