We started OpenAI seven years ago because we felt something really interesting was happening in AI and we to help steer it in a positive direction. It’s just really amazing to see how far this whole field has since then. And it’s really gratifying to hear from like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we hear from people who concerned, we hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels like we’re entering historic period right now where we as a world are going to define technology that will be so important for our society forward. And I believe that we can manage this good.
So today, I want to show you the state of that technology and some of the underlying design principles we hold dear.
So the first thing I’m going to you is what it’s like to build a tool an AI rather than building it for a human. So we have a new DALL-E model, generates images, and we are exposing it as an for ChatGPT to use on your behalf. And you can do things like ask, know, suggest a nice post-TED meal and draw a of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for you that you get out of ChatGPT. here we 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 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. is all generated by the AI as we speak. I actually don’t even know what we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other tools too, example, memory. You can say “save this for later.” the interesting thing about these tools is they’re very inspectable. 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, upcoming months. And you can look under the hood see that what it actually did was write a prompt just like human could. And so you sort of have this ability to inspect how machine is using these tools, which allows us to provide feedback 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 can say, “Now a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for the TED viewers out there.”
(Laughter)
So if you do this wonderful, wonderful meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools me having to tell it explicitly which ones to use any situation. And this, I think, shows a new of thinking about the user interface. Like, we are so to thinking of, well, we have these apps, we between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the menus and know all options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all those details from you. you don’t have to be the one who spells out every single sort of little piece what’s supposed to happen.
And as I said, this a live demo, so sometimes the unexpected will happen to us. But let’s a look at the Instacart shopping list while we’re at it. And you see we sent a list of ingredients to Instacart. Here’s you need. And the thing that’s really interesting is that the traditional UI still very valuable, right? If you look at this, still can click through it and sort of modify the actual quantities. And that’s something that I shows that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now we have tweet that’s been drafted for our review, which is a very important thing. We can click “run,” and there we are, we’re the manager, we’re to inspect, we’re able to change the work of the AI if we want to. And so 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 use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use old 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 can learn it. You could build machine, like a human child, and then teach it through feedback. Have a human who provides rewards and punishments as it tries things out and does things that are good or bad.
And this is exactly how we train ChatGPT. It’s a two-step process. First, we what Turing would have called a child machine through an learning process. We just show it the whole world, the whole internet say, “Predict what comes next in text you’ve never before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only to actually complete that math problem, to say what comes next, that nine up there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to do those skills. And for this, we provide feedback. We have AI try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” And this reinforces just the specific thing that the AI said, but very importantly, the whole that the AI used to produce that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it scenarios that it hasn’t seen before, that it hasn’t feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students wonderful things. Only 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 run with it.” So we had to collect some feedback data. Khan himself was very kind and offered 20 hours of his own time to provide feedback the machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you really should push on humans in this specific kind of scenario.” And we’ve actually made lots and lots improvements to the models this way. And when you push thumbs down in ChatGPT, that actually is kind of like up a bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when 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 clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to all the toys in the closet. This is a DALL-E-generated image, by the way. And the same sort reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s to help us provide even better feedback and to scale our to supervise the machine as time goes on. And let me show you I mean.
For example, you can ask GPT-4 a question like this, of how much passed between these two foundational blogs on unsupervised learning and learning from feedback. And the model says two months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this for me.
Now, in case, I’ve actually given the AI a new tool. This is a browsing tool where the model can issue search queries 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 then it finds the publication date the search results. It then is issuing another search query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a thing that humans want to do. It’s much more fun to be in the driver’s seat, be in this manager’s position where you can, if you want, triple-check the work. And out come so you can actually go and very easily verify any piece of this whole chain 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 collaboration between human and an AI. Because a human, using this fact-checking tool doing it in order to produce data for another AI to more useful to a human. And I think this really shows the shape of something we should expect to be much more common in future, where we have humans and machines kind of carefully and delicately designed in how they fit into a problem and how we want solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to create even more trustworthy machines. And I think that over time, if we get this right, we will be able to solve impossible problems.
And to give a sense of just how impossible I’m talking, I think we’re going to be able to almost every aspect of how we interact with computers. example, think 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 that much in time. And here is a specific spreadsheet of all the AI papers on the arXiv for the 30 years. There’s about 167,000 of them. And you can see there the data right here. But let show you the ChatGPT take on how to analyze a data set this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able to code, just like a data scientist would. And so you can just literally a file and ask questions about it. And very helpfully, you know, it knows the name of file and it’s like, “Oh, this is CSV,” comma-separated value file, “I’ll parse it you.” The only information here is the name of file, the column names like you saw and then the actual data. And from that it’s to infer what these columns actually mean. Like, that semantic information wasn’t in there. 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 in the paper,” like all of that, that’s work for a human to do, the AI is happy to help with it.
Now don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And once again, this is a super high-level instruction lots of 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 it comes up with some good ideas, I think. a histogram of the number of authors per paper, time series of papers per year, word cloud the paper titles. All of that, I think, will 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 papers per year. Something is happening in 2023, though. Looks like we were on exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you can inspect. And then we’ll word cloud. So you can see all 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, 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 papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, is the kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted it to this 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 piece of, you know, guidance. And 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 know what want.
