We started seven years ago because we felt like something really was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as world are going to define a technology that will be important for our society going forward. And I believe that we can manage this good.
So today, I want to show you the current state of that technology and some of underlying design principles that we hold dear.
So the thing I’m going to show you is what it’s to build a tool for an AI rather than building it for a human. we have a new DALL-E model, which generates images, and we are it as an app for ChatGPT to use on your behalf. And you do things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and care of the details for you that you get out of ChatGPT. And here 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 image. And that is something that really expands the power what it can do on your behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
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I’m getting just looking at it.
Now we’ve extended ChatGPT with other too, for example, memory. You can say “save this for later.” And the interesting about these tools is they’re very inspectable. So you get this pop up here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT users, over upcoming months. And you can under the hood and see that what it actually did write a prompt just like a human could. And so you sort have this ability to inspect how the machine 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 like to use information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing was suggesting earlier.” And 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 how it tastes.
But you can that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of about the user interface. Like, we are so used thinking of, well, we have these apps, we click them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know all the options. Yes, would like you to. Yes, please. Always good to polite.
(Laughter)
And by having this unified language interface on top tools, the AI is able to sort of take away all those details from you. you don’t have to be the one who spells out single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes 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 to Instacart. Here’s you 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 I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And now we have 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 change the work of the AI if we want to. And so this talk, you will be able to access this yourself. there we go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how we this, it’s not just about building these tools. It’s about the AI how to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an old idea. If you back to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict 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 math problem, the only way to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have 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 just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, to sort of your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes 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 is so great, We’re to be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math 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 couple months we were able to teach the AI that, “Hey, you really should push back on in this specific kind of scenario.” And we’ve actually made 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 of weakness where you should gather feedback.” And so when you do that, that’s way that we really listen to our users and make we’re building something that’s more useful for everyone.
Now, 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 know you’re just teaching them to stuff all the toys in 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 our to provide high-quality feedback. But for this, the AI itself happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these foundational blogs on unsupervised learning and learning from human feedback. And model says two months passed. But is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the 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 going to search for this and it does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into the 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 in manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very easily verify any of this whole chain of reasoning. And it actually 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 me this whole process is that it’s this many-step collaboration between a and an AI. Because a human, using this fact-checking tool is doing it in to produce data for another AI to become more useful to a human. And I think really shows the shape of something that we should expect to be much more common the future, where we have humans and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. And I think that over time, if we this process right, we will be able to solve problems.
And to give you a sense of just how impossible I’m talking, I we’re going to be able to rethink almost every aspect of how we interact with computers. example, think about spreadsheets. They’ve been around in some form since, we’ll say, 40 ago with VisiCalc. I don’t think they’ve really changed much in that time. And here is a specific spreadsheet of 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 me show you the ChatGPT take how to 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 code, just like 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 and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of the file, the column like you saw and then the actual data. And from it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. It has sort of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s these things are and that these are integer values and so it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI happy to help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make exploratory graphs?” And 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 kind of has to infer what I might be in. And so it comes up with some good ideas, think. So a histogram of the number of authors paper, time series of papers per year, word cloud the paper titles. All of that, I think, will pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make this plot of the papers per year. Something crazy is in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. I’m going to push back on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted by April 13?] So 13 was the cut-off date I believe. Can you that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it have sort of, inferred magically that this is what I wanted. But inject my intent, I provide this additional piece of, you know, guidance. And under hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what I want.
Now we’ll cut back the slide again. This slide shows a parable of I think we … A vision of how we end up using this technology in the future. A brought his very sick dog to the vet, and the made a bad call to say, “Let’s just wait 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, you need talk to a professional, here are some hypotheses.” He brought that to a second vet who used it to save 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 medical professional and with as a brainstorming partner was able to achieve an outcome that would not have happened otherwise. I this is something we should all reflect on, think about as we consider how to integrate systems into our world.
And one thing I believe deeply, is that getting AI right is going to require participation from everyone. that’s for deciding how we want it to slot in, that’s for the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. And so all have to become literate. And that’s, honestly, one of the reasons we released ChatGPT.
