We OpenAI seven years 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 just amazing to see how far this whole field has come since then. And it’s gratifying to hear from people like Raymond who are using the technology we are building, and others, so many wonderful things. We hear from people who are excited, we from people who are concerned, we hear from people who feel 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 a world are going define a technology that will be so important for our society going forward. And I that we can manage this for good.
So today, I to show you the current state of that technology and some of the underlying design principles that hold dear.
So the first thing I’m going to you is what it’s like to build a tool for an AI rather building it for a human. So we have a new DALL-E model, which generates images, and are exposing it as an app for ChatGPT to use on your behalf. And you can things like ask, you know, suggest a nice post-TED and draw a picture of it.
(Laughter)
Now you get of the, sort of, ideation and creative back-and-forth and taking care of the details for you you get out of ChatGPT. And here we go, it’s just the idea for the meal, but a very, detailed spread. So let’s see what we’re going to get. But ChatGPT doesn’t just generate images in case — sorry, it doesn’t generate text, it also generates image. And that is something that really expands the of what it can do on your behalf in terms of carrying out your intent. I’ll point out, this is all a live demo. This is all generated the AI as we speak. So I actually don’t even know we’re going to see. This looks wonderful.
(Applause)
I’m hungry just looking at it.
Now we’ve extended ChatGPT with tools too, for example, memory. You can say “save this for later.” And the thing about these tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by way, this is coming to you, all ChatGPT users, over upcoming months. you can look under the hood and see that it actually did was write a prompt just like human could. And so you sort of have this ability to inspect how the is using these tools, which allows us to provide feedback them.
Now it’s saved for later, and let me show you what it’s like to use information and to integrate with other applications too. You say, “Now make a shopping list for the tasty thing I was suggesting earlier.” And it a little tricky for the AI. “And tweet it out for all TED viewers out there.”
(Laughter)
So if you do make wonderful, wonderful meal, I definitely want to know how it tastes.
But can see that ChatGPT is selecting all these different tools without having to tell it explicitly which ones to use any situation. And this, I think, shows a new way of about the user interface. Like, we are so used to of, well, we have these apps, we click between them, we copy/paste between them, and usually it’s a great within an app as long as you kind of know the and know all the options. Yes, I would like to. Yes, please. Always good to be polite.
(Laughter)
And by this unified language interface on top of tools, the AI able to sort of take away all those details you. So you don’t have to be the one spells out every single sort of little piece of what’s to happen.
And as I said, this is a live demo, so sometimes the unexpected happen to us. But let’s take a look at the Instacart shopping while we’re at it. And you can see we sent a list of ingredients Instacart. Here’s everything you need. And the thing that’s really interesting is that the traditional is still very valuable, right? If you look at this, you still can click through it and sort modify the 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 them. And now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and there are, we’re the manager, we’re able to inspect, we’re able to change work of the AI if we want to. And so after this talk, will be able to access this yourself. And there we go. Cool. you, everyone.
(Applause)
So we’ll cut back to the slides. Now, the thing about how we build this, it’s not just about these tools. It’s about teaching the AI how to use them. Like, do we even want it to do when we these very high-level questions? And to do this, we use an old idea. If you go to Alan Turing’s 1950 paper on the Turing test, he says, you’ll never an answer to this. Instead, you can learn it. could build a machine, like a human child, and teach it through feedback. Have a human teacher who rewards and punishments as it tries things out and does that are either good or bad.
And this is exactly how train ChatGPT. It’s a two-step process. First, we produce what Turing would have called a child through an unsupervised learning process. We just show it whole world, the whole internet and say, “Predict what next in text you’ve never seen before.” And this imbues it with all sorts of wonderful skills. For example, if you’re shown math problem, the only way to actually complete that math problem, to what comes next, that green nine up there, is to actually the math problem.
But we actually have to do a second step, too, is to teach the AI what to do with those skills. And for this, we provide feedback. have the AI try out multiple things, give us multiple suggestions, then a human rates them, says “This one’s better than one.” And this reinforces not just the specific thing that the AI said, but very importantly, the process that the AI used to produce that answer. And this it to generalize. It allows it to teach, to of infer your intent and apply it in scenarios it hasn’t seen before, that it hasn’t received feedback.
