We started OpenAI years ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s just really amazing to see how far this whole has come 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 who are excited, we hear from people who are concerned, we hear from who feel both those emotions at once. And honestly, that’s how feel. Above all, it feels like we’re entering an historic period right where we as a world are going to define a that will be so important for our society going forward. And I believe that we can manage for good.
So today, I want to show you the state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show you is it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we are exposing it as app for ChatGPT to use on your behalf. And can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you all of the, sort of, ideation and creative back-and-forth and taking 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 to get. ChatGPT doesn’t just generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that really expands power of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is generated by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m getting just looking at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about 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 months. And you can look under the hood and see that it actually did was write a prompt just like a could. And so you sort of have this ability inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And make it a little for the 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 see that ChatGPT is selecting all these different tools without me having to tell it explicitly which ones use in any situation. And this, I think, shows new way of thinking about the user interface. Like, we so used to thinking of, well, we have these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app as as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language on top of tools, the AI is able to sort of away all those details from you. So you don’t have to be the who 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 happen us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still very valuable, right? you look at this, you still can click through and sort of modify the actual quantities. And that’s that I think shows that they’re not going away, traditional UIs. It’s we have a new, augmented way to build them. now we have a tweet that’s been drafted for review, which is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to the work of the AI if we want to. so after 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 build this, it’s not just about these tools. It’s about teaching the AI how to them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an idea. If you go back to Alan Turing’s 1950 paper on the Turing test, says, you’ll never program an answer to this. Instead, you learn it. You could build a machine, like a child, and then teach it through feedback. Have a human teacher provides rewards and punishments as it tries things out and does things are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an learning process. We just show it the whole world, the whole internet and say, “Predict comes next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re a 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 a second step, too, which is teach the AI what to do with those skills. And for this, we provide feedback. have the AI try 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 specific thing the AI said, but very importantly, the whole process that the AI used to produce that answer. And allows it to generalize. It allows it to teach, sort of infer your intent and apply it in scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach AI are not what you’d expect. For example, when first showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re going be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, it happily pretend that one plus one equals three and with it.” So we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over course of a couple of months we were able teach the AI that, “Hey, you really should push back on humans in this 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 like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And so 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 thing. If you think about asking a kid to their room, if all you’re doing is inspecting the floor, you don’t know if you’re teaching 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 have scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help us provide better feedback and to scale our ability to supervise the machine as time goes on. And let show you what I mean.
For example, you can GPT-4 a question like this, of how much time passed between these two foundational blogs on unsupervised learning learning from human feedback. And the model says two passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting better time we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where model can issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. It says, I’m just going search for this and it actually does the search. It it finds the publication date and the search results. 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 this manager’s where you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of whole chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut to the side. And so thing that’s so interesting me about this whole process is that it’s this many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce data for AI to become more useful to a human. And I think this really shows the shape of something we should expect to be much more common in the future, we have humans and machines kind of very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make sure the humans are providing the 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 more trustworthy machines. I think that over time, if we get this process right, we will be to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact with computers. For example, think spreadsheets. They’ve been 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 spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. And can see there the data right here. But let me you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just like a data scientist would. And so can just literally upload a file and ask questions it. And very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information is the name of the file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, semantic information wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are that these are 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 I 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 intent behind it. But I don’t even know what I want. And the AI kind has to infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers per year, 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 most common. It’s going to then make this nice plot of the per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. could be going on there? By the way, all this is Python code, you can inspect. then we’ll see word cloud. So you can see all these wonderful things 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 on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of 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, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted it 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 provide this piece of, you know, guidance. And under the hood, AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know I 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 up using this technology in the future. A person his very sick dog to the vet, and the made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In the meanwhile, he provided the test, like, the full medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He that information to a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. You cannot overly rely them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner was able to an outcome that would 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 thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how want it to slot in, that’s for setting the rules of the road, what an AI will and won’t do. And if there’s one to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that can achieve the OpenAI mission of ensuring that artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. mean … I suspect that within every mind out here there’s feeling of reeling. Like, I suspect that a very number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having rethink the way that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why it you who’s come up with this technology that 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, all those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And first one was just to confront reality as it lays. that we just thought really hard about like: What it going to take to make progress here? We a lot of things that didn’t work, so you only see the that did. And I think that the most important thing has 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 something at point might emerge?
