We OpenAI seven years ago because we felt like something interesting was happening in AI and we wanted to steer it in a positive direction. It’s honestly just really amazing to see far this whole field has come since then. And it’s really gratifying to hear people like Raymond who are using the technology we building, and others, for so many wonderful things. We hear from people are excited, we hear from people who are concerned, hear from people who feel both those emotions at once. honestly, that’s how we feel. Above all, it feels 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 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 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 for human. So we have a new DALL-E model, which generates images, and we are exposing as an app for ChatGPT to use on your behalf. you can do things like ask, you know, suggest a post-TED meal and draw a picture of it.
(Laughter)
Now you get all of the, of, ideation and creative back-and-forth and taking care of the details for that you get out of ChatGPT. And here we go, it’s not just the for the meal, but a very, very detailed spread. 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 an image. And that is something that really expands power of what 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 the 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 at it.
Now we’ve extended ChatGPT with other tools too, for example, memory. can say “save this for later.” And the interesting thing about tools is they’re very inspectable. So you get this little pop here that says “use the DALL-E app.” And by the way, this coming to you, all ChatGPT 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 you of have this ability to inspect how the machine is using these tools, which allows to provide feedback to them.
Now it’s saved for later, and let me show you it’s like to use that information and to integrate other applications too. You can say, “Now make a list for the tasty thing I was suggesting earlier.” And make it a little tricky the AI. “And tweet it out for all the TED viewers out there.”
(Laughter)
So if do make this wonderful, wonderful meal, I definitely want to know how tastes.
But you can see that ChatGPT is selecting these different tools without me having to tell it explicitly which to use in any situation. And this, I think, shows a way of thinking about the user interface. Like, we are used to thinking of, well, we have these apps, we click 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 to polite.
(Laughter)
And by having this unified language interface on top of tools, AI is able to sort of take away all those from you. So you don’t have to be the one who spells every single sort of little piece of what’s supposed 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 shopping list while we’re at it. And you can see we a list of ingredients to Instacart. Here’s everything you need. And the thing that’s really interesting that the traditional UI is still very valuable, right? If you look at this, you can click through it and sort of modify the actual quantities. And that’s something that I think shows they’re not going away, traditional UIs. It’s just we have a new, augmented way to build them. now we have a tweet that’s been drafted for our review, is also a very important thing. We can click “run,” and 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 this talk, you 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 important thing how we build this, it’s not just about building these tools. It’s about teaching the how to use them. Like, what do we even want it to do we ask these very high-level questions? And to do this, we 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 machine, like a human child, and then teach it feedback. Have a human teacher who provides rewards and punishments as tries things out and does things that are either good or bad.
And this is how we train ChatGPT. It’s a two-step process. First, we produce Turing would have called a child machine through an unsupervised learning process. just show it the whole world, the whole internet and say, “Predict what comes in text you’ve never seen before.” And this process imbues it with all sorts of skills. For example, if you’re shown a math problem, the only way actually complete that math problem, to say 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 AI what to do with those skills. And for this, we provide feedback. We have the try out multiple things, give us multiple suggestions, and a human rates them, says “This one’s better than that one.” this reinforces not just the specific thing that the AI said, very importantly, the whole process that the AI used to that answer. And this allows it to generalize. It allows 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, the things we have to teach the AI are what you’d expect. For example, when we first showed GPT-4 Khan Academy, they said, “Wow, this is so great, We’re going to be to teach students wonderful things. Only one 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 and run with it.” So had to collect some feedback data. Sal Khan himself was very kind and offered 20 hours of his time to provide feedback to the machine alongside our team. And over the course a couple of months we were able to teach the AI that, “Hey, really should push back on humans in this specific kind of scenario.” And we’ve actually made lots lots of improvements to the models this way. And when you push that thumbs down ChatGPT, that actually is kind of like sending up bat signal to our team to say, “Here’s an area weakness where you should gather feedback.” And so when you do that, that’s one way that really listen to our users and make sure we’re something that’s more useful for everyone.
Now, providing high-quality feedback a hard thing. If you think about asking a 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 have to scale our ability to provide high-quality feedback. But this, the AI itself is happy to help. It’s happy to help us even better feedback and to scale our ability to supervise machine as time goes on. And let me show you what I mean.
