We started OpenAI seven years ago because we felt like really interesting was happening in AI and we wanted help steer it in a positive direction. It’s honestly just really amazing to see how far this field has come since then. And it’s really gratifying to hear from people like Raymond are using the technology we are building, and others, for many wonderful things. We hear from people who are excited, we hear from people are concerned, we hear from people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an period right now where we as a world are to define a technology that will be so important our society going forward. And I believe that we manage this for good.
So today, I want to show the current state of that technology and some of the design principles that we hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather than building it for human. So we have a new DALL-E model, which generates images, and we exposing it as an app for ChatGPT to use your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now get all of the, sort of, ideation and creative back-and-forth and taking of the details for you that you get out ChatGPT. And here we go, it’s not just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, also generates an image. And that is something that expands the power of what it can do on your behalf in terms carrying out your intent. And I’ll point out, this is all a live demo. This is all by the AI as we speak. So I actually don’t even know what we’re going see. This looks wonderful.
(Applause)
I’m getting hungry just looking it.
Now we’ve extended ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re inspectable. So you get this little pop up here that says “use the DALL-E app.” And the way, this is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it actually was write a prompt just like a human could. And so you sort of have this ability to how the machine is using these tools, which allows us to provide feedback to them.
Now it’s for later, and let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping list for the tasty thing I 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 do this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly ones to use in any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to thinking of, well, have these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of the menus and know all the options. Yes, I would like you to. Yes, please. good to be polite.
(Laughter)
And by having this unified language interface 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 who spells out every sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the shopping list while we’re at it. And you can see we sent a list ingredients to Instacart. Here’s everything you need. And the that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can click it and sort of modify the actual quantities. And that’s something that think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” there we are, we’re the manager, we’re able to inspect, we’re able to change the of the AI if we want to. And so after this talk, you 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 we build this, it’s not just about building these tools. It’s about teaching the AI to use them. Like, what do we even want to do when we ask these very high-level questions? to do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never program an to this. Instead, you can learn it. You could build a machine, like human child, and then teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does things that are either good bad.
And this is exactly how we train ChatGPT. It’s two-step process. First, we produce what Turing would have called a child through an unsupervised 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 process it with all sorts of wonderful skills. For example, if you’re shown a math problem, the 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 a step, too, which is to teach the AI what to do with those skills. And this, we provide feedback. We have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than that one.” And this reinforces not the specific thing that the AI said, but very importantly, whole process that the AI used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent apply it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to the AI are not what you’d expect. For example, we 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, it doesn’t double-check students’ math. there’s some bad math in there, it will happily pretend that one plus one equals three and run it.” So we had to collect some feedback data. Sal Khan himself 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, really should push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to models this way. And when you push that thumbs down in ChatGPT, that actually is of like sending up a bat signal to our team say, “Here’s an area of weakness where you should gather feedback.” And when you do that, that’s one way that we listen to our users and make sure we’re building that’s more 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 if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, the way. And the same sort of reasoning applies to AI. As we to harder tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as time on. And let me show you what I mean.
For example, you can ask GPT-4 a question like this, how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says 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 we can actually use the AI fact-check. And it can actually check its own work. can say, fact-check this 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 and click into web pages. And it actually writes 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 results. It then is issuing another search query. It’s going to click the blog post. And all of this you could do, but it’s a very task. It’s not a thing that humans really want to do. It’s much more to be in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations you can actually go and very easily verify any piece of this whole chain of reasoning. And it turns out two months was wrong. Two months and one week, that was correct.
(Applause)
And we’ll back to the side. And so thing that’s so to me about this whole process is that it’s many-step collaboration between a human and an AI. Because human, using this fact-checking tool is doing it in order to produce for another AI to become more useful to a human. And think this really shows the shape of something that we should expect to be much more common in future, where we have humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve problem. We make sure that the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even more trustworthy machines. And I think over time, if we get this process right, we will be able solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to be able rethink almost every aspect of how we interact with computers. For example, 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 of all the AI papers on the arXiv for past 30 years. There’s about 167,000 of them. And can see there the data right here. But let show you the ChatGPT take on how to analyze a set like this.