GB: Yes. And I think that, mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that was we wanted to be, was a deep learning lab, and exactly to do it? I think that in the early days, didn’t know. We tried a lot of things, and person was working on training a model to predict the next in Amazon reviews, and he got a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, where the nouns and are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was the first time that you this emergence, this sort of semantics that emerged from this syntactic process. And there we knew, you’ve got to scale thing, you’ve got to see where it goes.
CA: So I think this explain the riddle that baffles everyone looking at this, because these things are as 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. 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 bring enough of them together, you get these ant colonies that completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you saw just something pop just blew your mind 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, means it’s really learned an internal circuit for how to do it. And the interesting thing is actually, if you have it add 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, but it hasn’t generalized, right? It’s like you can’t memorize the 40-digit table, that’s more atoms than there are in the universe. So it had to learned something general, but that it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here 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 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 engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, and you can start 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 all of these curves in there. 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 that are 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 the big fears then, that arises from this. If it’s fundamental what’s happening here, that as you scale up, things emerge that you can maybe predict some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk something truly terrible emerging?
GB: Well, I think all of these questions of degree and scale and timing. And I think thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to incrementally. And so I think that what we kind of right now, if you look at this talk, a lot of what I on is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, how do you know if book summary is any good? You have to read the book. No one wants to do that.
(Laughter) And so I that the important thing will be that we take this step by step. that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record these machines that they’re able to actually carry out intent. And I think we’re going to have to produce even better, more efficient, more reliable ways 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 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 the expansion of the scale and the human feedback that you talked is basically going to take it on that journey actually getting to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think 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 works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. They might be right 70 years plus one or something like that is what you need. But think that our approach has always been, you’ve got to to the limits of this technology to really see in action, because that tells you then, oh, here’s how we move on to a new paradigm. And we just haven’t exhausted the here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the right way do this is to put it out there in public and then harness all this, you know, instead just your team giving feedback, the world is now feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded 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 going to build models that sort of, you know, somehow them accountable and was capable of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta and so forth are all scrambling catch up. And some of their criticisms have been, you forcing us to put this 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 always going to get it right. one thing I think has been incredibly important, from the very beginning, when we were about how to build artificial general intelligence, actually have benefit all of humanity, like, how are you supposed do that, right? And that 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 right. I don’t know how to execute that plan. Maybe someone else does. But me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other path I see, which is that you do let reality hit you in face. And I think you do give people time give input. You do have, before these machines are perfect, before they are super powerful, 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 people were going to do with it was generate misinformation, to 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. Suppose you’re sitting in room, there’s a box on the table. You believe that in that is something that, there’s a very strong chance it’s 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 print there that says: “Pandora.” And there’s a chance that this actually unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t 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 remember I in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? On one hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 years away and people get time to get it right, which do you pick? And you know, I just really it in 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 life on the line a much more real way than any of us typing things computers and developing this technology 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 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 improving the algorithms, all of these things, they are happening. And if you don’t them together, you get an overhang, which means that if someone does, the moment that someone does manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what kind of safety you get. And so I think that one thing I away is like, even you think about development of other of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans could do. But I think that if you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed has been, you’ve got to it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you want us to have is that have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the for this child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: I it’s true. And I think it’s also important to this may shift, right? We’ve got to take each as we encounter it. And I think it’s incredibly important today that we all get literate in this technology, figure out how to the feedback, decide what we want from it. And hope is that that will continue to be the path, but it’s so good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)