We started OpenAI seven ago because we felt like something really interesting was in AI and we wanted to help steer it in a positive direction. It’s honestly just really to see how far this whole field has come since then. And it’s really to hear from people like Raymond who are using technology we are building, and others, for so many wonderful things. We hear from who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an historic period right now where we a world are going to define a technology that will be so important for our society going forward. I believe that we can manage this for good.
So today, I want to show the current state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to show you what it’s like to build a tool for an AI rather than building for a human. So we have a new DALL-E model, which generates images, and we are exposing it an app for 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, ideation and creative back-and-forth and taking care the details for you that you get out of ChatGPT. here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s what we’re going to get. But ChatGPT doesn’t just generate images this case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the power of what it can on your behalf in terms of carrying out your intent. And I’ll point out, this all a live demo. This is all generated by the AI as speak. So I actually don’t even know what we’re to see. This looks wonderful.
(Applause)
I’m getting hungry looking at it.
Now we’ve extended ChatGPT with other too, for 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 DALL-E app.” And by the way, this is coming you, all ChatGPT users, over upcoming months. And you can look the hood and see that what it actually did was write a just like a human could. And so you sort of have this ability inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, let me show you what it’s like to use that information and to with other applications too. You can say, “Now make 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 you do make this wonderful, wonderful meal, I definitely want to know it tastes.
But you can see that ChatGPT is selecting all these different without me having to tell it explicitly which ones to use 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 these apps, click between them, we copy/paste between them, and usually it’s a great experience within an app as long 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 AI able to sort of take away all those details from you. So you don’t have to be one who spells out every single sort of little of what’s supposed to happen.
And as I said, this is a live demo, so the unexpected will happen to us. But let’s take a at the Instacart shopping list while we’re at it. And you see we sent a 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? you look at this, you still can click through it and sort of modify actual quantities. And that’s something that I think shows that they’re not going away, UIs. It’s just we have a new, augmented way to build them. And we have a tweet that’s been drafted for our review, which is also a important thing. We can click “run,” and there we are, we’re the manager, we’re able inspect, we’re able to change the work of the AI if want to. And so after this talk, you will be able to access this yourself. And we go. Cool. Thank you, everyone.
(Applause)
So we’ll back to the slides. Now, the important thing about we build this, it’s not just about building these tools. It’s teaching the AI how to use them. Like, what do we even want it do when we ask these very high-level questions? And to do this, we an old idea. If you go back to Alan Turing’s 1950 paper on Turing test, he says, you’ll never program an answer this. Instead, you can learn it. You could build a machine, like a human child, and teach it through feedback. Have a human teacher who provides rewards and punishments as it tries things out does 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 unsupervised learning process. We show it the whole world, the whole internet and say, “Predict what comes next 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 only to actually complete that math problem, to say what comes next, green nine up there, is to actually solve the math problem.
But we actually have to do second step, too, which is to teach the AI what to 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 better that one.” And this reinforces not just the specific thing the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it to generalize. allows it to teach, to sort of infer your intent and apply it in scenarios that it hasn’t before, that it hasn’t received feedback.
Now, sometimes the things we have to teach the are not what you’d expect. For example, when we showed GPT-4 to Khan Academy, they said, “Wow, this is so great, We’re to be able to teach students wonderful things. Only one problem, doesn’t double-check students’ math. If there’s some bad math in there, will happily pretend that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan himself very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the of a couple of months we were able to teach the that, “Hey, you really should push back on humans this specific kind of scenario.” And we’ve actually made lots and of improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up a signal to our team to say, “Here’s an area of weakness where you should gather feedback.” so when you do that, that’s one way that really listen to our users and make sure we’re building that’s more useful for everyone.
Now, providing high-quality feedback is hard 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 stuff all the toys in the closet. This is a nice DALL-E-generated image, by way. And the same sort of reasoning applies to AI. As move to harder tasks, we will have to scale our ability to provide high-quality feedback. for this, the AI itself is happy to help. It’s happy to help provide even better feedback and to scale our ability supervise the machine as time goes on. And let me you what I mean.
For example, you can ask GPT-4 a like this, of how much time passed between these two foundational blogs on learning and learning from human feedback. And the model says months passed. But is it true? Like, these models are not 100-percent reliable, although they’re getting every time we provide some feedback. But we can use the AI to fact-check. And it can 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 and click into pages. And it actually writes out its whole chain of as it does it. It says, I’m just going to search for and it actually does the search. It then it finds the publication date and the results. It then is issuing another search query. It’s going to click into the blog post. And of this you could do, but it’s a very tedious task. It’s not a thing that humans really to do. It’s much more fun to be in driver’s seat, to be in this manager’s position where you can, if want, triple-check the work. And out 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 week, that was correct.
