We started OpenAI seven ago because we felt like something really interesting was happening in AI we wanted to help steer it in a positive direction. It’s honestly really amazing to see how far this whole field has come then. And it’s really gratifying to hear from people Raymond who are using the technology we are building, and others, for so wonderful things. We hear from people who are excited, we hear from people who are concerned, we hear people who feel both those emotions at once. And honestly, that’s we feel. Above all, it feels like we’re entering an period right now where we as a world are going to define a technology that will be so for our society going forward. And I believe that can manage this for good.
So today, I want to show you the state of that technology and some of the underlying principles that we hold dear.
So the first thing I’m going to show you is what it’s like to 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 a of it.
(Laughter)
Now you get all of the, sort of, ideation and creative back-and-forth taking care of the details for you that you get of ChatGPT. And here we go, it’s not just the idea for meal, but a very, very detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t generate images in this case — sorry, it doesn’t text, it also generates an image. And that is something that expands the power of what it can do on behalf in terms of carrying out your intent. And I’ll out, this is all a live demo. This is all by the AI as we speak. So I actually don’t know what we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT other tools too, for example, memory. You can say “save for 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 the hood and see what it actually did was write a prompt just like a human could. And so sort of have this ability to inspect how the machine is using these tools, which us to provide feedback to them.
Now it’s saved later, and let me show you what it’s like to use that information to integrate with other applications too. You can say, “Now make a shopping for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it for all the TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting all these different tools without me having to tell explicitly which ones to use in any situation. And this, I think, shows a new way 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 experience within an app as long as you kind of know the menus and know the options. Yes, I would like you to. Yes, please. Always good be polite.
(Laughter)
And by having this unified language interface on of tools, the AI is able to sort of take away those details from you. So you don’t have to be the who spells out every single sort of little piece of what’s supposed to happen.
And I said, this is a live demo, so sometimes the unexpected will happen to us. But let’s take look 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. the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you can click through it and sort of modify the quantities. And that’s something that I think shows that they’re not going away, traditional UIs. It’s just we have new, augmented way to build them. And now we a tweet that’s been drafted for our review, which also a very important thing. We can click “run,” and there we are, we’re manager, we’re able to inspect, we’re able to change 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 back to the slides. Now, the important thing about how we build this, it’s just about building these tools. It’s about teaching the AI to use them. Like, what do we even want it to when we ask these very high-level questions? And to do this, we use an old idea. you go back to Alan Turing’s 1950 paper on Turing test, he 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 and punishments as it tries things out and does things that are either good or bad.
And is exactly how we train ChatGPT. It’s a two-step process. First, we produce what Turing would have a child machine through an unsupervised learning process. We just it the whole world, the whole internet and say, “Predict what comes next in text you’ve never seen before.” this process imbues it with all sorts of wonderful skills. example, if you’re shown a math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to solve the math problem.
But we actually have to a second step, too, which is to teach the AI what do with those skills. And for this, we provide feedback. We have the AI try out things, give us multiple suggestions, and then a human rates them, “This one’s better than that one.” And this reinforces not just specific thing that the AI said, but very importantly, the whole process that AI used to produce that answer. And this allows it generalize. It allows it to teach, to sort of infer your intent and it in scenarios that it hasn’t seen before, that it hasn’t received feedback.
Now, sometimes the things we to teach the AI are not what you’d expect. For example, we first showed GPT-4 to Khan Academy, they said, “Wow, is so great, We’re going to be able to students wonderful things. Only one problem, it doesn’t double-check students’ math. there’s some bad math in there, it will happily that one plus one equals three and run with it.” we had to collect some feedback data. Sal Khan was very kind and offered 20 hours of his own to provide feedback to the machine alongside our team. And over the course of a couple months we 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 and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind like sending up a bat signal to our team to say, “Here’s an of weakness where you should gather feedback.” And so you do that, that’s one way that we really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality is a hard thing. If you think about asking kid to clean their room, if all you’re doing is the floor, you don’t know if you’re just teaching them to stuff all the toys the closet. This is a nice DALL-E-generated image, by the way. And the same sort of reasoning to AI. As we move to harder tasks, we will have to scale our ability to high-quality feedback. But for this, the AI itself is to help. It’s happy to help us provide even better and to scale our ability to supervise the machine as time goes on. let me show you what I mean.
