Jim Keller: Moore’s Law, Microprocessors, Abstractions, and First Principles

Jim Keller is a legendary microprocessor engineer, having worked at AMD, Apple, Tesla, and now Intel. He’s known for his work on the AMD K7, K8, K12 and Zen microarchitectures, Apple A4, A5 processors, and co-author of the specifications for the x86-64 instruction set and HyperTransport interconnect. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584, follow on Spotify https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL, or support it on Patreon https://www.patreon.com/lexfridman. This episode is presented by Cash App. Download it (App Store https://apps.apple.com/us/app/cash-app/id711923939, Google Play https://play.google.com/store/apps/details?id=com.squareup.cash&hl=en_US), use code “LexPodcast”. Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 – Introduction 02:12 – Difference between a computer and a human brain 03:43 – Computer abstraction layers and parallelism 17:53 – If you run a program multiple times, do you always get the same answer? 20:43 – Building computers and teams of people 22:41 – Start from scratch every 5 years 30:05 – Moore’s law is not dead 55:47 – Is superintelligence the next layer of abstraction? 1:00:02 – Is the universe a computer? 1:03:00 – Ray Kurzweil and exponential improvement in technology 1:04:33 – Elon Musk and Tesla Autopilot 1:20:51 – Lessons from working with Elon Musk 1:28:33 – Existential threats from AI 1:32:38 – Happiness and the meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Jim Keller, legendary microprocessor engineer who has worked at AMD, Apple, Tesla, and now Intel. He's known for his work on AMD K7, K8, K12, and Zen microarchitectures, Apple A4 and A5 processors, and co-author of the specification for the x8664 instruction set and Hypertransport Interconnect. He's a brilliant first principles engineer, an out-of-the-box thinker, and just an interesting and fun human being to talk to. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcasts, follow on Spotify, support it on Patreon, or simply connect with me on Twitter,

• 0:45Lex Fridman?

  at LexFriedman, spelled F-R-I-D-M-A-N. I recently started doing ads at the end of the introduction. I'll do one or two minutes after introducing the episode and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the App Store. I personally use Cash App to send money to friends, but you can also use it to buy, sell, and deposit Bitcoin in just seconds. Cash App also has a new investing feature. You can buy fractions of a stock, say $1 worth, no matter what the stock price is. Brokerage services are provided by Cash App Investing, a subsidiary of Square and member SIPC.

• 1:29Lex Fridman?

  I'm excited to be working with Cash App to support one of my favorite organizations called FIRST, best known for their first robotics and Lego competitions. They educate and inspire hundreds of thousands of students in over 110 countries and have a perfect rating at Charity Navigator, which means that donated money is used to maximum effectiveness. When you get Cash App from the App Store or Google Play and use code LEXPODCAST, you'll get $10 and Cash App will also donate $10 to FIRST, which again is an organization that I've personally seen inspire girls and boys to dream of engineering a better world. And now here's my conversation with Jim Keller.

• 2:29Lex Fridman?

  What are the differences and similarities between the human brain and a computer with the microprocessor as core? Let's start with a philosophical question, perhaps.

• 2:39Jim Keller?

  Well, since people don't actually understand how human brains work, I think that's true.

• 2:46Lex Fridman?

  I think that's true.

• 2:47Jim Keller?

  So it's hard to compare them. Computers are, you know, there's really two things. There's memory and there's computation, right? And to date, almost all computer architectures are global memory, which is a thing, right? And then computation, where you pull data in, you do relatively simple operations on it and write data back.

• 3:10Lex Fridman?

  So it's decoupled in modern, in modern computers? And you're, you're thinking the human brain, everything's a mesh, a mess that's combined together?

• 3:19Jim Keller?

  What people observe is there's, you know, some number of layers of neurons, which have local and global connections, and information is stored in some distributed fashion. And people build things called neural networks in computers, where the information is distributed in some kind of fashion. You know, there's a mathematics behind it. I don't know that the understanding of that is super deep. The computations we run on those are straightforward computations. I don't believe anybody has said a neuron does this computation. So to date, it's hard to compare them, I would say.

• 4:01Lex Fridman?

  So let's get into the basics before we zoom back out. How do you build a computer from scratch? What is a microprocessor? What is a microarchitecture? What's an instruction set architecture? Maybe even as far back as what is a transistor?

• 4:17Jim Keller?

  So the special charm of computer engineering is there's a relatively good understanding of abstraction layers. So down at the bottom, you have atoms, and atoms get put together in materials like silicon or dope silicon or metal, and we build transistors. On top of that, we build logic gates, right? And then functional units, like an adder, a subtractor, an instruction parsing unit, and then we assemble those into, you know, processing elements. Modern computers are built out of, you know, probably 10 to 20 locally, you know, organic processing elements or coherent processing elements, and then that runs computer programs,

• 5:02Jim Keller?

  right? So there's abstraction layers, and then software, you know, there's an instruction set you run, and then there's assembly language, C, C++, Java, JavaScript, you know, there's abstraction layers, you know, essentially from the atom to the data center, right? So when you, when you build a computer, you know, first there's a target, like what's it for? Like, how fast does it have to be? Which, you know, today, there's a whole bunch of metrics about what that is. And then, in an organization of, you know, 1,000 people who build a computer, there's lots of different disciplines that you have to operate on. Does that make sense? And so...

• 5:43Lex Fridman?

  So there's a bunch of levels of abstraction of, in an organization, I can tell, and in your own vision, there's a lot of brilliance that comes in at every one of those layers. Some of it is science, some of it is engineering, some of it is art. What's the most, if you could pick favorites, what's the most important, your favorite layer on these layers of abstractions? Where does the magic enter this hierarchy?

• 6:10Jim Keller?

  I don't really care. That's the fun, you know, I'm somewhat agnostic to that. So I would say...

• 6:20Jim Keller?

  For relatively long periods of time, instruction sets are stable. So the x86 instruction set, the ARM instruction set. What's an instruction set? So it says, how do you encode the basic operations? Load, store, multiply, add, subtract, conditional branch. You know, there aren't that many interesting instructions. If you look at a program and it runs, you know, 90% of the execution is on 25 op codes. You know, 25 instructions and those are stable. Right? What does it mean stable? Intel architecture has been around for 25 years. It works. It works. And that's because the basics, you know, are defined a long time ago. Right?

• 7:02Jim Keller?

  Now, the way an old computer ran is you fetched instructions and you executed them in order. Do the load, do the add, do the compare. The way a modern computer works is you fetch large numbers of instructions, say 500. And then you find the dependency graph between the instructions. And then you execute in independent units, those little micrographs. So a modern computer, like people like to say, computers should be simple and clean. But it turns out the market for a simple, complete, clean, slow computers is zero. Right? We don't sell any simple, clean computers.

• 7:46Jim Keller?

  Now, you can, there's, how you build it can be clean, but the computer people want to buy, that's say, you know, phone or data center, fetches a large number of instructions, computes the dependency graph and then executes it in a way that gets the right answers. And optimize that graph somehow. Yeah. They run deeply out of order and then there's semantics around how memory ordering works and other things work. So the computer sort of has a bunch of bookkeeping tables. It says, what order should these operations finish in or appear to finish in? But to go fast, you have to fetch a lot of instructions and find all the parallelism. Now, there's a second kind of computer.

• 8:32Jim Keller?

  Which we call GPUs today. And I call it the difference. There's found parallelism, like you have a program with a lot of dependent instructions. You fetch a bunch and then you go figure out the dependency graph and you issue instructions out of order. That's because you have one serial narrative to execute, which in fact is in, can be done out of order.

• 8:52Lex Fridman?

  You call it a narrative?

• 8:53Jim Keller?

  Yeah.

• 8:54Lex Fridman?

  Wow.

• 8:55Jim Keller?

  So, yeah, so humans think in serial narrative. So read, read a book, right? There's a, you know, there's a sentence after sentence after sentence and there's paragraphs. Now you could diagram that. Imagine you diagrammed it properly and you said, which sentences could be read in any order, any order without changing the meaning, right?

• 9:16Lex Fridman?

  That's a fascinating question to ask of a book. Yeah.

• 9:19Jim Keller?

  Yeah, you could do that, right? So some paragraphs could be reordered, some sentences can be reordered. You could say, he is tall and smart and X, right? And it could, it doesn't matter the order of tall and smart. But if you say the tall man is wearing a red shirt, what colors, you know, like you can create dependencies, right? Right? And so GPUs, on the other hand, run simple programs on pixels. But you're given a million of them and the first order, the, the screen you're looking at doesn't care which order you do it in. So I call that given parallelism.

• 10:01Jim Keller?

  Simple narratives around the large numbers of things where you can just say it's parallel because you told me it was.

• 10:09Lex Fridman?

  So found parallelism where the narrative is sequential, but you discover like little pockets of parallelism versus?

• 10:18Jim Keller?

  Turns out large pockets of parallelism.

• 10:20Lex Fridman?

  Large, so how hard is it to this?

• 10:22Jim Keller?

  Well, how hard is it? That's just transistor count, right? So once you crack the problem, you say, here's how you fetch 10 instructions at a time. Here's how you calculated the dependencies between them. Here's how you describe the dependencies. Here's, you know, these are pieces, right?

