We started OpenAI seven years because we 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 this whole field come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where we as a world are going define a technology that will be so important for our going forward. And I believe that we can manage this good.
So today, I want to show you the current state that technology and some of the underlying design principles that we dear.
So the first thing I’m going to show you is what it’s like 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 an for ChatGPT 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, of, ideation and creative back-and-forth and taking care of the details for you that you get out 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, it doesn’t generate text, it generates an image. And that is something that really expands the 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 all generated the AI as we speak. So I actually don’t even what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, this is coming to you, ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was a prompt just like a human could. And so you sort have this ability to inspect how the machine is using these tools, which allows to provide feedback 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. You say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And make it little tricky for the AI. “And tweet it out all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, wonderful meal, I definitely to know how it tastes.
But you can see ChatGPT is selecting all these different tools without me having to it explicitly which ones to use in any situation. And this, think, shows a new way of thinking about the user interface. Like, 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 within app as long as you kind of know the and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details from you. So you don’t have to the one who spells out every single sort of piece of what’s supposed to happen.
And as I said, this is a demo, so sometimes the unexpected will happen to us. But let’s take a look the Instacart shopping list while we’re at it. And you can see we sent list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting is the traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we have a tweet that’s been for our 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 change the work of the AI we want to. And so after this talk, you will be to access this yourself. And 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 about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to do we ask these very high-level questions? And 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 program an answer to this. Instead, you can learn it. could build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and 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, we produce Turing would have called a child machine through an unsupervised learning process. We just show it the world, the whole internet and say, “Predict what comes next in you’ve never seen before.” And this process 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 green up there, is to actually solve the math problem.
But actually have to do a second step, too, which to teach the AI what to do with those skills. for this, we provide feedback. We have the AI out multiple things, give us multiple suggestions, and then human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows it to teach, to sort of infer your and apply it in scenarios that it hasn’t seen before, it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, said, “Wow, this is so great, We’re going to be able to teach wonderful things. Only one problem, it doesn’t double-check students’ math. If there’s some bad in there, it will happily pretend 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 to provide feedback to the machine our team. And over the course of a couple of months we were able to teach the that, “Hey, you really should push back on humans in this specific kind of scenario.” And we’ve actually lots and lots of improvements to the models this way. And you 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 should feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re something 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, don’t know if you’re just teaching them to stuff all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. As we move to tasks, we will have to scale our ability to provide high-quality feedback. But for this, the AI is happy to help. It’s happy to help us provide even better feedback and to scale our ability supervise the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 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, although they’re better every time we provide some feedback. But we can actually use AI 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 a new tool. This one is a browsing tool where the can issue search queries and click into web pages. And it actually writes out whole chain of thought as it does it. It says, I’m going to 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 blog post. And of this you could do, but it’s a very tedious task. It’s 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 citations so you can actually go and very easily verify any piece of this 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 to about this whole process is that it’s this many-step between a human and an AI. Because a human, using this fact-checking tool is it in order to produce data for another AI to become useful to a human. And I think this really shows the shape of something that we expect to be much more common in the future, where we humans and machines kind of very carefully and delicately designed in they fit into a problem and how we want to solve that problem. We make sure the humans are providing the management, the oversight, the feedback, the machines are operating in a way that’s inspectable and trustworthy. And together we’re able to actually even more trustworthy machines. And I think that over time, if we get this process right, we will able to solve impossible problems.
And to give you a sense just how impossible I’m talking, I think we’re going to able to rethink almost every aspect of how we with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve changed that much in that time. And here is specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can 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 another tool, this one Python interpreter, so it’s able to run code, just like a data scientist would. so you can just literally upload a file and questions about it. And very helpfully, you know, it knows name of the file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll it for you.” The only information here is the name the file, the column names like you saw and then the actual data. from that it’s able to infer what these columns mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a of authors in the paper,” like all of that, that’s for a human to do, and the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, you ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. I don’t even know what I want. And the AI kind of has to infer what 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 of papers per year, word cloud of the paper titles. All that, I think, will be pretty interesting to see. the great thing is, it can actually do it. Here go, a nice bell curve. You see that three kind of the most common. It’s going to then make nice plot of the papers 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 is Python code, you can inspect. And then we’ll see word cloud. So you can see these wonderful things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the problem is that the is not over. So I’m going to push back the machine. [Waitttt that’s not fair!!! 2023 isn’t over. percentage of papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the kind of ambitious one.
(Laughter)
So know, again, I feel like there was more I out of the machine here. I really wanted it to notice thing, maybe it’s a little bit of an overreach for it to have sort of, magically that this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is writing code again, so if you want to inspect it’s doing, it’s very possible. And now, it does the correct projection.
(Applause)
If noticed, it even updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A of how we may end up using this technology the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” the dog would not be here today had he listened. In meanwhile, he provided the blood test, like, the full medical records, to GPT-4, which said, “I am not vet, you need to talk to a professional, here some hypotheses.” He brought that information to a second vet who used to save the 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 with as a brainstorming partner was able to achieve an that would not have happened otherwise. I think this is something should all reflect on, think about as we consider how to these systems into our world.