For example, you 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 we provide some feedback. But we can use the AI to fact-check. And it can actually check own work. You can say, fact-check this for me.
Now, in this case, I’ve actually given the AI new tool. This one is a browsing tool where the model can issue search and click into web pages. And it actually writes its whole chain of thought as it does it. says, I’m just going to search for this and it actually does search. It then it finds the publication date and the search results. then is issuing another search query. It’s going to into the blog post. And all of this you could do, but it’s a tedious task. It’s not a thing that humans really want to do. It’s much more fun 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 this whole chain of reasoning. And it actually turns out two months was wrong. Two and one week, that was correct.
(Applause)
And we’ll cut back to the side. so thing that’s so interesting to me about this process is that it’s this many-step collaboration between a human an AI. Because a human, using this fact-checking tool is doing in order to produce 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 and machines kind of very carefully and delicately designed how they fit into a problem and how we want to solve problem. We make sure that the humans are providing management, the oversight, the feedback, and the machines are operating in a way that’s inspectable trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if get this process right, we will be able to impossible problems.
And to give you a sense of just how impossible I’m talking, I think we’re going be able to rethink almost every aspect of how interact with computers. For example, think about spreadsheets. They’ve been in some form since, we’ll say, 40 years ago VisiCalc. I don’t think they’ve really changed that much in that time. here is a specific spreadsheet of all the AI on the arXiv for the past 30 years. There’s 167,000 of them. And you can see there the right here. But let me show you the ChatGPT take on how to analyze data set like this.
So we can give ChatGPT access to yet another tool, this one Python interpreter, so it’s able to run code, just a data scientist would. And so you can just literally upload a file and questions about it. And very helpfully, you know, it knows the of the file and it’s like, “Oh, this is CSV,” comma-separated file, “I’ll parse it for you.” The only information here the name of the file, the column names like you saw and the actual data. And from that it’s able to infer what these actually mean. Like, that semantic information wasn’t in there. It has to of, put together its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s these things are and that these are integer values 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 to help it.
Now I don’t even know what I want to ask. So fortunately, can ask the machine, “Can you make some exploratory graphs?” And again, this is a super high-level instruction with lots of intent behind it. But I don’t know what I want. And the AI kind of has to infer what I might be in. And so it comes up with some good ideas, I think. So a histogram of number of authors per paper, time series of papers year, word cloud of the paper titles. All of that, I think, will be pretty interesting see. And the great thing is, it can actually do it. Here we go, a nice curve. You see that three is kind of the most common. It’s going to then make nice plot of the papers per year. Something crazy is happening in 2023, though. Looks like we on an exponential and it dropped off the cliff. What could going on there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you can see all these wonderful that appear in these titles.
But I’m pretty unhappy about 2023 thing. It makes this year look really bad. Of course, the problem is that year is not over. So I’m going to push back 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 there was more I wanted out of the machine here. I really it to notice this thing, maybe it’s a little of an overreach for it to have sort of, inferred magically that this is I wanted. But I inject my intent, I provide this additional of, you know, guidance. And under the hood, the AI is just writing code again, so if you to inspect what it’s doing, it’s very possible. And now, does the correct projection.
(Applause)
If you noticed, it updates the title. I didn’t ask for that, but it know I want.
Now we’ll cut back to the slide again. This shows a parable of how I think we … A vision of how we end up using this technology in the future. A person brought his very sick dog to vet, and the veterinarian made a bad call to say, “Let’s just wait and see.” And the would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, which said, “I not a vet, you need to talk to a professional, here are some hypotheses.” He brought that information to second vet who 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, that a human with a medical professional and with ChatGPT as a brainstorming partner was able achieve an outcome that would not have happened otherwise. think this is something we should all reflect on, think about as we how to integrate these systems into our world.
