We OpenAI seven years ago because we felt like something really was happening in AI and we wanted to help it in 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 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 we feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define a technology that will be so important for our going forward. And I believe that we can manage for good.
So today, I want to show you the current state of technology and some of the underlying design principles that hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which images, and we are exposing it as an app for 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 and care of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, but very, very detailed spread. So let’s see what we’re to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also an image. And that is something that really expands the power of what it do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This all generated by the AI as we speak. So actually don’t even know what we’re going to see. looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You say “save this for later.” And the interesting thing these tools is they’re very inspectable. So you get this pop up 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 actually did was write a prompt like a human could. And so you sort of have this ability to inspect 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 to use 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 tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is all these different tools without me having to tell it which ones to use in any situation. And this, I think, shows a new way of thinking the user interface. Like, we are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, and usually it’s a great experience an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And having this unified language interface on top of tools, the AI is able 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 of what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen us. But let’s take a look at the Instacart list while we’re at it. And you can see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If look at this, you still can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented to build them. And now we have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so this talk, you 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 we build this, it’s just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to when we ask these very high-level questions? And to this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. You could a machine, like a human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, produce what Turing would have called a child machine through unsupervised learning process. We just show it the whole world, whole 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, you’re shown a math problem, the only way to complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to a second step, too, which is to teach the what to do with those skills. And for this, provide feedback. We have the AI try out multiple things, give us multiple suggestions, and then a human them, says “This one’s better than that one.” And reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. this allows it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. one problem, it doesn’t double-check students’ math. If there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time provide feedback to the machine alongside our team. And over the course of couple of months we were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” And we’ve made lots and lots of improvements to the models this way. And when push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our to say, “Here’s an area of weakness where you gather feedback.” And so when you do that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a hard thing. 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 them to stuff all the 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 to scale our ability to provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you ask GPT-4 a question like this, of how much time passed between these two blogs on unsupervised learning and learning from human feedback. And the model says two months passed. is it true? Like, these models are not 100-percent reliable, they’re getting better every time we provide some feedback. But we can actually use the to fact-check. And it can actually check its own work. You can say, fact-check for me.
Now, in this case, I’ve actually given the AI a new tool. This one a browsing tool where the model can issue search queries and click into web pages. And actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and actually does the search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into blog post. And all of this you could do, it’s a very tedious task. It’s not a thing that really want to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually go very easily verify any piece of this whole chain reasoning. And it actually turns out two months was wrong. months and one week, that was correct.
(Applause)
And we’ll cut back to 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 is doing it in order to produce data for another AI become more useful to a human. And I think this really the 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 designed in how fit into a problem and how we want 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. I think that over time, if we get this process right, we be able to solve impossible problems.
And to give you a sense of just 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 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 all the AI papers on the arXiv for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But me 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 a data scientist would. And so you can just literally upload a file and ask questions about it. 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 the file, column names like you saw and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a that people submit papers and therefore that’s what these things are and that these integer values and so therefore it’s a number of authors in the paper,” like of that, that’s work for a human to do, and AI is happy 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?” And once again, this is a high-level instruction with lots of intent behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so it comes up with good ideas, I think. So a histogram of the number authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be 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 common. It’s going to then make this nice plot of the papers per year. 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. So you 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 on the machine. [Waitttt that’s fair!!! 2023 isn’t over. What percentage of papers in 2022 even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I like there was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I 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. now, it does the correct projection.
(Applause)
If you noticed, even updates the title. I didn’t ask for that, it know what I want.
Now we’ll cut back to the again. This slide shows a parable of how I think we … A vision how we may end up 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 dog would not be here today he listened. In the meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, are some hypotheses.” He brought that information to a second who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot overly on them. But this story, I think, shows that a human a medical professional and with ChatGPT as a brainstorming was able to achieve an outcome that would not have otherwise. I think this is something we should 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 going to require participation from everyone. And that’s deciding how we want it to slot in, that’s setting the rules of the road, for what an AI will 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, of the reasons we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I that within every mind out here there’s a feeling of reeling. Like, suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, much every single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, I guess my first question is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If you look at the compute progress, the progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we thought really hard about like: What is it going take to make progress here? We 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 get of people who are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about the fact that you saw in these language models that meant that if you continue to invest in them grow them, that something at some point might emerge?
