We started OpenAI seven years because we felt like something 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 this whole field has come since then. And it’s really gratifying to from people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people who are excited, we hear people who are concerned, we hear from people who feel both those at once. And honestly, that’s how we feel. Above all, it like we’re entering an historic period right now where as a world are going to define a technology will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show you the state of that technology and some of the underlying design that we hold 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 a human. we have a new DALL-E model, which generates images, we are exposing it as an app for ChatGPT to on your behalf. And you can do things like ask, you know, suggest nice post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of details for you that you get out of ChatGPT. And here we go, it’s not just idea for the meal, but a very, very detailed spread. let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t generate text, it also generates an image. And is something that really expands the power of what can do on your behalf in terms of carrying your intent. And I’ll point out, this is all a demo. This is all generated by the AI as we speak. So I don’t even know 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, for example, memory. can say “save this for later.” And 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 by the way, is coming to you, all ChatGPT users, over upcoming months. And you look under the hood and see that what it did was write a prompt just like a human could. so you sort of have this ability to inspect how the machine is these tools, which allows us to provide feedback to them.
Now it’s saved for later, and let 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 a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones use in any situation. And this, I think, shows a new way thinking about the user interface. Like, we are so used to thinking of, well, we have apps, we click between them, we copy/paste between them, 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. Always to be polite.
(Laughter)
And by having this unified language interface on top of tools, the is able to sort of take away all those from you. So you don’t have to be the 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 us. But let’s take a look at the Instacart shopping 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 interesting is that the traditional UI is still very valuable, right? If you at this, you still can click through it and sort modify the actual quantities. And that’s something that I think that they’re not going away, traditional UIs. It’s just have a new, augmented way to build them. And now have a tweet that’s been drafted for our review, which is also very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change the 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 build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even want it to do 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. You could build a machine, like human child, and then teach it through feedback. Have a teacher who provides rewards and punishments as it tries things out and things that are either good or bad.
And this 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 internet and say, “Predict what comes next in text you’ve never seen before.” And this process imbues it with sorts of wonderful skills. For example, if you’re shown a math problem, the way to actually complete that math problem, to say comes next, that green nine up there, is to actually solve the math problem.
But actually have to do a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us suggestions, and then a human rates them, says “This one’s 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 that answer. And this allows it to generalize. It it to teach, to sort of infer your intent and apply it in scenarios that hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things have to teach the AI are not what you’d expect. example, when we first showed GPT-4 to Khan Academy, they said, “Wow, this is great, We’re going to be able to teach students things. Only one problem, it doesn’t double-check students’ math. If there’s some math in there, it will happily pretend that one plus one three and run with it.” So we had to some feedback data. Sal Khan himself was very kind and offered 20 hours his own time to provide feedback to the machine our team. And over the course of a couple of we were able to teach the AI that, “Hey, really should push back on humans in this specific kind scenario.” And we’ve actually made lots and lots of improvements the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a bat signal to our team to say, “Here’s area of weakness where you should gather feedback.” And so when you that, that’s one way that we really listen to our users make sure we’re building something that’s more useful for everyone.
Now, providing high-quality feedback is a thing. If you think about asking a kid to clean room, if all you’re doing is inspecting the floor, don’t know if you’re just teaching them to stuff all toys in the closet. This is a nice DALL-E-generated image, by the way. the same sort of reasoning applies to AI. As we move to harder tasks, will have to scale our ability to provide high-quality feedback. But for this, the itself is happy to help. It’s happy to help provide even better feedback and to scale our ability to the machine as time goes on. And let me show what I mean.
For example, you can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says months passed. But is it true? Like, these models not 100-percent reliable, although they’re getting better every time we provide some feedback. But we can actually use AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a tool where the model can issue search queries and click into web pages. And it actually out its whole chain of thought as it does it. says, I’m just going to search for this and it does the search. It then it finds the publication date and the search results. It then issuing another search query. It’s going to click into the blog post. And all of you could do, but it’s a very tedious task. It’s not thing that humans really want to do. It’s much 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 this whole chain reasoning. And it actually turns out two months was wrong. Two months and one week, that correct.
(Applause)
And we’ll cut back to the side. And thing that’s so interesting to me about this whole is that it’s this many-step collaboration between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another AI to more useful to a human. And I think this shows the shape of something that we should expect to be much more common in the future, we have humans and machines kind of very carefully delicately designed in how they fit into a problem and how we to solve that problem. We make sure that the humans providing the management, the oversight, the feedback, and the machines are operating in way that’s inspectable and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, we get this process right, we will be able to impossible problems.
