We started OpenAI seven years ago because felt like something really interesting was happening in AI and we wanted to help steer it a positive direction. It’s honestly just really amazing to see how far whole field has come since then. And it’s really gratifying to hear from people like who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel both those emotions once. And honestly, that’s how we feel. Above all, feels like we’re entering an historic period right now where we as a world are to define a technology that will be so important for our society forward. And I believe that we can manage this for good.
So today, I want to show 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 like to build a tool for AI rather than building it for a human. So we have a DALL-E model, which generates images, and we are exposing it as an app for to use on your behalf. And you can do like ask, you know, suggest a nice post-TED meal and a picture of 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 for the meal, but a very, very detailed spread. So let’s see what we’re going to get. 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 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 generated by the AI we speak. So I actually don’t even know what we’re going to see. This wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And interesting thing about these tools is they’re very inspectable. So get this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all users, over upcoming months. And you can look under hood and see that what it actually did was write a prompt just like a human could. so you sort of have this ability to inspect how 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 that and to integrate with other applications too. You can say, “Now make a shopping list for the tasty thing was suggesting earlier.” And make it a little tricky for AI. “And tweet it out for 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 in any situation. this, I think, shows a new way of thinking the user interface. Like, we are so used to of, well, we have these apps, we click between them, copy/paste between them, and usually it’s a great experience within app as long as you kind of know the menus and all the options. Yes, I would like you to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to sort of take away all those from you. So you don’t have to be the one spells out every single sort of little piece of what’s supposed happen.
And as I said, this is a live demo, so sometimes the unexpected will to us. But let’s take a look at the Instacart shopping list while we’re at it. you can see we sent a list of ingredients to Instacart. Here’s everything need. And the thing that’s really interesting is that traditional UI is still very valuable, right? If you look this, you still can click through it and sort of modify the actual quantities. And that’s 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 a 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 change the work the AI if we want to. And so after talk, you will be able to access this yourself. And there go. Cool. Thank you, everyone.
(Applause)
So we’ll cut to the slides. Now, the important thing about how 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 we ask these high-level questions? And to do this, we use an old idea. If 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. could build a machine, like a human child, and teach 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 what Turing have called a child machine through an unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that problem, to say what comes next, that green nine up there, is to 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 the AI out multiple things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just the thing that the AI said, but very importantly, the whole process that the AI to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, when 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 some bad math in there, will happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you that thumbs down in ChatGPT, that actually is kind of like sending up a bat signal our team to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that we really listen our users and make sure we’re building something that’s useful for everyone.
Now, providing high-quality feedback is a hard thing. If you think about a kid to clean their 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 the closet. is a nice DALL-E-generated image, by the way. And same sort of reasoning applies to AI. As we move to harder tasks, we will have scale our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, you can GPT-4 a question like this, of how much time between these two foundational blogs on unsupervised learning and from human feedback. And the model says two months passed. But is true? Like, these models are not 100-percent reliable, although they’re getting better every time we some feedback. But we can actually use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, this case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search and click into web pages. And it actually writes out its whole of thought as it does it. It says, I’m going to search for this and it actually does the search. It then it finds publication date and the search results. It then is another search query. It’s going to click into the 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 the driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out come citations so you can actually and very easily verify any piece of this whole chain of reasoning. And it actually turns two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole process is that it’s this many-step collaboration a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another AI to become more useful a human. And I think this really shows the 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 delicately in how they fit into a problem and how we want solve that problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost aspect of how we interact with computers. For example, about spreadsheets. They’ve been around in some form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers 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 the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run code, just like a data scientist would. And so you just literally upload a file and ask questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this 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 and then the actual data. And from that it’s able to infer these columns actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that are integer values and so therefore it’s a number of authors in the paper,” all of that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what want to ask. So fortunately, you can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. But don’t even know what I want. And the AI of has to infer what I might be interested in. And so it comes up with some good ideas, think. So a histogram of the number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can actually do it. Here we go, nice bell curve. You see that three is kind of the common. It’s going to then make this nice plot of the papers per year. Something crazy happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in these titles.
But I’m unhappy about this 2023 thing. It makes this year look really bad. Of course, the is that the year 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 I believe. Can you use that make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really it to notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically that this is what I wanted. But I my intent, I provide this additional piece of, you know, guidance. under the hood, the AI is just writing code again, if you want 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 what I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian made a bad to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you need to talk 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. But story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something we all reflect on, think about as we consider how to integrate these systems into our world.