For example, can ask GPT-4 a question like this, of how time passed between these two foundational blogs on unsupervised and learning from human feedback. And the model says two months passed. But is it true? Like, these are not 100-percent reliable, although they’re getting better every time we provide some feedback. But can actually use the AI to fact-check. And it can actually check its own work. can say, fact-check this for me.
Now, in this case, I’ve actually the AI a new tool. This one is a browsing where the model can issue search queries and click into pages. And it actually writes out its whole chain of thought as it does it. says, I’m just going to search for this and it actually the search. It then it finds the publication date and the search results. It then is issuing another query. It’s going to click into the blog post. all of this you could do, but it’s a very task. It’s not a thing that humans really want do. It’s much more fun to be in the driver’s seat, to be in manager’s position where you can, if you want, triple-check the work. And out come citations so you actually go and very easily verify any piece of this chain of reasoning. And it actually 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 about this whole process is that it’s this many-step between a human and an AI. Because a human, using fact-checking tool is doing it in order to produce data for another to become more useful to a human. And I think this really shows the shape something that we should expect to be much more common in the future, we have humans and machines kind of very carefully and designed in how they fit into a problem and we want to solve that problem. We make sure that the humans are providing the management, the oversight, feedback, and the machines are operating in a way that’s and trustworthy. And together we’re able to actually create more trustworthy machines. And I think that over time, we get this process right, we will be able to solve impossible problems.
And give you a sense of just how 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 some form since, we’ll say, 40 years ago with VisiCalc. I don’t think they’ve really that much in that time. And here is a spreadsheet of all the AI papers on the arXiv the past 30 years. There’s about 167,000 of them. 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 able to code, just like a data scientist would. And so you can just upload a file and ask 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 it you.” The only information here is the name of the file, the column names you saw and 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 its world knowledge of knowing that, “Oh yeah, arXiv a site that people submit papers and therefore that’s what these things are and these are integer values and so therefore it’s a number authors in the paper,” like all of that, that’s work a human to do, and the AI is happy help with 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 behind it. But I don’t even know what I want. And AI kind of has to infer what I might be interested in. And so it comes with some good ideas, I think. So a histogram of the of authors per paper, time series of papers per year, word cloud of paper titles. All of that, I think, will be interesting to see. And the great thing is, it can actually it. Here we go, a nice bell curve. You see that three is kind of most common. It’s going to then make this nice plot the papers per year. Something crazy is happening in 2023, though. Looks like we were on an and it dropped off the cliff. What could be going there? By the way, all this is Python code, you inspect. And then we’ll see word cloud. So you see all these wonderful things that appear in these titles.
But I’m pretty unhappy this 2023 thing. It makes this year look really bad. course, the problem is that the year is not over. So I’m going push back on the machine. [Waitttt that’s not fair!!! 2023 isn’t over. What percentage of papers in 2022 were even posted April 13?] So April 13 was the cut-off date I believe. Can use that to make a fair projection? So we’ll see, this is the of ambitious one.
(Laughter)
So you know, again, I feel like there was more I out of the machine here. I really wanted it notice this thing, maybe it’s a little bit of overreach for it to have sort of, inferred magically this is what I wanted. But I inject my intent, I provide this additional piece of, know, guidance. And under the hood, the AI is just writing code again, so if want to inspect what it’s doing, it’s very possible. And now, it does correct projection.
(Applause)
If you noticed, it even updates the title. I didn’t for that, but it know what I want.
Now we’ll cut to the slide again. This slide shows a parable of I think we … A vision of how we may end up using this technology in future. A person brought his very sick dog to the vet, and the veterinarian a bad call to say, “Let’s just wait and see.” And 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 a second vet who used it save the dog’s life. Now, these systems, they’re not perfect. cannot overly rely on them. But this story, I think, shows that a with a medical professional and with ChatGPT as a brainstorming partner able to achieve an outcome that would 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, that getting AI right is going to require participation from everyone. And that’s for deciding we want it to slot 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 this just looks different. Just different from anything people had anticipated. And 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 all humanity.
Thank you.
(Applause)
(Applause ends)
Chris Anderson: Greg. Wow. I mean … suspect that within every mind out here there’s a feeling reeling. Like, I suspect that a very large number of people viewing this, look at that and you think, “Oh my goodness, pretty much every single thing about the I work, I need to rethink.” Like, there’s just new there. Am I right? Who thinks that they’re having to 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 just the hell have you done this?
