We started OpenAI seven years because we felt like something really interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just amazing to see how far this whole field has come then. And it’s really gratifying to hear from people like Raymond who are using technology we are building, and others, for so many things. We hear from people who are excited, we hear from people who are concerned, we from people who feel both those emotions at once. And honestly, that’s how we feel. all, it feels like we’re entering an historic period right now where we as world are going to define a technology that will so important for our society going forward. And I believe that can manage this for good.
So today, I want show you the current state of that technology and some of the underlying design that we hold dear.
So the first thing I’m going show you is what it’s like to build a tool for an AI rather than building it a human. So we have a new DALL-E model, which images, and we are exposing it as an app ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED meal and draw picture of it.
(Laughter)
Now you get all of the, sort of, and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, very spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate in this case — sorry, it doesn’t generate text, it also generates an image. that is something that really expands the power of it can do on your behalf in terms of carrying out intent. And I’ll point out, this is all a live demo. This is all generated by AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m getting hungry just looking at it.
Now we’ve ChatGPT with other tools too, for example, memory. You can say “save this later.” And the interesting thing about these tools is they’re very inspectable. So you this little pop up here that says “use the DALL-E app.” by the way, this is coming to you, all users, over upcoming months. And you can look under hood and see that what it actually did was write prompt just like a human could. And so you of have this ability to inspect how the machine is using these tools, which allows us to provide to them.
Now it’s saved for later, and let show you what it’s like to use that information and to integrate with applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And make it a tricky for the AI. “And tweet it out for all the TED out there.”
(Laughter)
So if you do make this wonderful, meal, I definitely want to know how it tastes.
But you see that ChatGPT is selecting all these different tools without me having to tell it explicitly ones to 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, have these apps, we click between them, we copy/paste between them, and it’s a great experience within an app as long you kind of know the menus and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by having unified language interface on top of tools, the AI is able to of take away all those details from you. So you don’t have to be the one who out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, sometimes the unexpected will happen to us. But let’s take a look at the Instacart list while we’re at it. And you can see sent a list of ingredients to Instacart. Here’s everything you need. the thing that’s really interesting is that the traditional UI is still valuable, right? If you look at this, you still can through it and sort of modify the actual quantities. that’s something that I think shows that they’re not away, traditional UIs. It’s just we have a new, augmented 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,” there we are, we’re the manager, we’re able to inspect, we’re to change the work of the AI if we want to. And so after this talk, you be able 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 build this, it’s not just about building these tools. It’s about teaching AI how to use them. Like, what do we even it to do when we ask these very high-level questions? And do this, we use an old idea. If you 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 build a machine, like a human child, and then it through feedback. Have a human teacher who provides rewards and punishments 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, produce what Turing would have called a child machine through an learning process. We just show it the whole world, the whole and say, “Predict what comes next in text you’ve never seen before.” And this process imbues with all sorts of wonderful skills. For example, if you’re shown a problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to actually solve math problem.
But we actually have to do a second step, too, which is to teach the AI to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and then a human rates them, says “This one’s than that one.” And this reinforces not just the specific thing that the AI said, but importantly, the whole process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that hasn’t received feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only 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 run with it.” So we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own time to feedback to the machine alongside our team. And over the course of a couple of months were able to teach the AI that, “Hey, you should push back on humans in this specific kind of scenario.” we’ve actually made lots and lots of improvements to the models way. And when you push that thumbs down in ChatGPT, that actually kind of like sending up a bat signal to our to say, “Here’s an area of weakness where you should gather feedback.” so when you do 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 a hard thing. If you think about asking a kid clean 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. is a nice DALL-E-generated image, by the way. And the same sort of reasoning applies to AI. we move to harder tasks, we will have to scale our ability provide high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even feedback and to scale our ability to supervise the machine as goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how much time passed these two foundational blogs on unsupervised learning and learning human feedback. And the model says two months passed. But is it true? Like, models are not 100-percent reliable, although they’re getting better every time provide some feedback. But we can actually use the AI to fact-check. And it actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a tool. This one is a browsing tool where the model can issue search queries click into web pages. And it actually writes out its whole chain of thought it does it. It says, I’m just going to search for this and it actually does the search. then it finds the publication date and the search results. then is issuing another search query. It’s going to click into blog post. And all 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 fun to be in the driver’s seat, to be this manager’s position where you can, if you want, triple-check the work. And come citations so you can actually go and very 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 interesting to me about this whole process that it’s this many-step collaboration between a human and AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And think this really shows the shape of something that should expect to be much more common in the future, where we have humans and machines kind very carefully and delicately designed in how they fit into problem and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, and machines are operating in a way that’s inspectable and trustworthy. And we’re able to actually create even more trustworthy machines. I think that over time, if we get this process right, will be able to solve impossible problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be to rethink almost every aspect of how we interact computers. For example, think about spreadsheets. They’ve been around in form since, we’ll say, 40 years ago with VisiCalc. I don’t they’ve really changed that much in that time. And is a specific spreadsheet of all the AI papers the arXiv for the past 30 years. There’s about 167,000 of them. And you can see the data right here. But let me show you the ChatGPT take on to analyze a data 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 like a scientist would. And 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 parse for you.” The only information here is the name of the file, the names like you saw and then the actual data. And from that it’s able to what these columns actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, is a site that people submit papers and therefore that’s what these things are that 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 it.