• 10:38Lex Fridman?

  So, and once you describe the dependencies, then it's just a graph, sort of, it's an algorithm that finds.

• 10:48Lex Fridman?

  What is that? I'm sure there's a graph theoretical answer here that's solvable. In general, programs, modern programs that human beings write, how much found parallelism is there?

• 11:01Jim Keller?

  About 10x.

• 11:02Lex Fridman?

  What does 10x mean?

• 11:03Jim Keller?

  So if you execute it in order.

• 11:06Lex Fridman?

  Versus?

• 11:08Jim Keller?

  You would get what's called cycles per instruction and it would be about, you know, three instructions, three cycles per instruction because of the latency of the operations and stuff. And in a modern computer executes it like point two, point two, five cycles per instruction. So it's about, we today find 10x. And there's two things. One is the found parallelism in the narrative, right? And the other is the predictability of the narrative, right? So certain operations, they do a bunch of calculations and if greater than one, do this, else do that. That, that decision is predicted in modern computers to high 90% accuracy.

• 11:53Jim Keller?

  So branches happen a lot. So imagine you have, you have a decision to make every six instructions, which is about the average, right? But you want to fetch 500 instructions, figure out the graph and execute them all in parallel. That means you have, let's say, if you effect 600 instructions and it's every six, you have to fetch, you have to predict 99 out of 100 branches correctly for that window to be effective.

• 12:19Lex Fridman?

  Okay, so parallelism, you can't parallelize branches, or you can?

• 12:24Jim Keller?

  No, you can predict, you can.

• 12:26Lex Fridman?

  What does predict a branch mean?

• 12:27Jim Keller?

  So imagine you do a computation over and over, you're in a loop.

• 12:31Lex Fridman?

  Yep.

• 12:31Jim Keller?

  So while n is greater than one, do. And you go through that loop a million times. So every time you look at the branch, you say, it's probably still greater than one.

• 12:42Lex Fridman?

  And you're saying you could do that accurately?

• 12:44Jim Keller?

  Very accurately. Modern computers.

• 12:46Lex Fridman?

  My mind is blown. How the heck do you do that? Wait a minute.

• 12:49Jim Keller?

  Well, you want to know? This is really sad. 20 years ago.

• 12:53Lex Fridman?

  Yes.

• 12:54Jim Keller?

  You simply recorded which way the branch went last time and predicted the same thing.

• 12:59Lex Fridman?

  Right.

• 13:00Jim Keller?

  Okay.

• 13:01Lex Fridman?

  What's the accuracy of that?

• 13:03Jim Keller?

  85%.

• 13:05Jim Keller?

  So then somebody said, hey, let's keep a couple of bits and have a little counter. So when it predicts one way, we count up and then pins. So say you have a three bit counter, so you count up and then you count down. And if it's, you know, you can use the top bit as the sign bit. So you have a signed two bit number. So if it's greater than one, you predict taken and less than one, you predict not taken. Right? Or less than zero, whatever the thing is. And that got us to 92%.

• 13:32Lex Fridman?

  Oh.

• 13:34Jim Keller?

  Okay. No, it gets better. This branch depends on how you got there. So if you came down the code one way, you're talking about Bob and Jane. Right? And then said, does Bob like Jane? It went one way. But if you're talking about Bob and Jill, does Bob like Jane? You go a different way. Right? So that's called history. So you take the history and a counter. That's cool. But that's not how anything works today. They use something that looks a little like a neural network. So modern, you take all the execution flows. And then you do basically deep pattern recognition of how the program is executing.

• 14:16Jim Keller?

  And you do that multiple different ways. And you have something that chooses what the best result is. There's a little supercomputer inside the computer.

• 14:27Lex Fridman?

  That's trying to predict.

• 14:28Jim Keller?

  That calculates which way branches go. So the effective window that it's worth finding grass in gets bigger.

• 14:36Lex Fridman?

  Why was that gonna make me sad? Cause that's amazing.

• 14:39Jim Keller?

  It's amazingly complicated.

• 14:41Lex Fridman?

  Oh, wow.

• 14:42Jim Keller?

  Well, here's the, here's the funny thing. So to get to 85% took a thousand bits. To get to 99% takes tens of megabits. So this is one of those. To get the result, you know, to get from a window of say 50 instructions to 500, it took three orders of magnitude or four orders of magnitude more bits.

• 15:09Lex Fridman?

  Now, if you get the prediction of a branch wrong, what happens then?

• 15:13Jim Keller?

  You flush the pipe.

• 15:14Lex Fridman?

  You flush the pipe, so it's just the performance cost.

• 15:16Jim Keller?

  But it gets even better.

• 15:17Lex Fridman?

  Yeah.

• 15:18Jim Keller?

  So we're starting to look at stuff that says, so executed down this path. And then you had two ways to go, but far, far away, there's something that doesn't matter which path you went. So you missed, you took the wrong path, you executed a bunch of stuff. Then you had the mispredicting to back that up, but you remembered all the results you already calculated. Some of those are just fine. Like if you read a book and you misunderstand a paragraph, your understanding of the next paragraph sometimes is invariant to that understanding. Sometimes it depends on it.

• 15:55Lex Fridman?

  And you can kind of anticipate that invariance.

• 16:00Jim Keller?

  Yeah, well, you can keep track of whether the data changed. And so when you come back through a piece of code, should you calculate it again or do the same thing?

• 16:08Lex Fridman?

  Okay, how much of this is art and how much of it is science? Cause it sounds pretty complicated, so.

• 16:15Jim Keller?

  Well, how do you describe a situation? So imagine you come to a point in the road where you have to make a decision.

• 16:20Lex Fridman?

  Yeah.

• 16:21Jim Keller?

  Right? And you have a bunch of knowledge about which way to go, maybe you have a map. So you want to go the shortest way? Or do you want to go the fastest way? Or you want to take the nicest road? So it's, there's some set of data.

• 16:34Lex Fridman?

  Right.

• 16:34Jim Keller?

  So imagine you're doing something complicated, like building a computer. And there's hundreds of decision points, all with hundreds of possible ways to go. And the ways you pick interact in a complicated way.

• 16:49Lex Fridman?

  Right.

• 16:50Jim Keller?

  And then you have to pick the right spot.

• 16:52Lex Fridman?

  Right.

• 16:52Jim Keller?

  So that's our science, I don't know.

• 16:54Lex Fridman?

  You avoided the question, you just described the Robert Frost poem, Road Less Taken.

• 17:00Jim Keller?

  Described the Robert Frost problem? That's what we do as computer designers, it's all poetry.

• 17:07Lex Fridman?

  Okay.

• 17:07Jim Keller?

  Great. Yeah, I don't know how to describe that because some people are very good at making those intuitive leaps. It seems like this combinations of things. Some people are less good at it, but they're really good at evaluating the alternatives, right? And everybody has a different way to do it. And some people can't make those leaps, but they're really good at analyzing it. So when you see computers are designed by teams of people who have very different skill sets. And a good team has lots of different kinds of people. And I suspect you would describe some of them as artistic, but not very many.

• 17:47Lex Fridman?

  Unfortunately, or fortunately.

• 17:48Jim Keller?

  Fortunately. Well, you know, computer design is hard. It's 99% perspiration.

• 17:57Lex Fridman?

  And

• 17:57Jim Keller?

  The 1% inspiration is really important.

• 18:00Lex Fridman?

  But you still need the 99%.

• 18:02Jim Keller?

  Yeah, you gotta do a lot of work. And then there's, there are interesting things to do at every level of that stack, so.

• 18:10Lex Fridman?

  At the end of the day, if you run the same program multiple times, does it always produce the same result? Is, is, is there some room for fuzziness there?

• 18:21Jim Keller?

  That's a math problem. So if you run a correct C program, the definition is every time you run it, you get the same answer.

• 18:29Lex Fridman?

  Yeah, that, well, that's a math statement.

• 18:31Jim Keller?

  Well, that's a, that's a language definitional statement. So

• 18:34Lex Fridman?

  Yes.

• 18:35Jim Keller?

  For years, when people did, when we first did 3D acceleration of graphics, you could run the same scene multiple times and get different answers.

• 18:45Lex Fridman?

  Right.

• 18:46Jim Keller?

  Right? And then some people thought that was okay, and some people thought it was a bad idea. And then when the HBC world used GPUs for calculations, they thought it was a really bad idea. Okay. Now, in modern AI stuff, people are looking at

• 19:04Jim Keller?

  Networks where the precision of the data is low enough that the data is somewhat noisy. And the observation is the input data is unbelievably noisy. So why should the calculation be not noisy? And people have experimented with algorithms that say can get faster answers by being noisy. Like as a network starts to converge. If you look at the computation graph, it starts out really wide and then it gets narrower. And you can say, is that last little bit that important? Or should I start the graph on the next wrap rev before we whittle it all the way down to the answer? Right? So you can create algorithms that are noisy. Now, if you're developing something and every time you run it, you get a different answer. It's really annoying.

• 19:46Jim Keller?