(Laughter)
OpenAI has few hundred employees. Google has thousands of employees working on intelligence. Why is it you who’s come up with technology that shocked the world?
Greg Brockman: I mean, the truth is, we’re all building on of giants, right, there’s no question. If you look the compute progress, the algorithmic progress, the data progress, all those are really industry-wide. But I think within OpenAI, we made a lot very deliberate choices from the early days. And the first was just to confront reality as it lays. And that just thought really hard about like: What is it going to take make progress here? We tried a lot of things that didn’t work, so you only see the things did. And I think that the most important thing has to get teams of people who are very different from each to work together harmoniously.
CA: Can we have the water, the way, just brought here? I think we’re going to need it, it’s dry-mouth topic. But isn’t there something also just about fact that you saw something in these language models meant that if you continue to invest in them and grow them, that something at some point emerge?
GB: Yes. And I think that, I mean, honestly, I think the story there is pretty illustrative, right? think that high 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 that in the early days, we didn’t know. We a lot of things, and one person was working training a model to predict the next character in Amazon reviews, and he a result where — this is a syntactic process, expect, you know, the model will predict where the commas go, the nouns and verbs are. But he actually got a state-of-the-art sentiment analysis classifier out of it. This could tell you if a review was positive or negative. I mean, today we are just like, come on, can do that. But this was the first time you saw this emergence, this sort of semantics that emerged from underlying syntactic process. And there we knew, you’ve got scale this thing, you’ve got to see where it goes.
CA: I think this helps explain the riddle that baffles looking at this, because these things are described as prediction machines. yet, what we’re seeing out of them feels … it just feels impossible that that come from a prediction machine. Just the stuff you showed just now. And the key idea of emergence is that when get more of a thing, suddenly different things emerge. It all the time, ant colonies, single ants run around, when you bring enough of them together, you get ant colonies that show completely emergent, different behavior. Or city where a few houses together, it’s just houses together. But as grow the number of houses, things emerge, like suburbs cultural centers and traffic jams. Give me one moment you when you saw just something pop that just blew your mind that you did not see coming.
GB: Yeah, well, so you can try in 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 circuit for how to do it. And the really interesting thing is actually, if you have it add a 40-digit number plus a 35-digit number, it’ll often get wrong. And so you can see that it’s really learning process, but it hasn’t fully generalized, right? It’s like can’t memorize the 40-digit addition table, that’s more atoms than are in the universe. So it had to have learned something general, but that it hasn’t fully yet learned that, Oh, I can sort of this to adding arbitrary numbers of arbitrary lengths.
CA: So what’s here is that you’ve allowed it to scale up and look at an incredible number of of text. And 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. one science that 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. When you think building a rocket, every tolerance has to be incredibly tiny. is true in machine learning. You have to get every single piece of the stack engineered properly, and you can start doing these predictions. There are all these incredibly scaling curves. They tell you something deeply fundamental about intelligence. If you look at GPT-4 blog post, you can see all of these curves in there. And now we’re starting to able to predict. So we were able to predict, for example, performance on coding problems. We basically look at some models that 10,000 times or 1,000 times smaller. And so there’s something about this that is actually scaling, even though it’s still early days.
CA: So 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 predict some level of confidence, but it’s capable of surprising you. Why isn’t there just a huge of something truly terrible emerging?
GB: Well, I think all of these are questions of degree and scale timing. And I think one thing people miss, too, is sort of the integration with the is also this incredibly emergent, sort of, very powerful thing too. And so that’s one the reasons that we think it’s so important to deploy incrementally. And so I that what we kind of see right now, if you look at this talk, a of what I focus on is providing really high-quality feedback. Today, tasks that we do, you can inspect them, right? It’s very easy to look at that math and be like, no, no, no, machine, seven was the correct answer. But even summarizing book, like, that’s a hard thing to supervise. Like, how do you know if this book is any good? You have to read the whole book. No one wants to do that.