GB: Yes. And I that, I mean, honestly, I think the story there is pretty illustrative, right? think that high level, deep learning, like we always that was what we wanted to be, was a deep learning lab, and how to do it? I think that in the early days, we didn’t know. We tried lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got a where — this is a syntactic process, you expect, you know, the 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 negative. I mean, today we are just like, come on, anyone can do that. But this was the first that you saw this emergence, this 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 baffles everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing of them feels … it just feels impossible that that could come from a prediction machine. the stuff you showed us just now. And the idea of emergence is that when you get more of a thing, suddenly things emerge. It happens all the time, ant colonies, single ants run around, 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 number of houses, things emerge, like suburbs and cultural centers and traffic jams. Give me one for you 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, you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned internal circuit for how to do it. And the really interesting thing is actually, you have it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms 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 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 an incredible number of pieces of text. And it is 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 really get good at is some of these emergent capabilities. And to do that actually, one of the things I is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every piece of the stack engineered properly, and then you can doing these predictions. There are all these incredibly smooth curves. They tell you something deeply fundamental about intelligence. If look at our GPT-4 blog post, you can see of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is smooth scaling, even though it’s still early days.
CA: So here is, one of the big then, that arises from this. If it’s fundamental to what’s happening here, that as 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 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 people miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of see now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks that we do, you inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary any good? You have to read the whole book. one wants to 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 we move on book summaries, we have to supervise this task properly. We have to build 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 like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never going to know 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 the scale and the human feedback that you talked is basically going to take it on that journey of actually getting to things like truth wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I 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 this field is the field of broken promises, of these experts saying X is going to happen, Y is how works. People have been saying neural nets aren’t going 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 to the limits of this technology to really see it action, because that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out there in public then harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad things are going to emerge, is out there. So, you know, the original story I heard on OpenAI when you were founded as a nonprofit, you were there as the great sort of check the big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held accountable and was capable of slowing the field down, need 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 such shockwaves the tech world that now Google and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing us put this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is 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 always going to it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the of it and then you push “go,” and you you got it right. I don’t know how to 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 is 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, these machines are perfect, before they are super powerful, that you actually have the ability to see in action. And we’ve seen it from GPT-3, right? GPT-3, we really afraid that the number one thing people were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are that 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 it’s something absolutely glorious that’s going to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the small print there says: “Pandora.” And there’s a chance that this actually could unleash evils on the world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. 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 was in Puerto Rico for AI conference. I’m sitting in the hotel room just out over this wonderful water, all these people having a time. And you think about it for a moment, if you could choose for basically Pandora’s box to be five years away or 500 away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five away. But if it gets to be 500 years away 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 you do the 500 years. brother was in the military at the time and like, he his life on the line in a much more real than any of us typing things in computers and this technology at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it I say that this is an industry-wide or even just like a human-development- of-technology-wide shift. And the more that sort of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, means that if someone does, or the moment that someone does to connect to the circuit, then you suddenly have very powerful thing, no one’s had any time to adjust, knows what kind of safety precautions you get. And I think that one thing I take away is like, even you think about development other sort of technologies, think about nuclear weapons, people about being like a zero to one, sort of, in what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so history, I think, of every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is that you … the model you want us to is that 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 for this child to collectively teach it to be wise and not tear us all down. Is that basically the model?
GB: I think it’s true. I think it’s also important to say this may shift, right? We’ve got to each step as we encounter it. And I think it’s 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 that continue to be the best path, but it’s so good we’re having this debate because we wouldn’t otherwise if it weren’t there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)