And thing I believe really deeply, is that getting AI right is going require participation from everyone. And that’s for deciding how we want to slot in, that’s for setting the rules of road, for what an AI will and won’t do. if there’s one thing to take away from this talk, it’s that this technology just looks different. Just different from people had anticipated. And so we all have to become literate. And that’s, honestly, of 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. I mean … I suspect that within every out here there’s a feeling of reeling. Like, I that a very large number of people viewing this, you at that and you think, “Oh my goodness, pretty much single thing about the way I work, I need rethink.” Like, there’s just new possibilities there. Am I right? thinks 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, 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 on intelligence. Why is it you who’s come up with this technology that 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, all of those really industry-wide. But I think within OpenAI, we made lot of very deliberate choices from the early days. And the one was just to confront reality as it lays. And that we just thought hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so you see the things that did. And I think that the most important thing has to get teams of people who are very different from each other to work harmoniously.
CA: Can we have the water, by the way, just brought here? I think we’re to need it, it’s a dry-mouth topic. But isn’t something 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 some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story there is pretty illustrative, right? I think high level, deep learning, like we always knew that what we wanted to be, was a deep learning lab, exactly how to do it? I think that in the early days, didn’t know. We tried a lot of things, and person was working on training a model to predict the character in Amazon reviews, and he got a result where — is a syntactic process, you expect, you know, the model predict where the commas go, where the nouns and verbs are. But he actually a state-of-the-art sentiment analysis classifier out of it. This model could you if a review was positive or negative. I mean, today we are like, come on, anyone can do that. But this was first time that you saw this emergence, this sort of semantics that emerged this underlying syntactic process. And there we knew, you’ve got to this thing, you’ve got to see where it goes.
CA: So I think helps explain the riddle that baffles everyone looking at this, because these things are described 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 showed us just now. And the key idea of emergence that when you get more of a thing, suddenly different things emerge. It happens the time, ant colonies, single ants run around, when you bring enough of together, you get these ant colonies that show completely emergent, different behavior. Or a city where few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs and cultural centers traffic jams. Give me one moment for you when you just something pop that just blew your mind that you just did see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, the will do it, which means it’s really learned an internal circuit for to do it. And the really interesting thing is actually, if you have add like a 40-digit number plus a 35-digit number, it’ll often it wrong. And so you can see that it’s really the process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than there are in the universe. So it to have learned something general, but that it hasn’t really yet learned that, Oh, I can sort of generalize this adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look an incredible number of pieces of text. And it learning things that you didn’t know that it was going to be 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 of these emergent capabilities. And to do actually, one of the things I think is very in this field is sort of engineering quality. Like, had to rebuild our entire stack. When you think about building a rocket, every tolerance has to be tiny. Same is true 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 you deeply fundamental about intelligence. If you look at our GPT-4 blog post, can see all of these curves in there. And now we’re to be able to predict. So we were able to predict, for example, the performance on coding problems. basically look at some models that are 10,000 times or 1,000 smaller. And so there’s something about this that is actually smooth scaling, though it’s still early days.
CA: So here is, one the big fears then, that arises from this. If it’s fundamental to what’s happening here, as you scale up, things emerge that you can maybe predict in some level of confidence, but it’s of surprising you. Why isn’t there just a huge risk of something truly terrible emerging?
GB: Well, 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, very thing too. And so that’s one of the reasons that think it’s so important to deploy incrementally. And so I think that we kind of see right now, if you look this talk, a lot of what I focus on providing really high-quality feedback. Today, the tasks that we do, can inspect them, right? It’s very easy to look at that problem and be like, no, no, no, machine, seven was the correct answer. even summarizing a book, like, that’s a hard thing supervise. Like, how do you know if this book summary is any good? You to read the whole book. No one wants to that.
(Laughter) And so I think that the important thing will be that we take step by step. And that we say, OK, as we move on to book summaries, we have to this task properly. We have to build up a track record with 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 ways of scaling this, sort of like making the machine be aligned 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 never to know that it’s not generating errors, that 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 the and the human feedback that you talked about is basically to take it on that journey of actually getting to things truth and wisdom and so forth, with a high degree of confidence. Can you be sure that?