(Applause)
And we’ll cut back 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 to produce data for another AI to become more to a human. And I think this really shows the shape of something that we should 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 a and how we want to solve that problem. We make that the humans are providing the management, the oversight, the feedback, and the machines are operating a way that’s inspectable and trustworthy. And together we’re able actually create even more trustworthy machines. And I think that over time, if we get process right, we will be able to solve impossible problems.
And to give you a sense of how impossible I’m talking, I think we’re going to be able to rethink almost every of 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 much in that time. And here a specific spreadsheet of all the AI papers on the for the past 30 years. There’s about 167,000 of them. you can see there the data right here. But let me show you the ChatGPT on how to analyze a data set like this.
So we can give ChatGPT access to yet tool, this one a Python interpreter, so it’s able run 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, knows the 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 file, the column names like you saw and then the actual data. And that it’s able to infer what these columns actually mean. Like, that semantic information wasn’t in there. It to sort of, put together its world knowledge of that, “Oh yeah, arXiv is a site that people submit papers and that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all that, that’s work for a human to do, and the AI is happy help with it.
Now I don’t even know what 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 lots of intent behind it. But I don’t even know I want. And the AI kind of has to infer what might be interested in. And so it comes up some good ideas, I think. So a histogram of the number of per paper, time series of papers per year, word of the paper titles. All of that, I think, will be 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 the most common. It’s going to then make this nice of the papers per year. Something crazy is happening in 2023, though. Looks we were on an exponential and it dropped off cliff. What could be going on there? By the way, this is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in titles.
But I’m pretty unhappy about this 2023 thing. It makes year look really bad. Of course, the problem is that the year is over. So I’m going to push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers 2022 were even posted by April 13?] So April 13 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 it to notice this thing, maybe it’s a little bit of an overreach for to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide additional piece of, you know, guidance. And under the hood, the AI just writing code again, so if you want to inspect what it’s doing, it’s possible. And now, it does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but know what I want.
Now we’ll cut back to the slide again. slide shows a parable of how I think we … A vision of how we may end up this technology in the future. A person brought his very sick dog to the vet, and veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here had he listened. In the meanwhile, he provided the blood test, like, the medical records, to 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 to save the dog’s life. Now, systems, they’re not 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. think this is something we should all reflect on, about as we consider how to integrate these systems into world.
And one thing I believe really deeply, is that getting AI right going to require participation from everyone. And that’s for deciding we want it to slot in, that’s for setting rules of the road, for what an AI will won’t do. And if there’s one thing to take away from talk, it’s that this technology just looks different. Just different from anything had anticipated. And so we all have to 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 artificial intelligence benefits all of humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … I suspect within every mind out here there’s a feeling of reeling. Like, suspect that a very 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, need to rethink.” Like, there’s just new possibilities there. Am I right? Who thinks that they’re to rethink the way that we 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 first question actually is how the hell have you done this?
(Laughter)
OpenAI a few hundred employees. Google has thousands of employees working artificial intelligence. Why is it you who’s come up with 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 look 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 deliberate choices the early days. And the first one was just to confront reality it lays. And that we just thought really hard about like: What is 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 been to get teams of people who are very from each other to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going need it, it’s a dry-mouth topic. But isn’t there also 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, think the story there is pretty illustrative, right? I think that high level, learning, like we always knew that was what we wanted be, was a deep learning lab, and exactly how do it? I think that in the early days, we didn’t know. tried a lot of things, and one person was working on training model to predict the next character in Amazon reviews, and he got a result — this is a syntactic process, you expect, you know, model will predict where the commas go, where the and verbs are. But he actually got a state-of-the-art analysis classifier out of it. This model could tell if a review was positive or negative. I mean, today we are just like, come on, anyone do that. But this was the first time that you saw this emergence, sort of semantics that emerged from this underlying syntactic process. there we knew, you’ve got to scale this thing, you’ve got to see it goes.
CA: So I think this helps explain the that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out them feels … it just feels impossible that that could 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, suddenly things emerge. It happens all the time, ant colonies, ants run around, when you bring enough of them together, you these ant colonies that show completely emergent, different behavior. Or a city a few houses together, it’s just houses together. But you grow the number of houses, things emerge, like and cultural centers and traffic jams. Give me one moment for you when you saw just something pop 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 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 the 40-digit addition table, that’s more than there are in the universe. So it had to have learned something general, but that hasn’t really fully yet learned that, Oh, I can sort of generalize to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up look at an incredible number of pieces of text. it is learning things that you didn’t know that it was going to capable of learning.