And one I 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 for setting the rules of the road, for an AI will and won’t do. And if there’s one to take away from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that every mind out here there’s a feeling of reeling. Like, suspect that a very 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? 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, I my first question actually is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working 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 shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made a of very deliberate choices from the early days. And the first one was to confront reality as it lays. And that we just thought really about like: What is it going to take to make 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 has been to get teams of people who are very different each other to work together harmoniously.
CA: Can we have water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t there something also just the fact that you saw something in these language that meant that if you continue to invest in them and grow them, that at some point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep lab, and exactly how to do it? I think that in the early days, we didn’t know. We a lot of things, and one person was working on training a to predict the next character in Amazon reviews, and he got result where — this is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But he got a state-of-the-art sentiment analysis classifier out of it. model could tell you if a review was positive or negative. mean, today we are just like, come on, anyone do that. But this was the first time that you this emergence, this sort of semantics that emerged from this underlying process. And there we knew, you’ve got to scale thing, you’ve got to see where it 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 feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And the key idea of 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 that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But as you grow the number of houses, things emerge, suburbs and cultural centers and traffic jams. Give me one for you when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model do it, which means it’s really learned an internal circuit for how to it. And the really interesting thing is actually, if you have it add like a 40-digit number plus 35-digit number, it’ll often get 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 40-digit addition table, that’s more 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, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve allowed it to scale and look at an incredible number of pieces of text. And it is learning that you didn’t know that it was going to be of learning.
GB Well, yeah, and it’s more nuanced, too. So one that we’re starting to really get good at is some of these emergent capabilities. And to do that actually, of the things I think is very undersung in field is sort of engineering quality. Like, we had to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same true in machine learning. You have to get every piece of the stack engineered properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you something deeply fundamental intelligence. If you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able to predict. So we able to predict, for example, the performance on coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I all of these are questions of degree and scale and timing. And think one thing people 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 that we think it’s so important to deploy incrementally. And so I think that what we of see right now, if you look at this talk, lot of what I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very 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 thing supervise. Like, how do you know if this book summary is good? You have to read the whole book. No one to do that.
(Laughter) And so I think that the thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record with these machines that they’re able to carry out our 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 this session, there are critics who say that, you know, there’s real understanding inside, the system is going to always — we’re never to know 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 one moment, but that the expansion of the scale and human feedback that you talked about is basically going to take it on that of actually getting to things like truth and wisdom and so forth, with high degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been just like, reality hit you in the face, right? It’s like field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something that is what you need. But I think that our 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 new paradigm. And we just haven’t exhausted the 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 public and then harness all this, you know, instead of your team giving feedback, the world is now giving feedback. … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I heard on when you were founded as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was capable slowing the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google and and so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put out here without proper guardrails or we die. You know, how you, like, make the case that what you have is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has been incredibly important, from the very beginning, when we thinking about how to build artificial general intelligence, actually it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, get this super powerful thing, and then you figure the safety of it and then you push “go,” and you hope you got it right. I don’t how to execute that plan. Maybe someone else does. But for me, that was always terrifying, didn’t feel right. And so I think that this alternative approach is the only other that I see, which is that you do let hit you in the face. And I think you do give people time give input. You do have, before these machines are perfect, before they are 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 number one thing people going to do with it was generate misinformation, try to tip elections. Instead, the one thing 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 a strong chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in the small print that says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t 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 remember I was in 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 a moment, if you could choose for basically that Pandora’s box be five years away or 500 years away, which you pick, right? On the one hand you’re like, well, maybe for you personally, it’s to have it be five years away. But if it gets to be 500 years 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 at the time and like, he puts his life on the line in a much real way than any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on the you’ve to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you at the whole history of computing, I really mean it when I say this is an industry-wide or even just almost 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 if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage to to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who what kind of safety precautions you get. And so I that one thing I take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about being like a zero to one, of, change 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 and you’ve got 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 have is that we have birthed this extraordinary child may have superpowers that take humanity to a whole new place. It is our responsibility to provide the guardrails for this child to collectively teach it to wise and not to tear us all down. Is basically the model?
GB: I think it’s true. And think it’s also important to say this 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 technology, figure out how to provide the feedback, decide we want from it. And my hope is that that will continue to be the best path, it’s so good we’re honestly having this debate because we wouldn’t otherwise it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)