Together, believe that we can achieve the OpenAI mission of ensuring that general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, I suspect that a very large number people viewing this, you look at that and you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to the way that we do things? Yeah, I mean, it’s amazing, it’s also really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question actually just how the hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this that shocked the world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the data progress, all of are really industry-wide. But I think within OpenAI, we made a lot of very choices from the early days. And the first one was to confront reality as it lays. And that we just thought really hard like: What is it going to take to make progress here? We tried a of things that didn’t work, so you only see things that did. And I think that the most thing has been to get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, by the way, just here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the that you saw something in these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the there is pretty illustrative, right? I think that high level, deep learning, like we always that was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict the character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. he actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if 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 that emerged from this underlying process. And there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think this helps explain the riddle baffles everyone looking at this, because these things are described as machines. And yet, what we’re seeing out of them feels … it just feels that that could come from a prediction machine. Just the stuff you us just now. And the key idea of emergence is that when you get of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you enough of 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, suburbs and cultural centers and traffic jams. Give me one moment for when you saw just something pop that just blew your that you just did not see coming.
GB: Yeah, well, you can try this in ChatGPT, if you add 40-digit —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s really learned an internal for how to do it. And the really interesting thing is actually, if have 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 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 than are in the universe. So it had to have something general, but that it hasn’t really fully yet learned that, Oh, can sort 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 look at an number of pieces of text. And it is learning that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting to really get good is predicting some of these emergent capabilities. And to that actually, one of the things I think is undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about building a rocket, every tolerance has be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are all these incredibly smooth scaling curves. tell you something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting be able to predict. So we were able to predict, example, the performance on coding problems. We basically look at models that are 10,000 times or 1,000 times smaller. And so there’s something this 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. it’s fundamental to what’s happening here, that as you scale up, things emerge that can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there just a risk of something truly terrible emerging?
GB: Well, I think all of these are of degree and scale and timing. And I think one 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 reasons that think it’s so important to deploy incrementally. And so I think what we kind of see right now, if you at 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 very easy to look at that math problem and like, no, no, no, machine, seven was the correct answer. But 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 whole book. one wants to do that.
(Laughter) And so I that the important thing will be 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. We 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 even better, more efficient, more reliable ways of scaling this, sort of like making the machine be aligned you.
CA: So we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system is going always — we’re never going to know that it’s not generating errors, that doesn’t have common sense and so forth. Is it belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked about is basically going take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can be sure of that?
GB: Yeah, well, I think that the OpenAI, mean, the short answer is yes, I believe that is where we’re headed. And I that the OpenAI approach here has always been just like, let reality hit you the face, right? It’s like this field is the field broken promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right maybe 70 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 limits of this technology really see it in action, because that tells you then, oh, here’s how we can move on to a new paradigm. we just haven’t exhausted the fruit here.
CA: I mean, it’s quite a stance you’ve taken, that the right way to do is to put it out there in public and then harness this, you know, instead of just your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded as a nonprofit, well you were as the great sort of check on the big companies their unknown, possibly evil thing with AI. And you were 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 of what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now Google and Meta so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, do you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about these questions the time. Like, seriously all the time. And I don’t think we’re going to get it right. But one thing I has been incredibly important, from the very beginning, when were thinking about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed to do that, right? And that plan of being, well, you build in secret, you get this super powerful thing, and 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 alternative approach is the only other path that I see, which is that you let reality hit you in the face. And I think you give people time to give input. You do have, before these are perfect, before they are super powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the number one was generating Viagra spam.
(Laughter)
CA: So Viagra spam bad, but there are things that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe that in that box is something that, there’s very strong chance it’s something absolutely glorious that’s going to give beautiful to 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 chance that this actually could unimaginable evils on the 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 story that I 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 hotel room just looking out over this wonderful water, these people having a good time. And you think it for a moment, if you could choose for basically that Pandora’s box to be years away or 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 to be 500 away and people get more time to get it right, which do you pick? And you know, I just felt it in the moment. I was like, of course do the 500 years. My brother was in the military at the time like, he puts his life on the line in a much more real way any of us typing things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach right. But I don’t think that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean it when I say 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 put together pieces that are 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 together, you get an overhang, which means that if someone does, or the moment someone does manage to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one I take away is like, even you think about of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve to 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 is we have birthed this extraordinary child that may have superpowers take humanity to a whole new place. It is our collective responsibility to provide the guardrails this child to collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And I think it’s also to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s incredibly important today that we all do get in this technology, figure out how to provide the feedback, what we want from it. And my hope is that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to TED blowing our minds.
(Applause)