We OpenAI seven years ago because we felt like something interesting was happening in AI 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 people like Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people who concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering an historic right now where we as a world are going to define a technology will be so important for our society going forward. I believe that we can manage this for good.
So today, want to show you the current state of that and some of the underlying design principles that we hold dear.
So first thing I’m going to show you is what it’s like build a tool for an AI 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 nice post-TED meal and draw a picture it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for the meal, a very, very detailed spread. So let’s see what we’re going get. But ChatGPT doesn’t just generate images in this — sorry, it doesn’t generate text, it also generates an image. And that is something really expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all by the AI 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, for example, memory. can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this little pop up here that says “use DALL-E app.” And by the way, this is coming to you, all ChatGPT users, over months. And you can look under the hood and that what it actually did was write a prompt just a human could. And so you sort of have this ability to inspect how the is using these tools, which allows us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to that information and to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” make it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me to tell it explicitly which ones to use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we click 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 all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface top of tools, the AI is able to sort of take away those details from you. So you don’t have to be the one who out every single sort of little piece of what’s supposed to happen.
And as said, this is a live demo, so sometimes the will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that the UI is still very valuable, right? If you look at this, you still can click through it sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have a that’s been drafted for 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 AI if we want to. And so after this talk, will be 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 about how build this, it’s not just about building these tools. It’s about 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. you go back to Alan 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, like a child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, the internet and say, “Predict what comes next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to say what comes next, that green up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach AI what to do with those skills. And for this, we feedback. We have the AI try out multiple things, us multiple suggestions, and then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the we have to teach the AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going to be able teach students wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s bad math in there, it will happily pretend that plus one equals three and run with it.” So had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to provide feedback to the machine 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 of to the models this way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our team say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one way that we 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 clean their room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all toys in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies AI. As we move to harder tasks, we will have to our ability to provide high-quality feedback. But for this, the AI itself is happy to help. It’s happy help us provide even better feedback and to scale our ability supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a question like this, of much time passed between these two foundational blogs on learning and learning from human feedback. And the model two months passed. But is it true? Like, these models are 100-percent reliable, although 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 me.
Now, in this case, I’ve actually given the a new tool. This one is a browsing tool where the model issue search queries and click into web pages. And it actually writes its whole chain of thought as it does it. It says, I’m just going to search for and it actually does the search. It then it finds the publication and the search results. It then is issuing another search query. It’s going to into the blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, you want, triple-check the work. And out come citations so can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And so thing that’s so interesting to about this whole process is that it’s this many-step collaboration between a human and an AI. a human, using this fact-checking tool is doing it order to produce data for another AI to become more to a human. And I think this really shows shape of something that we should expect to be much more common in the future, where we humans and machines kind of very carefully and delicately in how they fit into a problem and how we to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the are operating in a way that’s inspectable and trustworthy. together we’re able to actually create even more trustworthy machines. And I think that over time, if get this process right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every 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 think they’ve changed that much in that time. And here is a spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the data here. But let me show you the ChatGPT take on how analyze a data set like this.
So we can give ChatGPT to yet another tool, this one a Python interpreter, so it’s able to run code, just a data scientist would. And 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 parse it for you.” The only information here the name of the file, the column names like you saw and then the data. And from that it’s able to infer what these columns actually mean. Like, that semantic wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what these things and that these are integer values and so therefore it’s a number of authors in paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now 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 behind it. But I don’t even know what I want. And the AI kind of to infer what I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty interesting to see. And the great is, it can actually do it. Here we go, a nice bell curve. see that three is kind of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were on an exponential and dropped 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. you can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year 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 papers 2022 were 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 the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. really wanted it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that is what I wanted. But I inject my intent, provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even 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 the future. A person brought his very sick dog the vet, and the veterinarian made a bad call say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, he the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely on them. this story, I think, shows that a human with a 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 integrate these systems into our world.
And one thing believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for what an AI and won’t do. And if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything 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 achieve the OpenAI mission of ensuring that artificial general intelligence all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to rethink way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I my first question actually is just how the hell you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come with this technology that shocked the world?
Greg Brockman: I mean, the 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 of those really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was just confront reality as it lays. And that we just thought hard about like: What is it going to take to make progress here? tried a lot of things that didn’t work, so you only the things that did. And I think that the most important thing has been to teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there also just about the fact that you saw something in these language models that meant that if 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 is illustrative, right? I think that high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one person was working training a model to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, know, the model will predict where the commas go, where nouns and verbs are. But he actually got a state-of-the-art sentiment analysis out of it. This model could tell you if a review positive or negative. I mean, today we are just like, come on, anyone can do that. But was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to see where goes.
CA: So I think this helps explain the riddle that 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 a machine. Just the stuff you showed us just now. And the key idea of emergence is that you get more of a thing, suddenly different things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few 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 you you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this in ChatGPT, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit how to do it. And the really interesting thing actually, if you have it add like a 40-digit number a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, it hasn’t fully 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 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 and look at an incredible number of of text. And it is learning things that you didn’t know that it was going be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to get good at is predicting some of these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 blog post, you can see all 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. We basically look at models that are 10,000 times or 1,000 times smaller. so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, 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 can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I one thing people miss, too, is sort of the with the world is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I think 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 easy to look at that math problem and be like, no, no, no, machine, seven was the answer. But even summarizing a book, like, that’s a hard to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one wants do that.
(Laughter) And so I think that the important thing will be that take this step by step. And that we say, OK, as we move on to book summaries, have to supervise this task properly. We have to up 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 of scaling this, sort of like making the machine 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 know that it’s generating errors, that it doesn’t have common sense and so forth. Is it your belief, Greg, that is true at any one moment, but that the expansion of the scale the human feedback that you talked about is basically to take it on that journey of actually getting to things truth 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, I believe that is where we’re headed. And think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts X is going to happen, Y is how it works. have been saying neural nets aren’t going to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like that is what you need. But I think our approach has always been, you’ve got to push to the limits 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 just haven’t the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that right way to do this is to put it there in public and then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, know, bad things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded as nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is opposite. That your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are all to catch up. And 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 case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always going to get it right. But thing I think has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default plan being, well, you build in secret, you get this powerful thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so I think that this alternative 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 to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a very strong chance it’s something glorious that’s going to 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 chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, absolutely not. think you don’t do it 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 I in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it a moment, if you could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the one you’re like, well, maybe for you personally, it’s better to have it be five years away. But if gets to be 500 years away and people get more time to get it right, which do pick? And you know, I just really felt it in the moment. I like, of course you do the 500 years. My brother was in the military at time and like, he puts his life on the in a much more real way than any of typing things in computers and developing this technology at the time. And so, yeah, I’m sold on the you’ve got to approach this right. But don’t think that’s quite playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I say this is an industry-wide or even just almost like human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And if you don’t put them together, you get overhang, which means that if someone does, or the that someone does manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows what kind of safety precautions 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, talk about being like a zero to one, sort of, change in what could do. But I actually think that if you look at capability, it’s been quite over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve got figure out how to manage it for each moment that you’re increasing it.
CA: So I’m hearing is that you … the model you want us to have that we have birthed this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to provide the guardrails for this 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 may shift, right? We’ve got to take each step as we encounter it. I think it’s incredibly important today that we all do get literate in this technology, out how to provide the feedback, decide what we want from it. And my hope is that will continue to be 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: Brockman, thank you so much for coming to TED and our minds.
(Applause)