And one thing I believe really deeply, is that getting AI right is to require participation from everyone. And that’s for deciding how we want it to in, that’s for setting the rules of the road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s this technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring artificial general intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I suspect that very large number of people viewing this, you look that and you think, “Oh my goodness, pretty much single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the way we do things? Yeah, I mean, it’s amazing, but it’s also scary. So let’s talk, Greg, let’s talk.
I mean, guess my first question actually is just how the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on artificial intelligence. is it you who’s come up with this technology that the world?
Greg Brockman: I mean, the truth is, we’re all building on shoulders of giants, right, there’s question. If you look at the compute progress, the algorithmic progress, the data progress, all of those are industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the 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 tried a of things that didn’t work, so you only see the that did. And I think that the most important thing has been to get teams of people are very different from each other to work together harmoniously.
CA: Can have the water, by the way, just brought here? I we’re going to need it, it’s a dry-mouth topic. But isn’t there something just about the fact that you saw something in these language models that 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 the there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we to be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. We tried a lot things, and one person was working on training a to predict the next character in Amazon reviews, and got a result where — this is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he actually got state-of-the-art sentiment analysis classifier out of it. This model 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 saw 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 them … it just feels impossible that that could come from a prediction machine. 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 number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when saw just something pop that just blew your mind you just did not see coming.
GB: Yeah, well, so you try this in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal circuit for how to do it. And really interesting thing is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like 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, can sort of generalize this to adding arbitrary numbers arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of of text. And it is learning things that you didn’t know it was going to be capable of learning.
GB Well, yeah, and it’s nuanced, too. So one science that we’re starting to really good at is predicting some of these emergent capabilities. And do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we to rebuild our entire stack. When you think about a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. You to get every single piece of the stack engineered properly, then you can start doing these predictions. There are these incredibly smooth scaling curves. They tell you something deeply about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re to be able to 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 something about that is actually smooth scaling, even though it’s still 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 scale up, things emerge that you can maybe predict in some of confidence, but it’s capable of surprising you. Why isn’t there just a huge risk of something 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 world also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so important to incrementally. And so I think that what we kind of see right now, you look at this talk, a lot of what I focus is providing really high-quality feedback. Today, the tasks that do, you can inspect them, right? It’s very easy to look that math problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s a hard thing to supervise. Like, how do you if this book summary is any good? You have to the whole book. No one wants to do that.
(Laughter) And so think that the important thing will be that we take this step 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, of like making the machine be aligned with you.
CA: So we’re going to hear in this session, there are critics who say that, you know, there’s no understanding inside, the system is going to always — we’re never to know that it’s not generating errors, that it doesn’t have sense and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale the human feedback that you talked about is basically going to take it on that journey of actually to 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, 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 this field the field of broken promises, of all these experts saying X going to happen, Y is how it works. People have been neural nets aren’t going to work for 70 years. They haven’t been right yet. They be right maybe 70 years plus one or something like is what you need. But I think that our approach has always been, you’ve to push to the limits of this technology to really see in action, because that tells you then, oh, here’s how we can move to a new paradigm. And we just haven’t exhausted fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, that the way to do this is to put it out there public and then harness all this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things going to emerge, it is out there. So, you know, original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on the big doing their unknown, possibly evil thing with AI. And you were going to build models that of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that Google and Meta and so forth are all scrambling to catch up. And some their criticisms have been, you are forcing us to put this out here without guardrails or we die. You know, how do you, like, the case that what you have done is responsible here and not reckless.
GB: Yeah, we think about questions all the time. Like, seriously all the time. I don’t think we’re always going to get it right. But one thing I think has been important, from the very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are you supposed to that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you out the safety of it and then you push “go,” you hope 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 that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. I think you do give people time to give input. You do have, 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 thing people were going to with it was generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there things that are much worse. Here’s a thought experiment for you. you’re sitting 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 to your family and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance that this could 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 a story that I haven’t actually before, which is that shortly after we started OpenAI, I remember I was in Puerto Rico for an conference. I’m sitting in the hotel room just looking out this wonderful water, all these people having a good time. And you think about it for a moment, if could choose for 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 you personally, it’s better to have it be five 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 felt it the moment. I was like, of course you do 500 years. My brother was in the military at the time and like, puts his life on the line in a much real way than any of us typing things in 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 that’s quite playing the field as it truly lies. Like, you look at the whole history of computing, I really mean when I say that this is an industry-wide or just almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put together pieces that are there, right, we’re still making faster computers, we’re still the algorithms, all of these things, they are happening. if you don’t put them together, you get an overhang, means that if someone does, or 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 kind 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 about nuclear weapons, people about being like a 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. And the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to it for each moment that you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have is that we have birthed this extraordinary that may have superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for this child collectively teach it to be wise and not to us all down. Is that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve to take each step as we 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 hope is that that will continue be the best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for coming to and blowing our minds.
(Applause)