  And so most people think even today, every time you run the program, you get the same answer.

• 19:53Lex Fridman?

  No, I know, but the question is, that's the formal definition of a programming language.

• 19:59Jim Keller?

  There is a definition of languages that don't get the same answer, but people who use those. You always want something because you get a bad answer. And then you're wondering, is it because of something in the algorithm or because of this? And so everybody wants a little switch that says, no matter what, do it deterministically. And it's really weird because almost everything going into modern calculations is noisy. So why the answers have to be so clear?

• 20:24Lex Fridman?

  It's Right, so where do you stand?

• 20:26Jim Keller?

  I design computers for people who run programs. So if somebody says, I want a deterministic answer, like most people want that.

• 20:35Lex Fridman?

  Can you deliver a deterministic answer? I guess is the question, like when you

• 20:39Jim Keller?

  Yeah, hopefully, sure. What people don't realize is you get a deterministic answer, even though the execution flow is very undeterministic. So you run this program 100 times, it never runs the same way twice, ever.

• 20:52Lex Fridman?

  And the answer, it arrives at the same answer?

• 20:54Jim Keller?

  But it gets the same answer every time.

• 20:56Lex Fridman?

  It's just, it's just amazing. Okay. You've achieved in the eyes of

• 21:04Lex Fridman?

  Many people, legend status as a chip architect. What design creation are you most proud of? Perhaps because it was challenging, because of its impact, or because of the set of brilliant ideas that were involved in bringing it to life?

• 21:23Jim Keller?

  I find that description odd, and I have two small children, and I promise you, they think it's hilarious.

• 21:32Lex Fridman?

  This question?

• 21:33Jim Keller?

  Yeah.

• 21:34Lex Fridman?

  I do it for them.

• 21:35Jim Keller?

  So I'm really interested in building computers, and I've worked with really, really smart people. I'm not unbelievably smart. I'm fascinated by how they go together, both as a thing to do and as an endeavor that people do.

• 21:54Lex Fridman?

  How people and computers go together?

• 21:56Jim Keller?

  Yeah, like how people think and build a computer. And I find sometimes that the best computer architects aren't that interested in people, or the best people managers aren't that good at designing computers.

• 22:09Lex Fridman?

  So the whole stack of human beings is fascinating. So the managers, individual engineers.

• 22:15Jim Keller?

  Yeah, so, yeah, I said, I realize after a lot of years of building computers, where you sort of build them out of transistors, logic gates, functional units, computational elements, that you could think of people the same way. So people are functional units.

• 22:29Lex Fridman?

  Yes.

• 22:30Jim Keller?

  And then you could think of organizational design as a computer architectural problem. And then it's like, oh, that's super cool, because the people are all different, just like the computational elements are all different. And they like to do different things. And so I had a lot of fun, like reframing how I think about organizations.

• 22:48Lex Fridman?

  Just like with computers, we were saying execution paths, you can have a lot of different paths that end up at the same good destination. So what have you learned about the human abstractions from individual functional human units to the broader organization? What does it take to create something special?

• 23:11Jim Keller?

  Well, most people don't think simple enough.

• 23:17Jim Keller?

  All right. So do you know the difference between a recipe and the understanding?

• 23:23Jim Keller?

  There's probably a philosophical description of this. So imagine you're gonna make a loaf of bread.

• 23:27Lex Fridman?

  Yep.

• 23:28Jim Keller?

  The recipe says, get some flour, add some water, add some yeast, mix it up, let it rise, put it in a pan, put it in the oven. It's a recipe. Right. Understanding bread, you can understand biology, supply chains, you know, grain grinders, yeast, physics, you know, thermodynamics, like, there are so many levels of understanding there. And then when people build and design things, they frequently are executing some stack of recipes. Right. And the problem with that is the recipes all have limited scope. Like, if you have a really good recipe book for making bread, it won't tell you anything about how to make an omelet.

• 24:11Jim Keller?

  Right. But if you have a deep understanding of cooking, right, then bread, omelets, you know, sandwich, you know, there's, there's a different, you know, way of viewing everything. And most people, when you get to be an expert at something, you know, you're, you're hoping to achieve deeper understanding, not just a large set of recipes to go execute. And it's interesting to walk groups of people because executing recipes is unbelievably efficient. If it's what you want to do.

• 24:47Jim Keller?

  If it's not what you want to do, you're really stuck. And, and that difference is crucial. And, and everybody has a balance of, let's say, deeper understanding of recipes. And some people are really good at recognizing when the problem is to understand something deeply, deeply. Does that make sense?

• 25:05Lex Fridman?

  It totally makes sense. Does every stage of development, deep understanding on the team needed?

• 25:12Jim Keller?

  Well, this goes back to the art versus science question.

• 25:15Lex Fridman?

  Sure.

• 25:16Jim Keller?

  If you constantly unpacked everything for deeper understanding, you'd never get anything done.

• 25:20Lex Fridman?

  Right.

• 25:21Jim Keller?

  And if you don't unpack understanding when you need to, you'll do the wrong thing. And then at every juncture, like, human beings are these really weird things because everything you tell them has a million possible outputs. Right. And then they all interact in a hilarious way.

• 25:37Lex Fridman?

  Yeah.

• 25:38Jim Keller?

  And then having some intuition about what do you tell them, what do you do, when do you intervene, when do you not, it's, it's complicated.

• 25:45Lex Fridman?

  Right, so.

• 25:46Jim Keller?

  It's, it's, you know, essentially computationally unsolvable.

• 25:50Lex Fridman?

  Yeah, it's an interactive problem, sure. Humans are a mess. But with deep understanding, do you mean also sort of fundamental questions of things like, what is a computer? Like, or why? Like, the why question is why are we even building this? Like, of purpose? Or do you mean more like going towards the fundamental limits of physics? Sort of really getting into the core of the science?

• 26:24Jim Keller?

  Well, in terms of building a computer, think simple, think a little simpler. So, common practice is you build a computer. And then when somebody says, I want to make it 10% faster. You'll go in and say, all right, I need to make this buffer bigger. And maybe I'll add an add unit. Or, you know, I have this thing that's three instructions wide. I'm going to make it four instructions wide. And what you see is each piece gets incrementally more complicated. Right? And then at some point, you hit this limit. Like, adding another feature or buffer doesn't seem to make it any faster. And then people will say, well, that's because it's a fundamental limit. And then somebody else will look at it and say, well, actually, the way you divided the problem up and the way that different features are interacting is limiting you.

• 27:09Jim Keller?

  And it has to be rethought, rewritten. Right? So then you refactor it and rewrite it. And what people commonly find is the rewrite is not only faster, but half as complicated.

• 27:20Lex Fridman?

  From scratch?

• 27:21Jim Keller?

  Yes.

• 27:21Lex Fridman?

  So, how often in your career, but just have you seen as needed, maybe more generally, to just throw the whole out, the whole thing out?

• 27:31Jim Keller?

  This is where I'm on one end of it. Every three to five years.

• 27:36Lex Fridman?

  Which end are you on?

• 27:37Jim Keller?

  Rewrite more often.

• 27:39Lex Fridman?

  Rewrite, and three to five years is?

• 27:42Jim Keller?

  If you want to really make a lot of progress in computer architecture, every five years, you should do one from scratch.

• 27:48Lex Fridman?

  So where does the x86 64 standard come in? Or what, how often do you?

• 27:55Jim Keller?

  I wrote the, I was the co-author of that spec in 98. That's 20 years ago.

• 28:00Lex Fridman?

  Yeah, so that's still around.

• 28:02Jim Keller?

  The instruction set itself has been extended quite a few times.

• 28:06Lex Fridman?

  Yes.

• 28:06Jim Keller?

  And instruction sets are less interesting than the implementation underneath. There's been, on x86 architecture, Intel's designed a few, AIM's designed a few, very different architectures. And I don't want to go into too much of the detail about how often, but

• 28:26Jim Keller?

  there's a tendency to rewrite it every, you know, 10 years, and it really should be every five.

• 28:32Lex Fridman?

  So you're saying you're an outlier in that sense in the?

• 28:34Jim Keller?

  Rewrite more often.

• 28:35Lex Fridman?

  Rewrite more often.

• 28:36Jim Keller?

  Well, and here's?

• 28:37Lex Fridman?

  Isn't it scary?

• 28:38Jim Keller?

  Yeah, of course. Well, scary to who?

• 28:42Lex Fridman?

  To everybody involved, because like you said, repeating the recipe is efficient. Companies want to make money, well, no, individual engineers want to succeed. So you want to incrementally improve, increase the buffer from three to four.

• 28:58Jim Keller?

  Well, this is where you get into diminishing return curves. I think Steve Jobs said this, right? So every, you have a project and you start here and it goes up and you have diminishing return. And to get to the next level, you have to do a new one. And the initial starting point will be lower than the old optimization point, but it'll get higher. So now you have two kinds of fear, short-term disaster and long-term disaster.

• 29:24Lex Fridman?

  And you're, you're?

• 29:25Jim Keller?

  So grownups, right? Like, you know, people with a quarter-by-quarter business objective are terrified about changing everything.

• 29:34Lex Fridman?

  Yeah.