Now I don’t even know what I want to ask. So fortunately, you can ask 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 kind of has to infer what I might be interested in. And it comes up with some good ideas, I think. So a of the number of authors per paper, time series of papers per year, cloud of the paper titles. All of that, I think, will be pretty to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You that three is kind of the most common. It’s going then make this nice plot of the papers per year. crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, 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 about this 2023 thing. It makes this year look really bad. Of course, the problem that the year is not over. So I’m going push back on 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 you use that make a fair projection? So we’ll see, this is kind of ambitious one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I really wanted to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this additional piece of, you know, guidance. And the hood, the AI is just writing code again, so you want to inspect what it’s doing, it’s very possible. And now, it the correct projection.
(Applause)
If you noticed, it even updates title. I didn’t ask for that, but it know what want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how we may up using this technology in the future. A person brought very sick dog to the vet, and the veterinarian made a bad call to say, “Let’s wait and see.” And the dog would 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 need to talk to professional, here are some hypotheses.” He brought that information to a second vet used it to save the dog’s life. Now, these systems, they’re perfect. You cannot overly rely on them. But this story, I think, shows a human with a medical professional and with ChatGPT as a brainstorming partner was to achieve an outcome that would not have happened otherwise. I this is something 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 going to require participation everyone. And that’s for deciding how we want it to 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 technology just looks different. Just different from anything people had anticipated. so we all have to become literate. And that’s, honestly, one the reasons we released ChatGPT.
Together, I believe that we can achieve the mission of ensuring that artificial general intelligence benefits all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling of reeling. Like, I suspect that a large number of people viewing this, you look at that and think, “Oh my goodness, pretty much every single thing about the way I work, I to rethink.” Like, there’s just new possibilities there. Am I right? thinks that they’re having to rethink the way that do things? Yeah, I mean, it’s amazing, but it’s really scary. So let’s talk, Greg, let’s talk.
I mean, I guess my question actually is just how the hell have you 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 truth is, we’re all on shoulders of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all of those are really industry-wide. But I think OpenAI, we made a lot of very deliberate choices the early days. And the first one was just to confront reality as it lays. And we just thought really hard about like: What is it going to to make progress here? We tried a lot of that didn’t work, so you only see the things that did. I think that the most important thing has been get teams of people who are very different from other to work together harmoniously.
CA: Can we have the water, by the way, just here? I think 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 meant if you continue to invest in them and grow them, that something at point might emerge?
GB: Yes. And I think that, I mean, honestly, I think story there is pretty illustrative, right? I think that level, deep learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how to do it? I think that the early days, we didn’t know. We tried a lot of things, and one was working on training a model to predict the character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, the model will predict the commas go, where the nouns and verbs are. But actually got a state-of-the-art sentiment analysis classifier out of it. This model could tell you if a was positive or negative. I mean, today we are like, come on, anyone can 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 think this 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 feels … it just feels impossible that that could come a prediction machine. Just the stuff you showed us just now. And the idea of emergence is that when you get more a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, get these ant colonies that show completely emergent, different behavior. Or a city where a few houses together, it’s houses together. But as you grow the number of houses, things emerge, like suburbs and centers and traffic jams. Give me one moment for you you saw just something pop that just blew your mind that you just did not coming.
GB: Yeah, well, so you can try this in ChatGPT, if add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the model will do it, which it’s really learned an internal circuit for how to do it. And the really interesting is actually, if you have it add like a 40-digit plus a 35-digit number, it’ll often get it wrong. And you can see that it’s really learning the process, but it hasn’t fully generalized, right? It’s you can’t memorize the 40-digit addition table, that’s more atoms there are in the universe. So it had to have something general, but that it hasn’t really fully yet that, Oh, I can sort of generalize this to adding arbitrary of arbitrary lengths.