And to give you a sense of just how impossible I’m talking, think we’re going to be able to rethink almost every aspect how we interact with computers. For example, think about spreadsheets. They’ve around in some form since, we’ll say, 40 years with VisiCalc. I don’t think they’ve really changed that in that time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 of them. And you can see there data right here. But let me show you the ChatGPT take on how to analyze a data like this.
So we can give ChatGPT access to another tool, this one a Python interpreter, so it’s to run code, just like a data scientist would. And you can just literally upload a file and ask questions about it. And very helpfully, know, it knows 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 name of the file, the column names like you saw then the actual data. And 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 world knowledge of that, “Oh yeah, arXiv is a site that people papers and therefore that’s what these things are and that these are values and so therefore it’s a number 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 I don’t know what I want to ask. So fortunately, you can ask the machine, “Can you some exploratory graphs?” And once again, this is a super high-level instruction with lots of behind it. But I don’t even know what I want. And the kind of 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, word cloud of the paper titles. All that, I think, will be pretty interesting to see. And the great thing is, can actually do it. Here we go, a nice bell curve. You see that is kind of the most common. It’s going to then make this nice plot of papers per year. Something crazy is happening in 2023, though. Looks like we were on an exponential it dropped off the cliff. What could be going on there? the way, all this is Python code, you can inspect. And we’ll see word cloud. So you can see all these things that appear in these titles.
But I’m pretty about this 2023 thing. It makes this year look really bad. Of course, the is that the year is not over. So I’m going to push on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of in 2022 were even posted by April 13?] So April 13 was the cut-off date believe. Can you use that to make a fair projection? we’ll see, this is the kind of ambitious one.
(Laughter)
So you know, again, I feel like there was I wanted out of the machine here. I really wanted to 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, provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so if want to inspect what 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 what 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, the veterinarian made a bad call to say, “Let’s just wait and see.” And the dog not be here today had he listened. In the meanwhile, provided the blood test, like, the full medical records, GPT-4, which said, “I am not a vet, you 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. cannot overly rely on them. But this story, I think, shows that human with a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. I think this is we should all reflect on, think about as we how to integrate these systems into our world.
And one 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 that technology just looks different. Just different from anything people had anticipated. And so we all have become literate. And that’s, honestly, one of the reasons released ChatGPT.
Together, I believe that we can achieve the OpenAI mission of ensuring that general 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 a of reeling. Like, I suspect that a very large number of people viewing this, you look at that you think, “Oh my goodness, pretty much every single thing about way I work, I need to rethink.” Like, there’s just possibilities 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, I guess my first actually is just how the hell have you done this?
(Laughter)
OpenAI has a few hundred employees. Google has of employees working on artificial intelligence. Why is it who’s come up with this technology that shocked the world?
Greg Brockman: I mean, the is, we’re all building on shoulders of giants, right, there’s no question. If you at the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But think within OpenAI, we made a lot of very choices from the early days. And the first one was to confront reality as it lays. And that we just thought really about like: What is it going to take to make progress here? We tried a lot 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 who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, brought here? I think we’re going to need it, it’s a dry-mouth topic. But isn’t something also just about the fact that you saw something these language models that meant that if you continue invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there pretty illustrative, right? I think that high level, deep learning, like always knew that was what we wanted to be, was a deep learning lab, and exactly to 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 he 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 a state-of-the-art sentiment classifier out of it. This model could tell you if a review was positive or negative. I mean, we are just like, come on, anyone can do that. But this was the time that you 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 things are described as prediction machines. And yet, what we’re seeing out of them feels … it feels impossible that that could come from a prediction machine. Just the stuff you showed us now. And the key idea of emergence is that when you get more of a thing, different things emerge. It happens all the time, ant colonies, single ants around, when you bring enough of them together, you get these ant colonies that show emergent, different behavior. Or a city where a few houses together, it’s just houses together. as you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just 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 do it, which means it’s really an internal circuit for how to do it. And the really interesting thing is actually, if you it add like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so can see that it’s really learning the process, but hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in the universe. So it had have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s happened here is that you’ve it to scale up and look at an incredible number of pieces of text. And it is learning that you didn’t know that it was going to be capable of learning.