• 29:34Jim Keller?

  And people who are trying to run a business or build a computer for a long-term objective, know that the short-term limitations block them from the long-term success. So if you look at leaders of companies that had really good long-term success, every time they saw that they had to redo something, they did.

• 29:55Lex Fridman?

  And so somebody has to speak up?

• 29:57Jim Keller?

  Or you do multiple projects in parallel. Like you optimize the old one while you build a new one and? But the marketing guys are always like, make, promise me that the new computer is faster on every single thing. And the computer architect says, well, the new computer will be faster on the average. But there's a distribution of results and performance and you'll have some outliers that are slower. And that's very hard because they have one customer who cares about that one.

• 30:22Lex Fridman?

  So, speaking of the long term, for over 50 years now, Moore's Law has served a, for me and millions of others, as an inspiring beacon of what kind of amazing future brilliant engineers can build.

• 30:34Jim Keller?

  Yep.

• 30:36Lex Fridman?

  I'm just making your kids laugh all of today.

• 30:38Jim Keller?

  That's great.

• 30:40Lex Fridman?

  So, first, in your eyes, what is Moore's Law if you could define for people who don't know?

• 30:47Jim Keller?

  Well, the, the simple statement was, from Gordon Moore, was double the number of transistors every two years. Something like that. And then my operational model is, we increase the performance of computers by 2X every two or three years. And it's wiggled around substantially over time. And also, in how we deliver, performance has changed.

• 31:14Lex Fridman?

  Right.

• 31:14Jim Keller?

  But the foundational idea was 2X the transistors every two years. The current cadence is something like, they call it a shrink factor. Like 0.6 every two years, which is not 0.5.

• 31:28Lex Fridman?

  But that, that's referring strictly, again, to the original definition?

• 31:31Jim Keller?

  Yeah, of transistor count.

• 31:33Lex Fridman?

  A shrink factor is just getting them smaller, smaller, smaller.

• 31:35Jim Keller?

  Well, as you, for a constant chip area. If you make the transistors smaller by 0.6, then you get one over 0.6 more transistors.

• 31:44Lex Fridman?

  So, can you linger on it a little longer? What's, what's a broader, what do you think should be the broader definition of Moore's Law? When you mentioned how you think of performance, just broadly, what's a good way to think about Moore's Law?

• 31:59Jim Keller?

  Well, first of all, so I, I've been aware of Moore's Law for 30 years.

• 32:05Lex Fridman?

  In which sense?

• 32:05Jim Keller?

  Well, I've been designing computers for forty.

• 32:09Lex Fridman?

  You're just watching it before your eyes, kind of?

• 32:12Jim Keller?

  Well, and somewhere where I became aware of it, I was also informed that Moore's Law was gonna die in ten to fifteen years. And I thought that was true at first, but then after ten years, it was gonna die in ten to fifteen years. And then at one point, it was gonna die in five years, and then it went back up to ten years. And at some point, I decided not to worry about that particular prognostication for the rest of my life. Which is, which is fun. And then I joined Intel and everybody said Moore's Law is dead. And I thought, that's sad, because it's the Moore's Law company. And it's not dead, and it's always been gonna die. And, you know, humans like these apocryphal kind of statements, like, we'll run out of food, or we'll run out of air, or we'll run out of room, or we'll run out of, you know, something.

• 32:56Lex Fridman?

  Right, but it's still incredible that it's lived for as long as it has. And yes, there's many people who believe now that Moore's Law is dead.

• 33:07Jim Keller?

  You know, they can join the last fifty years of people who had the same idea.

• 33:10Lex Fridman?

  Yeah, there's a long tradition, but why do you think? If you can, in text, try to understand it, why do you think it's not dead?

• 33:19Jim Keller?

  Well, first, let's just think, people think Moore's Law is one thing, transistors get smaller. But actually, under the sheets, there's literally thousands of innovations. And almost all those innovations have their own diminishing return curves. So if you graph it, it looks like a cascade of diminishing return curves. I don't know what to call that, but the result is an exponential curve. At least it has been. So, and we keep inventing new things. So if you're an expert in one of the things on a diminishing return curve, right? And you can see its plateau, you will probably tell people, well, this is, this is done. Meanwhile, some other pile of people are, are doing something different. So that's, that's just normal.

• 34:05Jim Keller?

  So then there's the observation of how small could a switching device be? So a modern transistor is something like a thousand by thousand by thousand atoms. Right? And you get quantum effects down around two to two to 10 atoms. So you can imagine a transistor as small as 10 by 10 by 10. So that's a million times smaller. And then the quantum computational people are working away at how to use quantum effects. So.

• 34:36Lex Fridman?

  A thousand by thousand by thousand.

• 34:38Jim Keller?

  Atoms.

• 34:40Lex Fridman?

  That's a really clean way of putting it.

• 34:43Jim Keller?

  Well, a fin, like a modern transistor, if you look at the fin, it's like 120 atoms wide. But we can make that thinner. And then there's, there's a gate wrapped around it and then there's spacing. There's a whole bunch of geometry. And, you know, a competent transistor designer could count both atoms in every single direction. Like there's techniques now to already put down atoms in a single atomic layer. Right? And you can place atoms if you want to. It's just, you know, from a manufacturing process, if placing an atom takes 10 minutes and you need to put, you know, 10 to the 23rd atoms together to make a computer, it would take a long time.

• 35:25Jim Keller?

  So the, the, the methods are, you know, both shrinking things and then coming up with effective ways to control what's happening.

• 35:34Lex Fridman?

  Manufacture stably and cheaply.

• 35:36Jim Keller?

  Yeah. So, so the innovation stock's pretty broad, you know, there's, there's equipment, there's optics, there's chemistry, there's physics, there's material science, there's metallurgy. There's lots of ideas about when you put different materials together, how do they interact? Are they stable? Is it stable over temperature? You know, like are they repeatable? You know, there's, there's like literally thousands of technologies involved.

• 36:01Lex Fridman?

  But just for the shrinking, you don't think we're quite yet close to the fundamental limits of physics?

• 36:07Jim Keller?

  I did a talk on Moore's Law and I asked for a roadmap to a path of 100 and after two weeks, they said, we only got to 50.

• 36:15Lex Fridman?

  100 what, sorry?

• 36:16Jim Keller?

  100x shrink.

• 36:17Lex Fridman?

  100x shrink? We only got to 50?

• 36:19Jim Keller?

  50 and I said, why don't you give us another two weeks?

• 36:24Jim Keller?

  Well, here's the thing about Moore's Law, right? So I believe that the next 10 or 20 years of shrinking is going to happen, right? Now, as a computer designer, you have two stances. You think it's going to shrink, in which case you're designing and thinking about architecture in a way that you'll use more transistors. Or conversely, not be swamped by the complexity of all the transistors you get, right? You have to have a strategy, you know?

• 36:56Lex Fridman?

  So you're open to the possibility and waiting for the possibility of a whole new army of transistors ready to work?

• 37:02Jim Keller?

  I'm expecting

• 37:04Lex Fridman?

  Expecting?

• 37:04Jim Keller?

  More transistors every two or three years by a number large enough that how you think about design, how you think about architecture has to change. Like imagine you build buildings out of bricks and every year the bricks are half the size. Or every two years. Well, if you kept building bricks the same way, you know, so many bricks per person per day, the amount of time to build a building would go up exponentially.

• 37:32Lex Fridman?

  Right.

• 37:33Jim Keller?

  Right? But if you said, I know that's coming, so now I'm going to design equipment that moves bricks faster, uses them better, because maybe you're getting something out of the smaller bricks, more strength, thinner walls, you know, less material efficiency out of that. So once you have a roadmap with what's going to happen, transistors, they're going to get, we're going to get more of them. Then you design all this collateral around it to take advantage of it and also to cope with it. Like, that's the thing people don't understand. It's like, if I didn't believe in Moore's Law and then Moore's Law transistors showed up, my design teams were all drowned.

• 38:08Lex Fridman?

  So what's the, what's the hardest part of this inflight of new transistors? I mean, even if you just look historically throughout your career, what's the thing, what fundamentally changes when you add more transistors in the task of designing an architecture?

• 38:27Jim Keller?

  Well, there's two constants, right? One is people don't get smarter.

• 38:32Lex Fridman?

  By the way, there's some science showing that we do get smarter because of nutrition, whatever. Sorry to bring that up.

• 38:38Jim Keller?

  Flint effect.

• 38:39Lex Fridman?

  Yes.

• 38:39Jim Keller?

  Yeah, familiar with it. Nobody understands it. Nobody knows if it's still going on. So that's a...

• 38:44Lex Fridman?

  Or whether it's real or not. But yeah.

• 38:47Jim Keller?

  I sort of...

• 38:48Lex Fridman?

  Anyway, but not exponentially.

• 38:49Jim Keller?

  I would believe for the most part, people aren't getting much smarter.

• 38:52Lex Fridman?

  The evidence doesn't support it. That's right.

• 38:54Jim Keller?

  And then teams can't grow that much.

• 38:56Lex Fridman?

  Right.

• 38:57Jim Keller?