Now, the things we have 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. If there’s some bad math in there, it will pretend 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 a couple of months we were able to teach AI that, “Hey, you really should push back on humans in this kind of scenario.” And we’ve actually made lots and lots of improvements to the models this way. when you push that thumbs down in ChatGPT, that actually is kind of sending up a bat signal to our team to say, “Here’s an area of weakness where should gather feedback.” And so when you do that, that’s one way we really listen to our users and make sure we’re building something that’s more useful everyone.
Now, providing high-quality feedback is a hard thing. you think about asking a kid to clean their room, all you’re doing is inspecting the floor, you don’t if you’re just teaching them to stuff all the in the closet. This is a nice DALL-E-generated image, 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 happy to help. It’s happy to help us provide even better and to scale our ability to supervise the machine time goes on. And let me show 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 unsupervised learning learning from 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 we provide some feedback. But we can actually use the AI to fact-check. And can actually check its own work. You can say, fact-check this me.
Now, in this case, I’ve actually given the AI a new tool. This one is a browsing where the model can issue search queries and click web pages. And it actually writes out its whole chain thought 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 and the search results. It then is issuing another query. It’s going to click into the blog post. And all this you could do, but it’s a very tedious task. It’s not a thing humans really want to do. It’s much more fun to in the driver’s seat, to be in this manager’s position you can, if you want, triple-check the work. And out come citations so you can actually and very easily verify any piece of this whole chain of reasoning. And actually turns out two months was wrong. Two months and one week, was correct.
(Applause)
And we’ll cut back to the side. And so thing that’s interesting to me about this whole process is that it’s this many-step collaboration between human and an AI. Because a human, using this fact-checking tool is doing it in order to data for another AI to become more useful to a human. And I think this really shows the of something that we should expect to be much common in the future, where we have humans and machines of very carefully and delicately designed in how they fit into a problem and we want to solve that problem. We make sure that the humans are the management, the oversight, the feedback, and the machines operating in a way that’s inspectable and trustworthy. And together we’re able to actually create even trustworthy machines. And I think that over time, if get this process right, we will be able to solve problems.
And to give you a sense of just impossible I’m talking, I think we’re going to be able to rethink almost aspect 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 time. And here is a specific spreadsheet of all the AI papers on arXiv for the past 30 years. There’s about 167,000 them. And you can see there the data right here. But 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. very helpfully, you 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 information here is the 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 mean. Like, that semantic information wasn’t in there. It has to sort of, put together its world knowledge knowing that, “Oh yeah, arXiv is a site that submit papers and therefore that’s what these things are and that these are integer and so therefore it’s a number of authors in the paper,” like all of that, that’s work for human to do, and the AI is happy to with it.
Now I don’t even know what I want to ask. fortunately, you can ask the machine, “Can you make some exploratory graphs?” And once again, this a super high-level instruction with lots of intent behind it. But I don’t even what I want. And the AI kind of has infer what I might be interested in. And so it up with some good ideas, I think. So a histogram of the number of authors per paper, series of papers per year, word cloud of the paper titles. All of that, I think, be pretty interesting to see. And the great thing is, it can do it. Here we go, a nice bell curve. You see three is kind of the most common. It’s going to then this nice plot of the papers per year. Something is happening in 2023, though. Looks like we were on an exponential and dropped off the cliff. What could be going on there? By the way, all is Python code, you can inspect. And then we’ll see word cloud. So can see all these wonderful things that appear in these titles.
But I’m unhappy about this 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 on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage papers in 2022 were even posted by April 13?] April 13 was the cut-off date I believe. Can you use to make a fair projection? So we’ll see, this is the kind of one.
(Laughter)
So you know, again, I feel like was more I wanted out of the machine here. I wanted it to notice this thing, maybe it’s a little bit an overreach for it to have sort of, inferred magically that this what 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 does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t ask that, but it know what I want.
Now we’ll cut back to slide again. This slide shows a parable of how I we … A vision of how we may end up using technology in the future. A person brought his very sick dog the vet, and the veterinarian made a bad call to say, “Let’s just and see.” And the dog would not be here today had he listened. In the meanwhile, he provided blood test, like, the full medical records, to GPT-4, said, “I am not a vet, you need to 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 not perfect. You cannot overly rely them. But this story, I think, shows that a human with a medical professional and ChatGPT as a brainstorming partner was able to achieve an outcome that not have happened otherwise. I think this is something should all reflect on, think about as we consider how to integrate these systems our world.
