We started OpenAI seven ago because we felt like something really interesting was happening in and we wanted to help steer it in a direction. It’s honestly just really amazing to see how this whole field has come since then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who 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 a world are going to define a technology that will be so important for our society forward. And I believe that we can manage this for good.
So today, want to show you the current state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show you is what it’s like to build a for an AI rather than building it for a human. So we have new DALL-E model, which generates images, and we are exposing it as an app for ChatGPT to use 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 the details for you that 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 we’re going to get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a live demo. This is all generated by the as we speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So you get this little up here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, upcoming months. And you can look under the hood and see what it actually did was 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 to use that information and to integrate with other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, a new way of thinking about the user interface. Like, are so used to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great experience within an app long as you kind of know the menus and know all the options. Yes, I like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at Instacart shopping list while we’re at it. And you can we sent a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is very valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. And that’s something I think shows that they’re not going 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 “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut back the slides. Now, the important thing about how we build this, it’s not about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the test, he says, you’ll never program an answer to this. Instead, can learn it. You could build a machine, like a child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things 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 process imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we have to do a second step, too, which is to teach the AI what to with those skills. And for this, we provide feedback. We the AI try out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process the AI used to produce that answer. And this it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, 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 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 very and offered 20 hours of his own time to provide feedback to the machine alongside team. And over the course of a couple of months we were to teach the AI that, “Hey, you really should push back humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team to say, “Here’s an area weakness where you 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 clean their room, if you’re doing is inspecting the floor, you don’t know if you’re just teaching to stuff all the toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even better feedback and to scale ability to supervise the machine as time goes on. And me show you what I mean.
For example, you can ask GPT-4 question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. we can actually use the AI to fact-check. And it can actually check its work. You can say, fact-check this for me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m just to search for this and it actually does the search. It then finds the publication date and the search results. It is issuing another search query. It’s going to click into the blog post. And 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 in the driver’s seat, to be in this manager’s position where you can, if you want, triple-check work. And out come citations so you 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 back to the side. And so thing that’s so to me about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking is doing it in order to produce data for another AI to become more useful to a human. I think this really shows the shape of something that we expect to be much more common in the future, where we have and machines kind of very carefully and delicately designed in how fit into a problem and how we want to that problem. We make sure that the humans are providing the management, oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if get this process right, we will be able to solve problems.
And to give you a sense of just 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 around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a specific spreadsheet all the AI papers on the arXiv for the past 30 years. There’s about 167,000 them. And you 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 another tool, one 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 the name of the file it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name of file, the column names like you saw and then actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit and therefore that’s what these things are and that these integer 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 what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” once again, this is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI of has to infer what I might be interested in. And so comes up with some good ideas, I think. So 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 to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going to then make this nice of the papers per year. Something crazy is happening 2023, though. Looks like we were on an exponential and it dropped off the cliff. What could be on there? By the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going to back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] So April 13 was cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, feel like 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 of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask for that, but 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 end up using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call 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, full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used 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 professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how 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 different anything people had anticipated. And so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every mind here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and you think, “Oh my goodness, much every single thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess first question actually is just how the hell have you this?
(Laughter)
OpenAI has a few hundred 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: mean, the truth is, we’re all building on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot of deliberate choices from the early days. And the first one was just confront reality as it lays. And that we just thought really hard like: What is it going to take to make here? We tried a lot of things that didn’t work, you 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 each other work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s a dry-mouth topic. isn’t there something also just about the fact that saw something in these language models that meant that you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like we knew that was what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training a model predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics emerged from 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 riddle that baffles everyone looking at this, because these things are described prediction machines. And yet, what we’re seeing out of feels … it just feels impossible that that could from a prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single ants run around, when you bring of them together, you get these ant colonies that show completely emergent, different behavior. Or a where a few houses together, it’s just houses together. But as you grow the number houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an circuit for how to do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can 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 more atoms there are in the universe. So it had to learned something general, but that it hasn’t really fully learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve allowed to scale up and look at an incredible number of pieces of text. it 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 science that we’re starting to really get good at is predicting some of emergent capabilities. And to do that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. You have to get every piece of the stack engineered properly, and then you start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s fundamental to what’s here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. isn’t there just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And think one thing people miss, too, is sort of the 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 I think what we kind of see right now, if you look this talk, a lot of what I focus on is providing really high-quality feedback. Today, the that we do, you can inspect them, right? It’s easy to look at that math problem and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the thing will be that we take this step by step. And that we say, OK, as move on to book summaries, we have to supervise this task properly. have to build up a track record with these machines they’re able to actually carry out our intent. And think we’re going to have to produce even better, more efficient, more ways of scaling this, sort of like making the be aligned with you.
CA: So we’re going to hear later this session, there are critics who say that, you know, there’s no real understanding inside, the 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. it your belief, Greg, that it is true at any one moment, that 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 like and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, believe that is 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 like this field the field of broken promises, of all these experts saying X is going to happen, Y 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 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 to the of this technology to really see it in action, that tells you then, oh, here’s how we can move on a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out in public and then harness all this, you know, of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going 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 sort check on the big companies doing their unknown, possibly evil thing with AI. And you were going to models that sort of, you know, somehow held them 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 so forth are all scrambling to catch up. And of their criticisms have been, you are forcing us to put out here without proper guardrails or 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 all the time. Like, seriously all time. And I don’t think we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, get this super powerful thing, and then you figure 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. And so I that this alternative approach is the only other path I see, which is that you do let reality hit in the face. And I think you do give people time to input. You do have, before these machines are perfect, before they are powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to with it was generate misinformation, try to tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re sitting a room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s something absolutely that’s going to give beautiful gifts to your family and to everyone. But there’s actually a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open 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 actually told before, which is shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in the hotel room just looking out over this water, all these people having a good time. And you think about it for 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 away and people get more time get it right, which do you pick? And you know, I just really felt in the moment. I was like, of course you do the 500 years. My brother was the military at the time and like, he puts his life on the line a much more real way than any of us things in computers and developing this technology at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s quite 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 improving the algorithms, of these things, they are happening. And if you don’t put them together, 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 kind safety precautions you get. And so I think that one thing I away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what humans could do. But I actually that if you look 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 do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this child that may have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to provide the feedback, what we want from it. And my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much coming to TED and blowing our minds.
(Applause)