  Right, so human beings, you know, we're really good in teams of 10, you know, up to teams of 100. They can know each other. Beyond that, you have to have organizational boundaries. So you're kind of, you have, those are pretty hard constraints. Right, so then you have to divide and conquer. Like, as the designs get bigger, you have to divide it into pieces. You know, the power of abstraction layers is really high. We used to build computers out of transistors. Now, we have a team that turns transistors into logic cells and another team that turns them into functional. You know, it's another one that turns into computers. Right, so we have abstraction layers in there. And you have to think about when do you shift gears on that? We also use faster computers to build faster computers.

• 39:41Jim Keller?

  So some algorithms run twice as fast on new computers, but a lot of algorithms are n squared. So, you know, a computer with twice as many transistors and it might take four times as long to run. So you have to refactor the software. Like, simply using faster computers to build bigger computers doesn't work.

• 40:01Lex Fridman?

  So...

• 40:01Jim Keller?

  So you have to think about all these things.

• 40:03Lex Fridman?

  So in terms of computing performance and the exciting possibility that more powerful computers bring, is shrinking the thing which you've been talking about, one of the, for you, one of the biggest exciting possibilities of advancement in performance? Or is there other directions that you're interested in? Like, like in the direction of sort of enforcing given parallelism? Or like doing massive parallelism in terms of many, many CPUs?

• 40:31Lex Fridman?

  You know, stacking CPUs on top of each other, that kind of, that kind of parallelism or any kind of.

• 40:37Jim Keller?

  Well, think about it a different way. So old computers, you know, slow computers, you said A equals B plus C times D. Pretty simple, right? And then we made faster computers with vector units and you can do proper equations and matrices. Right? And then modern like AI computations are like convolutional neural networks where you convolve one large data set against another. And so there's sort of this hierarchy of mathematics, you know, from simple equation to linear equations to matrix equations to to deeper kind of computation. And the data sets are getting so big that people are thinking of data as a topology problem.

• 41:21Jim Keller?

  You know, data is organized in some immense shape. And then the computation, which sort of wants to be get data from immense shape and do some computation on it. So the, what computers have allowed people to do is have algorithms go much, much further. So that, that paper you, you referenced, the Sutton paper. They talked about, you know, like when AI started, it was apply rule sets to something. That's a very simple computational situation. And then when they did first chess thing, they, they solved deep searches. So have a huge database of moves and results, deep search, but it's still just a search.

• 42:04Jim Keller?

  Right? Now we, we take large numbers of images and we use it to train these weight sets that we convolve across. It's a completely different kind of phenomena. We call that AI. Now they're doing the next generation. And if you look at it, they're going up this mathematical graph, right? And then computations, both computation and data sets support going up that graph.

• 42:30Lex Fridman?

  Yeah, the kind of computation that might, I mean, I would argue that all of it is still a search, right? Just like you said, a topology problem of data sets, you're searching the data sets for valuable data and also the actual optimization of neural networks is a kind of search for the.

• 42:49Jim Keller?

  I don't know. If you had looked at the inner layers of finding a cat.

• 42:54Jim Keller?

  It's not a search. It's it's a set of endless projections. So you know, a projection is here's a shadow of this phone.

• 43:01Lex Fridman?

  Yeah.

• 43:01Jim Keller?

  Right. And then you can have a shadow of that on to something, a shadow on that of something. If you look in the layers, you'll see this layer actually describes pointy ears and round eye-ness and fuzziness and. But the the computation to tease out the attributes is not search.

• 43:20Lex Fridman?

  Right. I mean.

• 43:21Jim Keller?

  Like, the inference part might be search, but the training is not search.

• 43:24Lex Fridman?

  Okay, well.

• 43:24Jim Keller?

  And then in deep networks, they look at layers and they don't even know it's represented.

• 43:31Jim Keller?

  And yet, if you take the layers out, it doesn't work.

• 43:33Lex Fridman?

  Okay, so.

• 43:34Jim Keller?

  So I don't think it's search.

• 43:35Lex Fridman?

  All right, well.

• 43:36Jim Keller?

  But you'd have to talk to a mathematician about what that actually is.

• 43:39Lex Fridman?

  Well, we could disagree, but the the it's just semantics, I think it's not, but it's certainly not.

• 43:46Jim Keller?

  I would say it's absolutely not semantics, but.

• 43:48Lex Fridman?

  Okay. All right. Well, if you want to go there. So optimization, to me, is search, and we're trying to optimize the ability of a neural network to detect cat ears. And the difference between chess and the space, the incredibly multi-dimensional, hundred thousand dimensional space that you know, networks are trying to optimize over is nothing like the chessboard database. So it's a totally different kind of thing. And, okay, in that sense, you can say.

• 44:23Jim Keller?

  Yeah, yeah.

• 44:23Lex Fridman?

  It loses the meaning.

• 44:24Jim Keller?

  I can see how you you might say if if you. The funny thing is, is the difference between given search space and found search space.

• 44:33Lex Fridman?

  Right, exactly.

• 44:34Jim Keller?

  Yeah, maybe that's a different way to describe.

• 44:35Lex Fridman?

  Beautiful way to put it, okay. But you're saying, what's your sense in terms of the basic mathematical operations? And the architectures, computer hardware that enables those operations? Do you see the CPUs of today still being a really core part of executing those mathematical operations?

• 44:54Jim Keller?

  Yes. Well, the operations, you know, continue to be add, subtract, load, store, compare and branch. It's remarkable. So it's interesting that the building blocks of, you know, computers or transistors and, you know, under that atoms, you've got atoms, transistors, logic gates, computers, right? You know, functional units of computers. The building blocks of mathematics at some level are things like adds and subtracts and multiplies. But the space mathematics can describe is, I think, essentially infinite. But the computers that run the algorithms are still doing the same things.

• 45:33Jim Keller?

  Now, a given algorithm might say, I need sparse data, or I need 32-bit data, or I need, you know, like a convolution operation that naturally takes 8-bit data, multiplies it and sums it up a certain way. So the, like the data types in TensorFlow imply an optimization set. But when you go write down and look at the computers, it's an and or gates doing adds and multiplies. Like, like that hasn't changed much. Now, the quantum researchers think they're going to change that radically. And then there's people who think about analog computing because you look in the brain and it seems to be more analog-ish, you know, that maybe there's a way to do that more efficiently.

• 46:16Jim Keller?

  But we have a million X on computation. And I don't know the relationship between computational, let's say, intensity and ability to hit mathematical abstractions. I don't know any way to describe that. But, but just like you saw in AI, you went from rule sets to simple search to complex search to, say, found search. Like, those are, you know, orders of magnitude more computation to do. And as we get the next two orders of magnitude, like a friend, Raja Gadori, said, like every order of magnitude changes the computation.

• 46:57Lex Fridman?

  Fundamentally changes what the computation is doing.

• 46:59Jim Keller?

  Yeah.

• 47:01Jim Keller?

  Oh, you know, the expression, the difference in quantity is a difference in kind. You know, the difference between ant and anthill. Right. Or neuron and brain. You know, there's, there's, there's, there's indefinable place where the, the quantity changed the quality. Right. And we've seen that happen in mathematics multiple times. And, you know, my, my guess is it's going to keep happening.

• 47:25Lex Fridman?

  So in your sense is, yeah, if you focus head down and shrinking the transistor.

• 47:31Jim Keller?

  Well, it's not just head down. We're aware of the software stacks that are running and the computational loads. And we're kind of pondering, what do you do with a, a petabyte of memory that wants to be accessed in a sparse way? And have, you know, the kind of calculations AI programmers want. So there's a, there's a dialogue and interaction, but when you go in the computer chip. You know, you find addercent subtractors and multipliers and.

• 47:59Lex Fridman?

  So if you zoom out then with, as you mentioned, Ray Sutton. The idea that most of the development in the last many decades in AI research came from just leveraging computation and just the simple algorithms waiting for the computation to improve.

• 48:16Jim Keller?

  Well, software guys have a, a thing that they call the, the, the problem of early optimization.

• 48:23Lex Fridman?

  Right.

• 48:23Jim Keller?

  So you write a big software stack and if you start optimizing, like the first thing you write, the odds of that being the performance limit is low. But when you get the whole thing working, can you make it 2X faster by optimizing the right things? Sure. While you're optimizing that, could you have written a new software stack, which would have been a better choice? Maybe. Now you have creative tension. So.

• 48:47Lex Fridman?

  But the whole time, as you're doing the writing, the, that's the software we're talking about. The hardware underneath gets faster.

• 48:53Jim Keller?

  Well, this goes back to the Moore's Law. If Moore's Law is going to continue, then your AI research should expect that to show up. And then you make a slightly different set of choices, then we've hit the wall. Nothing's going to happen. And from here, it's just us rewriting algorithms. Like, that seems like a failed strategy for the last 30 years of Moore's Law's death. So.

• 49:16Lex Fridman?

  So can you just linger on it? I, I think you've answered it, but I was just asked the same dumb question over and over. So what, why do you think Moore's Law is not going to die? Which is the most promising, exciting possibility of why it won't die in the next five, 10 years? So is it the continued shrinking of the transistor? Or is it another S curve that steps in and it totally sort of?