CA: So what’s happened here is you’ve allowed it to scale up and look at an incredible of pieces of text. And it is learning things that 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 to really 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 our entire stack. When you about building a rocket, every tolerance has to be tiny. Same is true in machine learning. You have to get every single piece of the engineered properly, and then you can start doing these predictions. There all these incredibly smooth scaling curves. They tell you something fundamental about intelligence. If you look at our GPT-4 post, you can see all of these curves in there. And now we’re to be able to predict. So we were able predict, for example, the performance on coding problems. We basically look at some that are 10,000 times or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: here is, one of the big fears then, that arises this. If it’s fundamental to what’s happening here, that you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t just a huge risk of something truly terrible emerging?
GB: Well, think all of these are questions of degree and and timing. And I think one thing people miss, too, is sort the integration with the world is also this incredibly emergent, sort of, very powerful thing too. And that’s one of the reasons that we think it’s so to deploy incrementally. And so I think that what we of see right now, if you look at this talk, a lot of I focus on is providing really high-quality feedback. Today, the tasks we do, you can inspect them, right? It’s very easy to at that math problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing to supervise. Like, do you know if this book summary is any good? You to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this step step. And that we say, OK, as we move on to book summaries, we have to supervise this properly. We have to build up a track record with machines that they’re able to actually carry out our intent. I think we’re going to have to produce even better, more efficient, reliable ways of scaling this, sort of like making machine be aligned with you.
CA: So we’re going to hear later in this session, are critics who say that, you know, there’s no real 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 at any one moment, but that the expansion of the scale the human feedback that you talked about is basically to take it on that journey of actually getting to like truth and wisdom and so forth, with a high degree of confidence. Can you be 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 approach here has been just like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts X is going to happen, Y is how it works. People been saying neural nets aren’t going to work for 70 years. They haven’t right yet. They might be right maybe 70 years plus one or something like that what you need. But I think that our approach has always been, you’ve got to push to limits of this technology to really see it in action, because tells you then, oh, here’s how we can move on 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 this is to put it out there in public then harness all this, you know, instead of just your team feedback, the world is now giving feedback. But … If, you know, bad things are to emerge, it is out there. So, you know, the original story that I heard on OpenAI when were founded as a nonprofit, well you were there as the great sort of on the big companies doing their unknown, possibly evil thing AI. And you were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. Or at that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so forth are scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper guardrails or we die. know, how do you, like, make the case that you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously all the time. 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 artificial intelligence, actually have it benefit all of humanity, like, are you supposed to do that, right? And that default plan of being, well, you in secret, you get this super powerful thing, and then figure out the safety of it and then you push “go,” you hope you got it right. I don’t know how to execute that plan. someone else does. But for me, that was always terrifying, it didn’t feel right. so I think that this alternative approach is the other path that I see, which is that you do let reality hit you in the face. I think you do give people time to give input. You do have, before these machines are perfect, before are super powerful, that you actually have the ability to them in action. And we’ve seen it from GPT-3, right? GPT-3, we were afraid that the number one thing people were going to do with it was generate misinformation, to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, but there are that are much worse. Here’s a thought experiment for you. Suppose you’re sitting in room, there’s a box on the table. You believe in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a percent thing in the small print there that says: “Pandora.” And there’s a chance that this actually could unimaginable evils on the world. Do you open that box?
GB: Well, so, absolutely not. I think don’t do it that way. And honestly, like, I’ll you a story that I haven’t actually told before, which is that shortly after we OpenAI, I remember I was in Puerto Rico for an AI conference. I’m sitting the hotel room just looking out over this wonderful water, all these people having a good time. you think about it for a moment, if you could for basically that Pandora’s box to be five years or 500 years away, which would you pick, right? the one hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to be 500 years and people get more time to get 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 military the time and like, he puts his life on the line in a much more real way any of us typing things in computers and developing this at the time. And so, yeah, I’m really sold on you’ve got to approach this right. But I don’t think that’s quite playing the field as truly lies. Like, if you look at the whole history of computing, I really mean it I say that this is an industry-wide or even just almost like 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. And if don’t put them together, you get an overhang, which that if someone does, or the moment that someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s had any time adjust, who knows what kind of safety precautions you get. And so I think that one I take away is like, even you think about development of sort of technologies, think about nuclear weapons, people talk about like a zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s been quite smooth over time. so the history, I think, of every technology we’ve developed been, you’ve got to do it incrementally and you’ve got to figure out how to manage for each moment that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to is that we have birthed this extraordinary child that may have superpowers that take humanity to whole new place. It is our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. Is 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 that we all get literate in this technology, figure out how to provide the feedback, decide we want from it. And my hope is that that will to be the best path, but 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 TED blowing our minds.
(Applause)