Now we’ll cut back to the slide again. This slide shows a parable of I think we … A vision of how we may end using this technology in the future. A person brought his very dog to the vet, and the veterinarian made a bad call to say, “Let’s just wait see.” And the dog would not be here today had he listened. the meanwhile, he provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you to talk to a professional, here are some hypotheses.” brought 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 achieve outcome that would not have happened otherwise. I think is something we should all reflect on, think about we consider how to integrate these systems into our world.
And one thing I really deeply, is that getting AI right is going 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 thing to take away from this talk, it’s that this just looks different. Just different from anything people had anticipated. And we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can the OpenAI mission of ensuring that artificial general intelligence benefits 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 large number people viewing this, you look at that and you think, “Oh goodness, pretty much every single thing about the way work, I need to rethink.” Like, there’s just new possibilities there. Am I right? Who 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, guess my first question actually is just how the hell have done this?
(Laughter)
OpenAI has a few hundred employees. has thousands of employees working on artificial intelligence. Why is it you who’s come up this technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all on shoulders of giants, right, there’s no question. If look at the compute progress, the algorithmic progress, the data progress, of those are really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. the first one was just to confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot of that didn’t work, so you only see the things that did. And think that the most important thing has been to get of people who are very different from each other to together harmoniously.
CA: Can we have the water, by way, just brought here? I think we’re going to it, it’s a dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models that meant that if you continue to invest them and grow them, that something at some point 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, we always knew that was what we wanted to be, was a learning lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a of things, and one person was working on training a model to predict the next in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model will predict where 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 negative. I 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 underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve to see where it goes.
CA: So I think helps 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 feels … it just feels impossible that could come from a prediction machine. Just the you showed us just now. And the key idea of emergence is when you get more of a thing, suddenly different things emerge. happens all the time, ant colonies, single ants run around, when 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 moment for when you saw just something pop that just blew mind that you just did not see coming.
GB: Yeah, well, so you can try in ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which means it’s 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 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 had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at incredible number of pieces of text. And it is learning that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science that we’re starting to really get at is predicting some of these emergent capabilities. And to do that actually, one the things I think is very undersung in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building rocket, every tolerance has to be incredibly tiny. Same is in machine learning. You have to get every single piece the stack engineered properly, and then you can start doing predictions. There are all these incredibly smooth scaling curves. They tell something deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see all of curves in there. And now we’re starting to be able to predict. So we were to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that as you up, things emerge that you can maybe predict in level of confidence, but it’s capable of surprising you. Why isn’t there just huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions degree and scale and timing. And I think one 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 see right now, if you look at this talk, a lot of what focus on is providing really high-quality feedback. Today, the tasks that we do, you can them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But summarizing a book, like, that’s a hard thing to supervise. Like, how you know if this book summary is any good? have to read the whole book. No one wants to do that.
(Laughter) And so I think that important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise this task properly. have to build up a track record with 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 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 going to know that it’s generating errors, that it doesn’t have common sense and forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the and the human feedback that you talked about is basically going to take it on that journey of getting to things like truth and wisdom and so forth, a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here always been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all experts saying X is going to happen, Y is how it works. People have been saying nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years one or something like that is what you need. But I think that our approach always been, you’ve got to push to the limits of this to really see it in action, because that tells 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 stance you’ve taken, that the right way to do is to put it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, things are going to emerge, it is out there. So, know, the original story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great of check on the big companies doing their unknown, possibly evil thing with AI. And you were to build models that sort of, you know, somehow held them accountable and was capable of slowing the down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves through the tech world now Google and Meta and so forth are all scrambling to up. And some of their criticisms have been, you are forcing us to put this out 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 it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan of being, well, you build secret, you get this super powerful thing, and then you figure out the safety of and 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, didn’t feel right. And so I think that this alternative approach the only other path that I see, which is that you do let reality hit you the face. And I think you do give people time to give input. You have, before these machines are perfect, before they are powerful, that you actually have the ability to see them action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one people were going to do with it was generate misinformation, try to tip elections. Instead, the number one thing generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are things that 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 very strong chance it’s something absolutely glorious that’s going to give beautiful gifts to your family and everyone. But there’s actually also a one percent thing in the small print there that says: “Pandora.” there’s a chance that this actually could unleash unimaginable evils on world. Do you open that box?
GB: Well, so, not. I think you don’t do it that way. And honestly, like, I’ll tell a story that I haven’t actually told before, which that shortly after we started OpenAI, I remember I was in Rico for an AI conference. I’m sitting in the hotel room looking out over this wonderful water, all these people a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five away or 500 years away, which would you pick, right? On the one hand you’re like, well, maybe you personally, it’s better to have it be five away. But if it gets to be 500 years away and people more time to get it right, which do you pick? And you know, just really felt it in 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 computers developing this technology 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 when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t together the pieces that are there, right, we’re still making computers, we’re still improving the algorithms, all of these things, they are happening. And if you don’t put together, you get an overhang, which means that if someone does, or the that someone does manage to connect to the circuit, you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of safety precautions you get. And so think that one thing I take away is like, even you think development of other sort of technologies, think about nuclear weapons, talk 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 smooth over time. And so the history, I think, every technology we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure out to manage it for each moment that you’re increasing it.
CA: what I’m hearing is that you … the model you us to have is that we have birthed this extraordinary child that have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively teach it to be and not to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important say 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 do get literate in technology, figure out how to provide the feedback, decide we want from it. And my hope is that will continue to be the best path, but it’s 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)