• 49:42Jim Keller?

  Well, the shrinking of the transistor is literally thousands of innovations.

• 49:47Lex Fridman?

  Right, so there's

• 49:48Jim Keller?

  So

• 49:48Lex Fridman?

  There's

• 49:48Jim Keller?

  There's all

• 49:49Lex Fridman?

  S curves in there.

• 49:50Jim Keller?

  There's a whole bunch of S curves just kind of running their course and being reinvented and new things, you know, the semiconductor fabricators and technologists have all announced what's called nano wires. So they took a fin which had a gate around it and turned that into little wires. So you have better control of that and they're smaller. And then from there, there are some obvious steps about how to shrink that. The metallurgy around wire stacks and stuff has very obvious abilities to shrink. And, you know, there's a whole combination of things there to do.

• 50:27Lex Fridman?

  Your sense is that we're going to get a lot?

• 50:30Jim Keller?

  Yeah.

• 50:30Lex Fridman?

  If this innovation from just that shrinking?

• 50:33Jim Keller?

  Yeah, like a factor of a hundred's a lot.

• 50:36Lex Fridman?

  Yeah, I would say that's incredible. And it's totally

• 50:39Jim Keller?

  It's only 10 or 15 years.

• 50:42Lex Fridman?

  Now, you're smarter, you might know, but to me, it's totally unpredictable of what that 100x would bring in terms of the nature of the computation that people would be

• 50:51Jim Keller?

  Yeah, you're familiar with Bell's law. So for a long time, it was mainframes, minis, workstation, PC, mobile.

• 50:58Lex Fridman?

  Yeah.

• 50:59Jim Keller?

  Moore's law drove faster, smaller computers, right? And then when we were thinking about Moore's law, Roger Goodory said every 10x generates a new computation. So scalar, vector, matrix, topological computation, right? And if you go look at the industry trends, there was, you know, mainframes and then mini computers and then PCs. And then the internet took off. And then we got mobile devices. And now we're building 5G wireless with one millisecond latency. And people are starting to think about the smart world where everything knows you, recognizes you, like, like, like the transformations are going to be like unpredictable.

• 51:44Lex Fridman?

  How does it make you feel that you're one of the key architects of this kind of future? So you're not, we're not talking about the architects of the high level? People who build the Angry Bird apps and.

• 52:00Jim Keller?

  What's wrong with Angry Bird apps? Who knows?

• 52:02Lex Fridman?

  I'm gonna take a stand.

• 52:03Jim Keller?

  The whole point of the universe.

• 52:04Lex Fridman?

  I'm gonna take a stand at that and the attention distracting nature of mobile phones. I'll take a stand. But anyway, in terms of.

• 52:12Jim Keller?

  I don't think that matters much.

• 52:14Lex Fridman?

  The, the, the side effects of smartphones or the attention distraction, which part?

• 52:20Jim Keller?

  Well, who knows, you know, where this is all leading. It's changing so fast.

• 52:24Lex Fridman?

  Wait, so back to.

• 52:25Jim Keller?

  My parents used to yell at my sisters for hiding in the closet with a wired phone with a dial on it. Stop talking to your friends all day.

• 52:31Lex Fridman?

  Right.

• 52:32Jim Keller?

  Now my wife yells at my kids for talking to their friends all day on text. It looks the same to me.

• 52:38Lex Fridman?

  It's always, it's echoes of the same thing. Okay, but you are the, one of the key people architecting the hardware of this future. How's that make you feel? Do you feel responsible?

• 52:50Lex Fridman?

  Do you feel excited?

• 52:52Jim Keller?

  So we're, we're in a social context. So there's billions of people on this planet. There are literally millions of people working on technology. I feel lucky to, to be, you know, what, doing what I do and getting paid for it. And there's an interest in it, but there's so many things going on in parallel. It's like the actions are so unpredictable. If I wasn't here, somebody else would do it. The, the vectors of all these different things are happening all the time. You know, there's a, I'm sure some philosopher or meta philosophers, you know, wondering about how we transform our world.

• 53:33Lex Fridman?

  So you can't deny the fact that these tools, whether, that these tools are changing our world.

• 53:41Jim Keller?

  That's right.

• 53:42Lex Fridman?

  So do you think it's changing for the better?

• 53:45Jim Keller?

  Somebody, I, I read this thing recently. It said the, the two disciplines with the highest GRE scores in, in college are physics and philosophy. Right? And they're both sort of trying to answer the question, why is there anything? Right? And the philosophers, you know, are on the kind of theological side and the physicists are obviously on the, you know, the material side. And there's a hundred billion galaxies with a hundred billion stars. It seems, well, repetitive at best.

• 54:17Jim Keller?

  So I, you know, there's, there's on our way to 10 billion people. I mean, it's hard to say what it's all for, if that's what you're asking.

• 54:26Lex Fridman?

  Yeah, I guess, I guess I am.

• 54:28Jim Keller?

  Things do tend to significantly increases in complexity. And I'm, I'm curious about how computation, like, like our world, our physical world inherently generates mathematics. It's kind of obvious, right? So we have X, Y, Z coordinates. You take a sphere, you make it bigger, you get a surface that falls, you know, grows by R squared. Like, it generally generates mathematics and the mathematicians and the physicists have been having a lot of fun talking to each other for years. And computation has been, let's say relatively pedestrian. Like computation in terms of mathematics has been doing binary, binary algebra.

• 55:08Jim Keller?

  While those guys have been gallivanting through the other realms of possibility. Right? And now recently, the computation lets you do mathematic, mathematical computations that are sophisticated enough. That nobody understands how the answers came out.

• 55:26Lex Fridman?

  Machine learning.

• 55:27Jim Keller?

  Machine learning.

• 55:28Lex Fridman?

  Yeah.

• 55:28Jim Keller?

  It used to be, you get data set, you, you guess at a function. The function is considered physics is if it's predictive of new functions, new data sets. Modern. You can take a large data set with no intuition about what it is and use machine learning to find a pattern that has no function. Right? And it can arrive at results that I don't know if they're completely mathematically describable. So computation has kind of done something interesting compared to A equal B plus C.

• 56:04Lex Fridman?

  There's something reminiscent of that step from the, the basic operations of addition.

• 56:11Lex Fridman?

  To taking a step towards neural networks that's reminiscent of what life on Earth at its origins was doing. Do you think we're creating sort of the next step in our evolution in in creating artificial intelligence systems?

• 56:24Jim Keller?

  That will. I don't know. I mean, there's so much in the universe already, it's hard to say.

• 56:28Lex Fridman?

  Where we stand in this whole thing.

• 56:30Jim Keller?

  Are human beings working on additional abstraction layers and possibilities? Yeah, it appears so. Does that mean that human beings don't need dogs? You know, no, like, like, there's so many things that are all simultaneously interesting and useful.

• 56:47Lex Fridman?

  Well, you've seen throughout your career, you've seen greater and greater level of abstractions built in artificial machines. Right? Do you think when you look at humans, you think that look of all life on Earth is a single organism building this thing, this machine with greater and greater levels of abstraction? Do you think humans are the peak, the top of the food chain in this long arc of history on Earth? Or do you think we're just somewhere in the middle? Are we are we the basic functional operations of a CPU? Are we the C++ program, the Python program, or with the neural network?

• 57:27Jim Keller?

  Like, somebody's, you know, people have calculated, like, how many operations does the brain do? Something, you know, I've seen the number 10 to the 18th about a bunch of times, arrived different ways. So could you make a computer that did 10 to the 20th operation?

• 57:40Lex Fridman?

  Yes.

• 57:41Jim Keller?

  Sure.

• 57:42Lex Fridman?

  So you think.

• 57:43Jim Keller?

  We're gonna do that. Now, is there something magical about how brains compute things? I don't know. You know, my personal experiences is interesting, because, you know, you think you know how you think, and then you have all these ideas, and you can't figure out how they happened. And if you meditate, you know, the, like, what what you can be aware of is interesting. So I don't know if brains are magical or not. You know, the physical evidence says no. Lots of people's personal experience says yes. So what would be funny is if brains are magical, and yet we can make brains with more computation. You know, I don't know what to say about that, but.

• 58:23Lex Fridman?

  Well, do you think magic is an emergent phenomenon?

• 58:26Jim Keller?

  It could be. I have no explanation for it.

• 58:30Lex Fridman?

  Let me ask Jim Keller of what what in your view is consciousness?

• 58:36Jim Keller?

  With with consciousness?

• 58:37Lex Fridman?

  Yeah, like what, you know, consciousness, love, things that are these deeply human things that seems to emerge from our brain. Is that something that we'll be able to make encode in chips? That get faster and faster and faster and faster?

• 58:54Jim Keller?

  That's like a 10 hour conversation. Nobody really knows.

• 58:57Lex Fridman?

  Can you summarize it in a couple of?

• 58:59Jim Keller?

  A couple of words?

• 59:02Jim Keller?