GB Well, yeah, it’s more nuanced, too. So one science that we’re starting really get good at is predicting some of these emergent capabilities. to do that actually, one of the things I think very undersung in this field is sort of engineering quality. Like, we had to rebuild entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And we’re starting to be able to predict. So we were able to predict, example, the 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 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 scale up, things emerge that you can maybe predict in some level of confidence, 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 degree and scale and timing. And think one thing people miss, too, is sort of integration with the world is also this incredibly emergent, sort of, powerful thing too. And so that’s one of the that we think it’s so important to deploy incrementally. so I think that what we kind of see now, if you look at this talk, a lot of what focus 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 problem and be like, no, no, no, machine, seven the correct answer. But even summarizing a book, like, that’s hard thing to supervise. Like, how do you know if this book summary any good? You have to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that take this step by step. And that we say, OK, as we move on to book summaries, we to supervise this task properly. We have to build a track record with these machines that they’re able to actually carry our intent. And I think we’re going to have produce 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 later this session, there are critics who say that, you know, there’s no real understanding inside, the is going to always — we’re never going to know that it’s not generating errors, it doesn’t have common sense and so forth. Is your belief, Greg, that it is true at any one moment, but that the expansion of scale and the human feedback that you talked about is basically going to it on that journey of actually getting to things truth and wisdom and so forth, with a high degree confidence. Can you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe that is we’re headed. And I think that the OpenAI approach here has always been just like, let hit you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y is it works. People have been saying neural nets aren’t to work for 70 years. They haven’t been right yet. They might be right maybe 70 years one or something like that is what you need. I think that our approach has always been, you’ve got to push the limits of this technology to really see it action, because that tells you then, oh, here’s how can move on 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 right way to this is to put it out there in public then harness all this, you know, instead of just your giving feedback, the world is now giving feedback. But … If, you know, bad things are going to emerge, it out there. So, you know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build models sort of, you know, somehow held them accountable and was of slowing the field down, if need be. Or at least that’s kind what I heard. And yet, what’s happened, arguably, is the opposite. That release of GPT, especially ChatGPT, sent such shockwaves through tech world that now Google and Meta and so forth are all to catch up. And some of their criticisms have been, you are us to put this out here without proper guardrails or we die. know, how do you, like, make the case that what have done is responsible here and not reckless.
GB: Yeah, we about these questions all the time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing think has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it all of humanity, like, how are you supposed to do that, right? that default plan of being, well, you build in secret, you get this super thing, and then you figure out the safety of it and you push “go,” and you hope you got it right. I don’t know to execute that plan. Maybe someone else does. But for me, that always terrifying, it didn’t feel right. And so I that this alternative approach is the only other path I see, which is that you do let reality hit in the face. And I think you do give time to give input. You do have, before these machines are perfect, they are super powerful, that you actually have the ability see them in action. And we’ve seen it from GPT-3, right? GPT-3, we really were afraid that the number one thing were going to do with it was generate misinformation, try tip elections. Instead, the number one thing was generating spam.
(Laughter)
CA: So Viagra spam is bad, but there are things are much worse. Here’s a thought experiment for you. Suppose you’re in a room, there’s a box on the table. believe that in that box is something that, there’s a very strong chance it’s 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 says: “Pandora.” And there’s a chance that this actually could unleash unimaginable evils the world. Do you open that box?
GB: Well, so, absolutely not. I think you don’t do it way. And honestly, like, I’ll tell you a story I haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all these people having a 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, would you pick, right? On the one hand you’re like, well, maybe for you personally, it’s better to it be five years away. But if it gets to be 500 years away people get more time to get it right, which do 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 like, he puts his life on the line in a more real way than any of us typing things in computers and developing technology at the time. And so, yeah, I’m really on the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I say that this is an industry-wide even just almost like a human-development- of-technology-wide shift. And the more you sort of, don’t put together the pieces that there, right, we’re still making faster computers, we’re still improving algorithms, all of these things, they are happening. And you don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage to connect to the circuit, then suddenly have this very powerful thing, no one’s had any time to adjust, who knows what of safety precautions you get. And so I think that one I take away is like, even you think about development of other sort technologies, think about nuclear weapons, people talk about being like a 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 so history, I think, of every technology we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. is our collective responsibility to provide the guardrails for child to collectively teach it to be wise and to tear us all down. Is that basically the model?
GB: think it’s true. And I think it’s also important to say may shift, right? We’ve got to take each step as encounter it. And I think it’s incredibly important today that we all do literate in this 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 if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)