We started OpenAI years ago because we felt like something really interesting was happening in and we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are the technology we are building, and others, for so wonderful things. We hear from people who are excited, hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a are going to define a technology that will be so important for our society going forward. And believe that we can manage this for good.
So today, I want to you the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. So we a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And you can do things 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 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 get. But ChatGPT doesn’t just generate images in this case — sorry, doesn’t generate text, it also generates an image. And 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 is all generated the AI as we speak. So I actually don’t even know what we’re going to see. This wonderful.
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I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. And can look under the hood and see that what it did was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using 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. can say, “Now make a shopping list for the thing I was suggesting earlier.” And make it a little tricky for the AI. “And tweet out for 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 all these different tools me having to tell it explicitly which ones to use in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a experience within an app as long as you kind of know the and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface on top 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 happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take look at the 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 thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click it and sort of modify the 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 way to build them. And now have a tweet that’s been drafted for our review, which is also a very important thing. We click “run,” and there we are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. And so after talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
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So we’ll cut back to slides. Now, the important thing about how we build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want it to do when ask these very high-level questions? And to do this, we an old idea. If you go back 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 rewards and punishments as tries things out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised 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 imbues it with all sorts of wonderful skills. For example, if you’re shown a math problem, only way to actually complete that math problem, to say what comes next, that green nine there, is to actually solve the 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 out multiple things, give us multiple suggestions, and then a rates them, says “This one’s better than that one.” And this reinforces not just the specific thing that AI said, but very importantly, the whole process that the AI used produce that answer. And this allows it to generalize. It allows it teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, the things we have to teach the AI are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this so great, We’re going to be able to teach students wonderful things. 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 we had to collect some data. Sal Khan himself was very kind and offered 20 hours of own time to provide feedback to the machine alongside our team. over the course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans 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 ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area of weakness where you should feedback.” And so when you do that, that’s one that we really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think asking a kid to 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, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our ability to supervise the machine time goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational on unsupervised learning 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 feedback. But we can actually use the AI to fact-check. And it can actually its own 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 the can issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. It says, I’m just to search for this and it actually does the search. It then it finds the publication date and search results. It then is issuing another search query. It’s going to click into the post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s more fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole of reasoning. And it actually turns out two months wrong. Two months and one week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this whole process is that it’s many-step collaboration between a human and an AI. Because a human, this fact-checking tool is doing it in order to produce data for another AI to become useful to a human. And I think this really shows shape of something that we should expect to be more common in the future, where we have humans and machines kind of very carefully and designed in how they fit into a problem and how we want to that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that time, if we get this process right, we will able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink 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 of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can there the data right here. But let me show you the take on how to analyze a data set like this.
So we can ChatGPT access to yet another tool, this one a interpreter, so it’s able to run code, just like data scientist would. And so you can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name of the and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it for you.” The only here is the name of the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv is site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number of in the paper,” like all of that, that’s work for a human to do, 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, this a super high-level instruction with lots of intent behind it. But I don’t even know what I want. the AI kind of has to infer what I might be interested in. And so it comes up some good ideas, I think. So a histogram of number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, think, will be pretty interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we were an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, can inspect. And then we’ll see word cloud. So you can all these wonderful things that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. course, the problem 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?] So April 13 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. really wanted it to notice this thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, it even the title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This slide shows parable of how I think we … A vision of how we may end using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I am a vet, you need to talk to a professional, here are some hypotheses.” brought that information to a second vet who used it to the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with medical professional and with ChatGPT as a brainstorming partner was able to achieve an outcome would not have happened otherwise. I think this is something we should reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for how we want it to slot in, that’s for setting the rules of the road, for what an will and won’t do. And if there’s one thing to take away from this talk, it’s that technology just looks different. Just different from anything people anticipated. And so we all have to become literate. And that’s, honestly, one of 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. I … I suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that we things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it you who’s come up with this technology shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders giants, right, there’s no question. If you look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. that we just thought really hard about like: What is it to take to make progress here? We tried a of things that didn’t work, so you only see the things that did. I think that the most important thing has been to teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something in these language models that that if you continue to invest in them and them, that something 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 was we wanted to be, was a deep learning lab, exactly how to do it? I think that in early days, we didn’t know. We tried a lot of things, one person was working on training a model to predict the character in Amazon reviews, and he got a result where — this a syntactic 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 out of it. This model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps explain the riddle that baffles everyone looking this, because these things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from a prediction machine. Just the stuff showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, you bring enough of them together, you get these colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, emerge, like suburbs and cultural centers and traffic jams. Give one moment for you when you saw just something pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really the 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 had to have learned general, but that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and at an incredible number of pieces of text. And it is learning things you didn’t know that it was going to be capable learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. And 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 stack. When you think about building a rocket, every tolerance to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, and then you can start doing 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, can see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, example, the performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and scale and timing. And I think one thing miss, too, is sort of the integration with the world also this incredibly emergent, sort of, very powerful thing too. And so that’s one of the reasons that we it’s so important to deploy incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct 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. one wants to do that.
(Laughter) And so I think that the important thing will that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. have to build up a track record with these machines that they’re able to actually carry out intent. And I think we’re going to have to produce better, more efficient, more reliable ways of scaling this, sort of like making the machine be with you.
CA: So we’re going to hear later in this session, there are critics who say that, know, there’s no real understanding inside, the system 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, but that the expansion of the and the human feedback that you talked about is basically going take it on that journey of actually getting to things like truth and and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that where we’re headed. And I think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural 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 you need. But I think that our approach has always been, you’ve got to push the limits of this technology to really see it in action, because tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted the fruit here.
CA: I mean, it’s a controversial stance you’ve taken, that the right way do this is to put it out there in public and harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing with AI. And you going to build models that sort of, you know, somehow held them and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. yet, what’s happened, arguably, is the opposite. That your release of GPT, especially ChatGPT, sent shockwaves through the tech world that now Google and Meta and forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have it benefit all of humanity, like, how are you 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 the safety of it and then push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, was always terrifying, it didn’t feel right. And so I think that this alternative approach the only other path that I see, which is that you do let hit you in the face. And I think you do give people to give input. You do have, before these machines are perfect, before they super powerful, that you actually have the ability to see them in action. And we’ve seen it GPT-3, right? GPT-3, we really were afraid that the one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating Viagra spam.
(Laughter)
CA: Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts your family and to everyone. But there’s actually also one percent thing in the small print there that says: “Pandora.” And there’s a that this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, I I was in Puerto Rico for an AI conference. I’m sitting in the room just looking out over this wonderful water, all these 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 would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it be five years away. if it gets to be 500 years away and people more time to get it right, which do you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he puts his on the line in a much more real way than of us typing things in computers and developing this at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s quite playing the as it truly lies. Like, if you look at the whole history of computing, I really it when I say that 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 together, you get an overhang, which means that if does, or the moment that someone does manage to to the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being a zero to one, sort of, change in what could do. But I actually think that if you look 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 incrementally and you’ve got to figure out how to 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 extraordinary child that may superpowers that take humanity to a whole new place. It is collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to 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 we encounter it. And I think it’s incredibly important that we all do get literate in this technology, out how to provide the feedback, decide what we want it. And my hope is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if it weren’t out there.
CA: Brockman, thank you so much for coming to TED and blowing minds.
(Applause)