GB Well, yeah, and it’s more nuanced, too. So one science we’re starting to really get good at is predicting some these emergent capabilities. And to do that actually, one of things I think is very undersung in this field is sort of quality. Like, we had to rebuild our entire stack. you think about building a rocket, every tolerance has to be incredibly tiny. Same is true machine learning. You have to get every single piece of the stack engineered properly, then you can start doing these predictions. There are all incredibly smooth scaling curves. They tell you something deeply fundamental about intelligence. you look at our GPT-4 blog post, you can all of these curves in there. And now we’re starting to be able predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that are 10,000 or 1,000 times smaller. And so there’s something about this is actually smooth scaling, even though it’s still early days.
CA: So here is, of the big fears then, that arises from this. it’s fundamental to what’s happening here, that as you scale up, things emerge that you can maybe in some level of confidence, but it’s capable of surprising you. Why isn’t there just 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 of integration with the world is also this incredibly emergent, of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And I think that what we kind of see right now, you look at this talk, a lot of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very to look 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, how you know if this book summary is any good? You have read the whole book. No one wants to do that.
(Laughter) And so I think that the important will be that we take this step by step. And that we say, OK, we move on to book summaries, we have to supervise this task properly. We have to build up track record with these machines that they’re able to actually carry out our intent. And I think we’re to have to produce even better, more efficient, more 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 understanding inside, the system is going to always — we’re going to know that it’s not generating errors, that it doesn’t have common and so forth. Is it your belief, Greg, that it is true at one moment, but that the expansion of the scale and the human that you talked about is basically going to take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. you be sure of that?
GB: Yeah, well, I think that OpenAI, I mean, the short answer is yes, I believe is where we’re headed. And I think that the OpenAI approach here has always just like, let reality hit you in the face, right? It’s like this is the field of broken promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going to work 70 years. They haven’t been right yet. They might be right maybe 70 years plus or something like that is what you need. But I that our approach has always been, you’ve got to push the limits of this technology to really see it in action, that tells you then, oh, here’s how we can move on to a new paradigm. 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 this, you know, instead of just your team giving feedback, the world is giving feedback. But … If, you know, bad things are going to emerge, it is there. So, you know, the original story that I on OpenAI when you were founded as a nonprofit, well you were there the great sort of check on the big companies doing their unknown, evil thing with AI. And you were going to build 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 what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such through the tech world that now Google and Meta so forth are all scrambling to catch up. And some of criticisms have been, you are forcing us to put this out here without proper guardrails we die. You know, how do you, like, make the that what you have done is responsible here and reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t we’re always going to get it right. But one thing I think has been incredibly important, from the beginning, when we were thinking about how to build artificial intelligence, actually have it benefit all of humanity, like, how are you supposed to do that, right? And default plan of being, well, you build in secret, you get super powerful thing, and then you figure out the safety of it and then push “go,” and you hope you got it right. don’t know how to execute that plan. Maybe someone does. But for me, that was always terrifying, it didn’t feel right. And so think that this alternative approach is the only other that I see, which is that you do let reality hit you in face. And I think you do give people time to give input. You do have, before these are perfect, before they are super powerful, that you have the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that number one thing people were going to do with it generate misinformation, try to tip elections. Instead, the number one thing was Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things 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 that in box is something that, there’s a very strong chance it’s something absolutely glorious that’s to give beautiful gifts to your family and to everyone. 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 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 story that I haven’t actually told before, which is that after we started OpenAI, I remember I was in Puerto Rico an AI conference. I’m sitting in the hotel room just looking out over this water, all these people having a good time. And you about it for a moment, if you could choose for basically that Pandora’s box to be years away or 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have it five years away. But if it gets to be 500 years away and people get more time to get right, which do you pick? And you know, I just felt it in the moment. I was like, of you do the 500 years. My brother was in the at the time and like, he puts his life on the line in a much more real than any of us typing things in computers and this 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 quite playing the as it truly lies. Like, if you look at the whole history computing, I really mean it when I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that you of, don’t put together the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, of these things, they are happening. And if you don’t them together, you get an overhang, which means that someone does, or the moment that someone does manage to connect the circuit, then you suddenly have this very powerful thing, one’s had any time to adjust, who knows what kind of safety precautions you get. so I think that one thing I take away is like, you think about development of other sort of technologies, think about nuclear weapons, people talk about being like zero to one, sort of, change in what humans do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it and you’ve got to figure out how to manage it for each moment that you’re it.
CA: So what I’m hearing is that you … the you want us to have is that we have birthed extraordinary child that may have superpowers that take humanity to a whole new place. It our collective responsibility to provide the guardrails for this child to collectively it to be wise and not to tear us all down. that basically the model?
GB: I think it’s true. And think it’s also important to say this may shift, right? We’ve got take each step as we encounter it. And I think it’s important today that we all do get literate in this technology, out how to provide the feedback, decide what we want it. And my hope is that that will continue to be the best path, but it’s so we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)