GB: Yeah, well, I think that the OpenAI, I mean, the short answer yes, I believe that is where we’re headed. And think that the OpenAI approach here has always been like, let reality hit you in the face, right? It’s like field is the field of broken promises, of all these experts saying X is going happen, Y is how it works. People have been saying neural nets aren’t going to for 70 years. They haven’t been right yet. They might be maybe 70 years plus one or something like that is what you need. But I that our approach has always been, you’ve got to push to the limits this technology to really see it in action, because that you then, oh, here’s how we can move on to a new paradigm. And we haven’t exhausted the fruit here.
CA: I mean, it’s quite a controversial stance you’ve taken, the right way to do this is to put it out there in public then harness all this, you know, instead of just team giving feedback, the world is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the story that I heard on OpenAI when you were as a nonprofit, well you were there as the great sort check on the big companies doing their unknown, possibly evil thing with AI. And you were going build models that sort of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s of what I heard. And yet, what’s happened, arguably, the opposite. That your release of GPT, especially ChatGPT, sent such shockwaves through the tech world that now and Meta and so forth are all scrambling to catch up. some of their criticisms have been, you are forcing to put this out here without proper guardrails or we die. You know, how you, like, make the case that what you have done is responsible here not reckless.
GB: Yeah, we think about these questions all the time. Like, seriously all the time. And don’t think we’re always going to get it right. But one thing I has been incredibly important, from the very beginning, when we were thinking how to build artificial general intelligence, actually have it benefit all of humanity, like, how are you supposed do that, right? And that default plan of being, well, build in secret, you get this super powerful thing, then you figure out the safety of it and then push “go,” and 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. And so I think that alternative approach is the only other path that I see, which is that you do let hit you in the face. And I think you do give people time to input. You do have, before these machines are perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve 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 was generating Viagra spam.
(Laughter)
CA: So Viagra spam is bad, there are things that are much worse. Here’s a thought experiment for you. you’re sitting in a room, there’s a box on table. You believe that in that box is something that, there’s a very strong it’s something absolutely glorious that’s going to give beautiful gifts to your family and to everyone. there’s actually also a one percent thing in the print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. you open that box?
GB: Well, so, absolutely not. I you don’t do it that way. And honestly, like, I’ll tell you a that I haven’t actually told before, which is that shortly after we started OpenAI, I remember I was Puerto Rico for an AI conference. I’m sitting in hotel room just looking out over this wonderful water, all people having a good time. And you think about it for a moment, if you could for basically that Pandora’s box to be five years away or 500 years away, which would pick, right? On the one hand you’re like, well, maybe for personally, it’s better to have it be five years away. But if it gets to 500 years away and people get more time to get it right, which you pick? And you know, I just really felt it the moment. I was like, of course you do the 500 years. My brother in the military at the time and like, he puts his life on the in a much more real way than any of us things in computers and developing this technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t that’s quite playing the field as it truly lies. Like, if look at the whole history of computing, I really mean it I say that this is an industry-wide or even almost like a human-development- of-technology-wide shift. And the more that you sort of, don’t put the 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 you don’t put together, you get an overhang, which means that if someone does, or the moment that does manage to connect to the circuit, then you suddenly have this very thing, no one’s had any time to adjust, who knows what kind of precautions you get. And so I think that one thing take away is like, even you think about development of other sort of technologies, think about nuclear weapons, talk about being like a zero to one, sort of, in what humans could do. But I actually think that if you look capability, it’s been quite smooth over time. And so the history, I think, of every we’ve developed has been, you’ve got to do it incrementally and you’ve got to figure out how manage it for each moment that you’re increasing it.
CA: So what I’m hearing is that … the model you want us to have is that we have birthed this extraordinary child that may superpowers that take humanity to a whole new place. It is our collective responsibility provide the guardrails for this child to collectively teach it to be and not to tear us all down. Is that the model?
GB: I think it’s true. And I think it’s important to say this may shift, right? We’ve got to take step as we encounter it. And I think it’s incredibly important today that all do get literate in this technology, figure out how to provide feedback, decide what we want from it. And my hope is that that will continue to be best path, but it’s so good we’re honestly having debate because we wouldn’t otherwise if it weren’t out there.
CA: Greg Brockman, thank you so much for to TED and blowing our minds.
(Applause)