And one thing I believe really deeply, is that getting AI right is going to require from 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 one thing to take from this talk, it’s that this technology just looks different. Just from anything people had anticipated. And so we all have to literate. And that’s, honestly, one of the reasons we ChatGPT.
Together, I believe that we can achieve the OpenAI of ensuring that artificial 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 feeling reeling. Like, I suspect that a very large number of viewing this, you look at that and you think, “Oh my goodness, pretty every single thing about the way I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to rethink the that we do things? Yeah, I mean, it’s amazing, but it’s also really scary. let’s talk, Greg, let’s talk.
I mean, I guess my first question actually is just how hell have you done this?
(Laughter)
OpenAI has a hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with this technology that shocked world?
Greg Brockman: I mean, the truth is, we’re building on shoulders of giants, right, there’s no question. you look at the compute progress, the algorithmic progress, the progress, all of those are really industry-wide. But I think within OpenAI, made a lot of very deliberate choices from the early days. And the first was just to confront reality as it lays. And 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 things that did. And think that the most important thing has been to teams of people who are very different from each other to together harmoniously.
CA: Can we have the water, by the way, just brought here? think we’re going to need it, it’s a dry-mouth topic. But isn’t there something also just about the fact you saw something in these language models that meant that if you continue to in them and grow them, that something at some point might emerge?
GB: Yes. I think that, I mean, honestly, I think the story is pretty illustrative, right? I think that high level, deep learning, like we always knew was what we wanted to be, was a deep lab, and exactly how to do it? I think in the early days, we didn’t know. We tried a lot things, and one person was working on training a model predict the next character in Amazon reviews, and he a result where — this is a syntactic process, you expect, you know, the model will predict where 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 you if a review was positive or negative. I mean, today are just like, come on, anyone can do that. But this the first time that you saw this emergence, this sort of that emerged from this underlying syntactic process. And there knew, you’ve got to scale this thing, you’ve got to where it goes.
CA: So I think this helps the riddle that baffles everyone looking at this, because these things described as prediction machines. And yet, what we’re seeing out of them feels … it just impossible that that could come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when you more of a thing, suddenly different things emerge. It happens all the time, colonies, single ants run around, when you bring enough of them together, you get these ant colonies that 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 cultural centers and jams. Give me one moment for you when you saw just pop that just blew your mind that you just not see coming.
GB: Yeah, well, so you can try this ChatGPT, if you add 40-digit numbers —
CA: 40-digit?
GB: 40-digit numbers, model will do it, which means it’s really learned an internal for how to do it. And the really interesting thing is actually, if you have it like a 40-digit number plus a 35-digit number, it’ll often get it wrong. And so you see that it’s really learning the process, but it hasn’t fully generalized, right? It’s like you can’t memorize 40-digit addition table, that’s more atoms than there are in universe. So it had to have learned something general, but it hasn’t really fully yet learned that, Oh, I can of generalize this to adding arbitrary numbers of arbitrary lengths.
CA: what’s happened here is that you’ve allowed it to scale 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 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. to do that actually, one of the things I think is very undersung this field is sort of engineering quality. Like, we had to rebuild our entire stack. When think about building a rocket, every tolerance has to be incredibly tiny. Same is true in machine learning. have to get every single piece of the stack properly, and then you can start doing these predictions. are all these incredibly smooth scaling curves. They tell you deeply fundamental about intelligence. If you look at our GPT-4 blog post, you can see all these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, the performance on coding problems. We look at some models that are 10,000 times or 1,000 times smaller. And so there’s something about that is actually smooth scaling, even though it’s still early days.
CA: So here is, one of big fears then, that arises from this. If it’s to what’s happening here, that as you scale up, things emerge you can maybe predict in some level of confidence, but it’s capable of surprising you. Why isn’t there a huge risk of something truly terrible emerging?