  Many people have observed that organisms run at lots of different levels, right? If you had two neurons, somebody said you'd have one sensory neuron and one motor neuron. Right? So we move towards things and away from things and we have physical integrity and safety or not. Right? And then if you look at the animal kingdom, you can see brains that are a little more complicated. And at some point, there's a planning system and then there's an emotional system that's, you know, happy about being safe or unhappy about being threatened. Right? And then our brains have massive numbers of structures, you know, like planning and movement and thinking and feeling and drives and emotions. And we seem to have multiple layers of thinking systems.

• 59:48Jim Keller?

  And we have a brain, a dream system that nobody understands whatsoever, which I find completely hilarious. And

• 59:56Jim Keller?

  you can think in a way that those systems are more independent and you can observe, you know, the different parts of yourself can observe them. I don't know which one's magical. I don't know which one's not computational.

• 1:00:12Jim Keller?

  So.

• 1:00:13Lex Fridman?

  Is it possible that it's all computation?

• 1:00:15Jim Keller?

  Probably. Is there a limit to computation? I don't think so.

• 1:00:20Lex Fridman?

  Do you think the universe is a computer?

• 1:00:23Jim Keller?

  Like it seems to be, it's a weird kind of computer. Because if it was a computer, right, like when they do calculations on what it, how much calculation it takes to describe quantum effects is unbelievably high. So if it was a computer, wouldn't you have built it out of something that was easier to compute? Right, that's a funny, it's a funny system. But then the simulation guys have pointed out that the rules are kind of interesting. Like, when you look really close, it's uncertain. And the speed of light says you can only look so far. And things can't be simultaneous, except for the odd entanglement problem where they seem to be. Like, the rules are all kind of weird. And somebody said physics is like having 50 equations with 50 variables to define 50 variables.

• 1:01:08Jim Keller?

  Like, you know, it's, you know, like physics itself has been a shitshow for thousands of years. It seems odd when you get to the corners of everything. You know, it's either uncomputable, or undefinable, or uncertain.

• 1:01:24Lex Fridman?

  It's almost like the designers of the simulation are trying to prevent us from understanding it perfectly.

• 1:01:29Jim Keller?

  But, but also, the, the things that require calculations require so much calculation that our idea of the universe of a computer is absurd because every single little bit of it takes all the computation in the universe to figure out. So that's a weird kind of computer. You know, you say the simulation is running in a computer, which has, by definition, infinite computation.

• 1:01:51Lex Fridman?

  Not infinite. Oh, you mean if the universe is infinite?

• 1:01:54Jim Keller?

  Yeah, well, well, every little piece of our universe seems to take infinite computation to figure out.

• 1:02:00Lex Fridman?

  Not infinite, just a lot.

• 1:02:01Jim Keller?

  Well, a lot's a pretty big number. Compute this little teeny spot takes all the mass in the local one light year by one light year space. It's close enough to infinite.

• 1:02:11Lex Fridman?

  What's the heck of a computer if it is one?

• 1:02:13Jim Keller?

  I know, it's, it's, it's a weird, it's a weird description because the simulation description seems to, to break when you look closely at it. But the rules of the universe seem to imply something's up. That seems a little arbitrary.

• 1:02:27Lex Fridman?

  The whole, the universe, the whole thing, the, the laws of physics?

• 1:02:31Jim Keller?

  Yeah.

• 1:02:31Lex Fridman?

  It just seems like, like, how did it come out to be the way it is?

• 1:02:37Jim Keller?

  Well, lots of people talk about that. It's, you know, it's, like I said, the two smartest groups of humans are working on the same problem.

• 1:02:43Lex Fridman?

  From different.

• 1:02:43Jim Keller?

  Different aspects, and they're both complete failures. So that's, that's kind of cool.

• 1:02:49Lex Fridman?

  They might succeed eventually.

• 1:02:51Jim Keller?

  Well, after 2,000 years, the trend isn't good.

• 1:02:55Lex Fridman?

  Oh, 2,000 years is nothing in the span of the history of the universe. So we have some time.

• 1:03:00Jim Keller?

  But the next 1,000 years doesn't look good either, so.

• 1:03:04Lex Fridman?

  That's what everybody says at every stage, but with Moore's Law, as you've just described, not being dead, the exponential growth of technology, the future seems pretty incredible.

• 1:03:14Jim Keller?

  Well, it'll be interesting, that's for sure.

• 1:03:16Lex Fridman?

  That's right. So what are your thoughts on Ray Kurzweil's sense that exponential improvement in technology will continue indefinitely? Is that how you see Moore's Law? Do you see Moore's Law more broadly, in the sense that technology of all kinds has a way of stacking S-curves on top of each other, where it'll be exponential? And then we'll see all kinds of-

• 1:03:41Jim Keller?

  What does an exponential of a million mean? That's a pretty amazing number. And that's just for a local little piece of silicon. Now, let's imagine you, say, decided to get 1,000 tons of silicon to collaborate in one computer at a million times the density.

• 1:04:01Jim Keller?

  Like, now you're talking, I don't know, 10 to the 20th more computation power than our current, already unbelievably fast computers? Like, nobody knows what that's gonna mean. You know, the sci-fi guys call it, you know, computonium. Like when, like a local civilization turns the nearby star into a computer. Like, I don't know if that's true, but-

• 1:04:23Lex Fridman?

  So just even when you shrink a transistor, the-

• 1:04:28Jim Keller?

  That's only one dimension.

• 1:04:29Lex Fridman?

  The ripple effects of that-

• 1:04:30Jim Keller?

  Like, people tend to think about computers as a cost problem, right? So, computers are made out of silicon and minor amounts of metals, and, you know, this and that. None of those things cost any money. Like, there's plenty of sand. Like, you could just turn the beach and a little bit of ocean water into computers. So, all the cost is in the equipment to do it. And the trend on equipment is, once you figure out how to build the equipment, the trend of cost is zero. Elon said, first you figure out what configuration you want the atoms in, and then how to put them there. Right?

• 1:05:07Lex Fridman?

  Yeah.

• 1:05:07Jim Keller?

  Because, well, what, here's the, you know, his, his great insight is, people are how constrained. I have this thing, I know how it works, and then little tweaks to that will generate something, as opposed to, what do I actually want, and then figure out how to build it. It's a very different mindset. And almost nobody has it, obviously.

• 1:05:29Lex Fridman?

  Well, let me ask on that topic. You were one of the key early people in the development of autopilot, at least in the hardware side. Elon Musk believes that autopilot and vehicle autonomy, if you just look at that problem, can follow this kind of exponential improvement. In terms of the how question that we're talking about, there's no reason why it can't. What are your thoughts on this particular space of vehicle autonomy? And you're a part of it, and Elon Musk's and Tesla's vision for?

• 1:06:01Jim Keller?

  Well, the computer you need to build was straightforward. And you could argue, well, does it need to be two times faster, or five times, or 10 times? But that's just a matter of time or price in the short run. So that's, that's not a big deal. You don't have to be especially smart to drive a car. So it's not like a super hard problem. I mean, the big problem with safety is attention, which computers are really good at, not skills.

• 1:06:27Lex Fridman?

  Well, let me push back on one. You say, everything you said is correct, but we as humans tend to, uh, tend to take for granted how incredible our vision system is. So.

• 1:06:44Jim Keller?

  You can drive a car with 2050 vision, and you can train a neural network to extract the distance of any object and the shape of any surface from a video and data.

• 1:06:54Lex Fridman?

  Yeah, but that.

• 1:06:55Jim Keller?

  It's really simple.

• 1:06:57Lex Fridman?

  No, it's not simple.

• 1:06:58Jim Keller?

  That's a simple data problem.

• 1:07:00Lex Fridman?

  It's not, it's not simple. It's because you, because it's not just detecting objects, it's understanding the scene, and it's being able to do it in a way that doesn't make errors. So the beautiful thing about the human vision system and our entire brain around the whole thing is we're able to fill in the gaps. It's not just about perfectly detecting cars. It's inferring the occluded cars. It's trying to, it's, it's understanding the.

• 1:07:29Jim Keller?

  I think that's mostly a data problem.

• 1:07:31Lex Fridman?

  So you think what data would compute with improvement of computation? With improvement in collection?

• 1:07:37Jim Keller?

  Well, there's a, you know, when you're driving a car and somebody cuts you off, your brain has theories about why they did it. You know, they're a bad person, they're distracted, they're dumb. You know, you can listen to yourself, right? So, you know, if you think that narrative is important to be able to successfully drive a car, then current autopilot systems can't do it. But if cars are ballistic things with tracks and probabilistic changes of speed and direction, and roads are fixed and given, by the way, they don't change dynamically. Right? You can map the world really thoroughly. You can place every object really thoroughly. Right? You can calculate trajectories of things really thoroughly.

• 1:08:22Jim Keller?

  Right?

• 1:08:23Lex Fridman?

  But everything you said about really thoroughly has a different degree of difficulty. So.

• 1:08:30Jim Keller?

  And you could say at some point, computer autonomous systems will be way better at things that humans are lousy at, like they'll be better at attention. They'll always remember there was a pothole in the road that humans keep forgetting about. They'll remember that this set of roads has these weirdo lines on it that the computers figured out once. And, and especially if they get updates, so if somebody changes a given.