(Laughter) so I think that the important thing will be that we take this by step. And that we say, OK, as we move on to summaries, we have to supervise this task properly. We to build up a track record with these machines that they’re able to actually out our intent. And I think we’re going to have to 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 later in this session, there are critics who 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 and so forth. Is it your belief, Greg, that it is true at any one moment, that the expansion of the scale and the human feedback that you talked is basically going to take it on that journey of actually to things like truth and wisdom and so forth, with a degree of confidence. Can you be sure of that?
GB: Yeah, well, I that the OpenAI, I mean, the short answer is yes, I that is where we’re headed. And I think that the approach here has always been just like, let reality you in the face, right? It’s like this field is the field of promises, of all these experts saying X is going to happen, Y how it works. People have been saying neural nets aren’t going work for 70 years. They haven’t been right yet. might be right maybe 70 years plus one or something like is 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 that tells then, oh, here’s how we can move on to a paradigm. And we just haven’t exhausted the fruit here.
CA: mean, it’s quite a controversial stance you’ve taken, that the right to do this is to put it out there in public and harness all this, you know, instead of just your team giving feedback, world is now giving feedback. But … If, you know, bad are going to emerge, it is out there. So, you know, the story that I heard on OpenAI when you were founded a nonprofit, well you were there as the great sort of check on big companies doing their unknown, possibly evil thing with AI. And were going to build models that sort of, you know, held them accountable and was capable of slowing the field down, if need be. at least that’s kind of what I heard. And yet, what’s happened, arguably, is the opposite. That your release of GPT, ChatGPT, sent such shockwaves through the tech world that now Google Meta and so forth are all scrambling to catch up. And some of their criticisms have been, are forcing us to put this out here without proper or we die. You know, how do you, like, the case that what you have done is responsible here and not reckless.
GB: Yeah, think about these questions all the time. Like, seriously the time. And I don’t think we’re always going 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, are you supposed to do that, right? And that plan of being, well, you build in secret, you this super powerful thing, and then you figure out the safety it and then 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 always terrifying, it didn’t feel right. And so I think that this alternative approach is only other path that I see, which is that do let reality hit you in the face. And I you do give people time to give input. You have, before these machines are perfect, before they are powerful, that you actually have the ability to see them in action. And we’ve it from GPT-3, right? GPT-3, we really were afraid the number one thing people were going to do it was generate misinformation, try to tip elections. Instead, the number one thing generating 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 glorious that’s going to give beautiful gifts to your and to everyone. But there’s actually also a one percent thing in small print there that says: “Pandora.” And there’s a chance this actually could unleash unimaginable evils on the world. Do you open box?
GB: Well, so, absolutely not. I think you don’t do that way. And honestly, like, I’ll tell you a story that haven’t actually told before, which is that shortly after we started OpenAI, remember I was in Puerto Rico for an AI conference. I’m sitting in the hotel just looking out over this wonderful water, all these having a good time. And you think about it for a moment, if could choose for basically that Pandora’s box to be five years away 500 years away, which would you pick, right? On the hand you’re like, well, maybe for you personally, it’s better to have be five years away. But if it gets to 500 years away and people get more time to get right, which do you pick? And you know, I just really it in the moment. I was like, of course you do the 500 years. My was in the military at the time and like, puts his life on the line in a much more real way any of us typing things in computers and developing technology at the time. And so, yeah, I’m really sold the you’ve got to approach this right. But I don’t think that’s playing the field as it truly lies. Like, if you look the whole history of computing, I really mean it when I that this is an industry-wide or even just almost like a human-development- of-technology-wide shift. And the that you sort of, don’t put together the pieces are there, right, we’re still making faster computers, we’re improving 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 someone does manage connect to the circuit, then you suddenly have this very powerful thing, no one’s any time to adjust, who knows what kind of precautions you get. And so I think that one thing I take away is like, you think about development of other sort of technologies, think about weapons, people talk about being like a zero to one, sort of, change in 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, every technology we’ve developed has been, you’ve got to do it incrementally you’ve got to figure out how to manage it for each moment you’re increasing it.
CA: So what I’m hearing is you … the model you want us to have that we have birthed this extraordinary child that may have 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. Is that basically the model?
GB: think it’s true. And I think it’s also important to this may shift, right? We’ve got to take each step as we it. And I think it’s incredibly important today that we all do get literate in technology, figure out how to provide the feedback, decide what we want from it. And 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 it weren’t out there.
CA: Greg Brockman, thank so much for coming to TED and blowing our minds.
(Applause)