GB: Well, I think all of these are questions of and scale and timing. And I think one thing people miss, too, is of the 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 incrementally. And so I think that what 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 that math problem and be like, no, no, no, machine, was the correct answer. But even summarizing a book, like, that’s a hard thing supervise. Like, how do 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 that the important thing will be that we take this step by step. And we say, OK, as we move on to book summaries, we have to supervise this task properly. We have build up a track record with these machines that they’re able to carry out our intent. And I think we’re going to have produce even better, more efficient, more reliable ways of scaling this, sort like making the machine be aligned with you.
CA: we’re going to hear later in this session, there are critics say that, you know, there’s no real understanding inside, the system going to always — we’re never going to know it’s not generating errors, that it doesn’t have common sense so forth. Is it your belief, Greg, that it is at any one moment, but that the expansion of the scale and human feedback that you talked about is basically going take it on that journey of actually getting to things like and wisdom and so forth, with a high degree of confidence. Can you sure 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 think that the OpenAI approach here has always been just like, let reality you in the face, right? It’s like this field is the field of broken promises, of these experts saying X is going to happen, Y how 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 plus one or something like that is what you need. But think that our approach has always been, you’ve got to push to limits of 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 just haven’t the fruit here.
CA: I mean, it’s quite a controversial you’ve taken, that the right way to do this is put it out there in public and then harness all this, know, instead of just your team giving feedback, the is now giving feedback. But … If, you know, bad things are going emerge, it is out there. So, you know, the original story that I heard on OpenAI you were founded as a nonprofit, well you were there as the great sort of check the big companies doing their unknown, possibly evil thing AI. And you were going to build models that of, you know, somehow held them accountable and was capable of the field down, if need be. Or at least that’s kind of I heard. And yet, what’s happened, arguably, is the opposite. your release of GPT, especially ChatGPT, sent such shockwaves the tech world that now Google and Meta and so are all 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. You know, how you, like, make the case that what you have done is here and not reckless.
GB: Yeah, we think about these questions all time. Like, seriously all the time. And I don’t think we’re always going get it right. But one thing I think has incredibly important, from the very beginning, when we were thinking about how to build general intelligence, actually have it benefit all of humanity, like, how are supposed to do that, right? And that default plan 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 how to that plan. Maybe someone else does. But for me, that was always terrifying, it didn’t feel right. And I think that this alternative approach is the only other path that I see, which is you do let reality hit you in the face. And think you do give people time to give input. You do have, before these machines perfect, before they are super powerful, that you actually the ability to see them in action. And we’ve seen from GPT-3, right? GPT-3, we really were afraid that the number one thing people were going to do it was 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 experiment for you. Suppose you’re sitting in a room, there’s a box on the table. You that in that box is something that, there’s a very chance it’s something absolutely glorious that’s going to give beautiful to your family and to everyone. But there’s actually also a one percent thing in the print there that 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 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 for an conference. I’m sitting in the 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 choose 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 it be five 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 the 500 years. My brother in the military at the time and like, he puts his life on the line a much 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 don’t think that’s quite playing the field as it truly lies. Like, if you at the whole history of computing, I really mean when I say that this is an industry-wide or even just almost a human-development- of-technology-wide shift. And the more that you sort of, don’t put the pieces that are there, right, we’re still making faster computers, we’re still improving the algorithms, all of things, they are happening. And if you don’t put them together, get an overhang, which means that if someone does, or the moment that someone manage to connect to the circuit, then you suddenly this very powerful thing, no one’s had any time to adjust, who knows kind of safety precautions you get. And so I that one thing I take away is like, even think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change what humans could do. But I actually think that if you look at capability, it’s quite smooth over time. And so the history, I think, of technology we’ve developed has been, you’ve got to do it incrementally and you’ve to figure out how to manage it for each that you’re increasing it.
CA: So what I’m hearing that you … the model you want us to have is that we birthed this extraordinary child that may have superpowers that take humanity to a whole place. It is our collective responsibility to provide the guardrails for this child to teach 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 this 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 get in this technology, figure out how to provide the feedback, decide what we want from it. And my is that that will continue to be the best path, but it’s good we’re honestly having this debate because we wouldn’t otherwise if weren’t out there.
CA: Greg Brockman, thank you so much for coming TED and blowing our minds.
(Applause)