So we can give ChatGPT access yet another tool, this one a Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a and ask questions about it. And very helpfully, you know, it the name of the file and it’s like, “Oh, this CSV,” comma-separated value file, “I’ll parse it for you.” The only information here is the name of file, the column names like you saw and then the actual data. from that it’s able to infer what these columns actually mean. Like, that information wasn’t in there. It has to sort of, put together its knowledge of knowing that, “Oh yeah, arXiv is a site that people submit papers and therefore that’s what things are and that these are integer values and so therefore it’s number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I want ask. So fortunately, you can ask the machine, “Can make some 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. the AI kind of has to infer what I might interested in. And so it comes up with some ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. of that, I think, will be pretty interesting to see. And great thing is, it 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 nice of the papers per year. Something crazy is happening 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 pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem is that the year is 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 cut-off date I believe. Can you use that to a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like there was more wanted out of the machine here. I really wanted it to notice this thing, maybe it’s a bit of an overreach for it to have sort of, inferred that this is what I wanted. But I inject intent, I provide this additional piece of, you know, guidance. And under the hood, the AI is just writing again, so 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 in the future. A person brought his very sick dog to the vet, and the veterinarian made a call to say, “Let’s just wait and see.” And dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the medical records, to GPT-4, which said, “I am not a vet, need to talk to a professional, here are some hypotheses.” He brought that information to second vet who used it to save the dog’s life. Now, these systems, they’re not perfect. You cannot rely on them. But this story, I think, shows that a human with a professional and with ChatGPT as a brainstorming partner was able to an outcome that would not have happened otherwise. I think is something we should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI is going to require participation from everyone. And that’s for deciding how we want it slot in, that’s for setting the rules of the road, for an AI will and won’t do. And if there’s 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 OpenAI mission ensuring that artificial general intelligence benefits all of 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, I suspect that a very large of people viewing this, you look at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s new possibilities there. Am I right? Who thinks that they’re having rethink 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 is just how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees on artificial intelligence. Why is 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 look at the compute progress, the algorithmic progress, data progress, all of those are really industry-wide. But I within OpenAI, we made a lot of very deliberate from the early days. And the first one was just to reality as it lays. And that we just thought hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, you only see the things that did. And I think that the most thing has been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, think the story there is pretty illustrative, right? I that high level, deep learning, like we always knew that was what we to be, was a deep learning lab, and exactly how to do it? I that in the early days, we didn’t know. We tried a lot of things, and one person working on training a model 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 where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could tell you a review was positive or negative. I mean, today we just like, come on, anyone can do that. But this was the first time that saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the riddle that everyone looking at this, because these things are described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could come from prediction machine. Just the stuff you showed us just now. And key idea of emergence is that when you get more of thing, suddenly different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers and jams. Give me one moment for you when you saw just something pop that blew your mind that you just 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, the model will it, which means it’s really learned an internal circuit for how do it. And the really interesting thing is actually, you have it add like a 40-digit number plus 35-digit number, it’ll often get it wrong. And so you can that it’s really learning the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are the universe. So it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened is that you’ve allowed it to scale up and at an incredible number of pieces of text. And is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. one science that we’re starting to really get good is predicting 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 our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have 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 to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some models are 10,000 times or 1,000 times smaller. And so there’s about this that is actually smooth scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, emerge that you can maybe predict in some level confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something truly emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing miss, too, is sort of the integration with the world is this incredibly emergent, sort of, very powerful thing too. so that’s one of the reasons that we think it’s so important deploy incrementally. And so I think that what we kind of see right now, if you look at talk, a lot of what I focus on is really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if book summary is any good? You have to read the whole book. No wants to do that.
(Laughter) And so I think that the important thing will be that we this step by step. And that we say, OK, we move on to book summaries, we have to supervise this 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 produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: So we’re to hear later in this session, there are critics say that, you know, there’s no real understanding inside, system is going to always — we’re never going to that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at any moment, but that the expansion of the scale and the human that you talked about is basically going to take it on that journey of actually getting things like truth and wisdom and so forth, with a high degree of confidence. Can you sure of that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And I think that the OpenAI here has always been just like, let reality hit in the face, right? It’s like this field is the of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t been yet. They might be right maybe 70 years plus one something like that is what you need. But I think our approach has always been, you’ve got to push to the of this technology to really see it in action, that tells you then, oh, here’s how we can on to a new paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right way to do 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 giving feedback. But … If, you know, things are going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you founded as a nonprofit, well you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if be. Or at least that’s kind of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the world that now Google and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all the time. Like, seriously the time. And I don’t think we’re always going to get it right. one thing I think has been incredibly important, from very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how you supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then you figure out the safety of it then you push “go,” and you hope you got right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t right. And so I think that this alternative approach is the only other path that I see, which that you do 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 in action. we’ve seen it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that are worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. You believe in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful gifts to your family to everyone. But 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 actually told before, which is shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room just looking out over wonderful water, all these people having a good time. And think about it for a moment, if you could choose basically that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for you personally, it’s better have it be five years away. But if it gets to 500 years away and people get more time to 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 at the time and like, he puts his life the line in a much more real way than any of us things in computers and developing this technology at the time. so, yeah, I’m really sold on the you’ve got to approach this right. But 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 say that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces 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, the moment that someone does manage to connect to circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one thing I take away is like, even think about development of other sort of technologies, think nuclear weapons, people talk about being like a zero one, sort of, change in what humans could do. But I actually think that if you at capability, it’s been quite smooth over time. And so the history, think, of every technology we’ve developed has been, you’ve got do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that you … the you want us to have is that we have this extraordinary child that may have superpowers that take humanity a whole new place. It is our collective responsibility to the guardrails for this child to collectively teach it be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And I think it’s also important to say this shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today we all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my is that that will continue to be the best path, it’s so good we’re honestly having this debate because wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)