• 1:08:54Lex Fridman?

  Right.

• 1:08:54Jim Keller?

  Like the, the key to robots and stuff, somebody said, is to maximize the givens.

• 1:09:00Lex Fridman?

  Right? Right.

• 1:09:01Jim Keller?

  Right? So, so having a robot pick up this bottle cap is way easier to put a red dot on the top. Cause then you'll have to figure out, you know, and if you want to do a certain thing with it, you know, maximize the givens is the thing. And autonomous systems are happily maximizing the givens. Like, like humans, when you drive someplace new, you remember it because you're processing it the whole time. And after the 50th time you drove to work, you get to work, you don't know how you got there, right? You're on autopilot. Right? Autonomous cars are always on autopilot. But the cars have no theories about why they got cut off or why they're in traffic.

• 1:09:39Lex Fridman?

  So that's.

• 1:09:39Jim Keller?

  They also never stop paying attention.

• 1:09:41Lex Fridman?

  Right? So I tend to believe you do have to have theories, meta models of other people, especially with pedestrians and cyclists, but also with other cars. So everything you said.

• 1:09:52Lex Fridman?

  Is like is actually essential to driving. Driving is a lot more complicated than people realize, I think. So sort of to push back slightly, but to.

• 1:10:01Jim Keller?

  So to cut into traffic, right?

• 1:10:03Lex Fridman?

  Yep.

• 1:10:03Jim Keller?

  You can't just wait for a gap. You have to be somewhat aggressive. You'll be surprised how simple a calculation for that is.

• 1:10:10Lex Fridman?

  I may be on that particular point, but there's a.

• 1:10:13Jim Keller?

  Yeah.

• 1:10:14Lex Fridman?

  That it, I, maybe I should have to push back. I would be surprised. You know what? Yeah, I'll just say where I stand. I would be very surprised, but I think it's. You might be surprised how complicated it is.

• 1:10:26Jim Keller?

  That I say, I tell people like progress disappoints in the short run and surprises in the long run.

• 1:10:30Lex Fridman?

  It's very possible. Yeah.

• 1:10:32Jim Keller?

  I suspect in 10 years, it'll be just like taken for granted.

• 1:10:35Lex Fridman?

  Yeah, probably. But you're probably right and I'll look like.

• 1:10:38Jim Keller?

  It's gonna be a $50 solution that nobody cares about. It's like GPS is like, wow, GPS is we have satellites in space.

• 1:10:45Lex Fridman?

  Yeah.

• 1:10:46Jim Keller?

  That tell you where your location is. It was a really big deal. Now everything has a GPS.

• 1:10:50Lex Fridman?

  Yeah, that's true. But I do think that systems that involve human behavior are more complicated than we give them credit for. So we can do incredible things with technology that don't involve humans. But when you.

• 1:11:02Jim Keller?

  I think humans are less complicated than people. You know, frequently obscribe.

• 1:11:07Lex Fridman?

  Maybe I.

• 1:11:08Jim Keller?

  We tend to operate out of large numbers of patterns and just keep doing it over and over.

• 1:11:12Lex Fridman?

  But I can't trust you because you're a human. That's something, something a human would say. But my, my hope is on the point you've made is even if, no matter who's right, even there. I'm hoping that there's a lot of things that humans aren't good at, that machines are definitely good at. Like you said, attention and things like that. Well, they'll be so much better that the overall picture of safety and autonomy will be, obviously cars will be safer. Even if they're not as good at.

• 1:11:41Jim Keller?

  I'm a, I'm a big believer in safety. I mean, there are already the current safety systems like cruise control that doesn't let you run into people and lane keeping. There are so many features that you just look at the Pareto of accidents and knocking off like 80% of them is, you know, super doable.

• 1:11:59Lex Fridman?

  Just to linger on the autopilot team and the efforts there, the. It seems to be that there's a very intense scrutiny by the media and the public in terms of safety, the pressure, the bar put before autonomous vehicles. What are your, sort of, as a person there, working on the hardware and trying to build a system that builds a safe vehicle and so on, what was your sense about that pressure? Is it unfair? Is it expected of new technology?

• 1:12:28Jim Keller?

  Yeah, it seems reasonable. I was interested, I talked to both American and European regulators, and I was worried that the regulations would write into the rules, "technology solutions." Like, modern brake systems imply hydraulic brakes. So if you read the regulations, to meet the letter of the law for brakes, it sort of has to be hydraulic, right? And the regulator said, they're interested in the use cases, like a head-on crash, an offset crash, don't hit pedestrians, don't run into people, don't leave the road, don't run a red light or a stoplight. They were very much into the scenarios.

• 1:13:10Jim Keller?

  And, you know, and they had, they had all the data about which scenarios injured or killed the most people. And, for the most part, those conversations were, like, what's the right thing to do to take the next step? Now Elon's very interested also in the, the benefits of autonomous driving are freeing people's time and attention, as well as safety. And I think that's also an interesting thing, but, you know, building autonomous systems so they're safe and safer than people seemed, since the goal is to be technic safer than people, having the bar to be safer than people and scrutinizing accidents seems philosophically,

• 1:13:54Jim Keller?

  you know, correct. So I think that's a good thing.

• 1:13:57Lex Fridman?

  What are...

• 1:14:00Lex Fridman?

  It's different than the things you worked at, you know, the Intel, AMD, Apple, with autopilot chip design and hardware design. What are interesting or challenging aspects of building this specialized kind of computing system in the automotive space?

• 1:14:17Jim Keller?

  I mean, there's two tricks to building like an automotive computer. One is the software team, the machine learning team is developing algorithms that are changing fast. So as you're building the accelerator, you have this, you know, worry or intuition that the algorithms will change enough that the accelerator will be the wrong one. Right? And there's the generic thing, which is if you build a really good general purpose computer, say its performance is one, and then GPU guys will deliver about 5X to performance for the same amount of silicon, because instead of discovering parallelism, you're given parallelism. And then special accelerators get another 2 to 5X on top of a GPU, because you say,

• 1:15:02Jim Keller?

  I know the math is always 8-bit integers into 32-bit accumulators, and the operations are the subset of mathematical possibilities. So auto, you know, AI accelerators have a claimed performance benefit over GPUs because in the narrow math space, you're nailing the algorithm. Now, you still try to make it programmable, but the AI field is changing really fast. So there's a, you know, there's a little creative tension there of, I want the acceleration afforded by specialization without being over specialized so that the new algorithm is so much more effective that you would have been better off on a GPU. So there is a tension there.

• 1:15:46Jim Keller?

  To build a good computer for an application like automotive, there's all kinds of sensor inputs and safety processors and a bunch of stuff. So one of Elon's goals to make it super affordable. So every car gets an autopilot computer. So some of the recent startups you look at, and they have a server in the trunk, because they're saying, I'm going to build this autopilot computer replaces the driver. Their cost budget's ten or twenty thousand dollars. And Elon's constraint was, I'm gonna put one in every car, whether people buy autonomous driving or not. So the the cost constraint he had in mind was great. Right. And to hit that, you had to think about the system design. That's complicated. It's it's fun. You know, it's like, it's like, it's craftsmen's work. Like, yeah, a violin maker, right?

• 1:16:31Jim Keller?

  You could say Stradivarius is this incredible thing. The musicians are incredible. But the guy making the violin, you know, picked wood and sanded it. And then he cut it, you know, and he glued it, you know, and he waited for the right day. So that when he put the finish on it, it didn't, you know, do something dumb. That's craftsmen's work. Right? You may be a genius craftsman because you have the best techniques and you discover a new one. But most engineers craftsmen's work. And humans really like to do that.

• 1:17:01Lex Fridman?

  You know, it's Smart humans.

• 1:17:02Jim Keller?

  No, everybody.

• 1:17:03Lex Fridman?

  All humans.

• 1:17:04Jim Keller?

  I don't know. I used to, I dug ditches when I was in college. I got really good at it. Satisfying.

• 1:17:09Lex Fridman?

  Yeah.

• 1:17:09Jim Keller?

  So

• 1:17:10Lex Fridman?

  Digging ditches is also craftsmen work.

• 1:17:12Jim Keller?

  Yeah, of course. So so there's an expression called complex mastery behavior. So when you're learning something, that's fun because you're learning something. When you do something, it's raw and simple. It's not that satisfying. But if the steps that you have to do are complicated and you're good at them, it's satisfying to do them. And then if you're intrigued by it all, as you're doing them, you sometimes learn new things that you can raise your game. But craftsmen's work is good. And engineers, like engineering is complicated enough that you have to learn a lot of skills. And then a lot of what you do is then craftsmen's work, which is fun.

• 1:17:50Lex Fridman?

  Autonomous driving, building a very, a resource constrained computer. So a computer has to be cheap enough to put in every single car.

• 1:17:57Jim Keller?

  Yeah.

• 1:17:57Lex Fridman?

  That essentially boils down to craftsmen's work. It's engineering.

• 1:18:02Jim Keller?

  You know, there's thoughtful decisions and problems to solve and trade offs to make. You need ten camera inputs or eight, you know, you're building for the current car or the next one. You know, how do you do the safety stuff? You know, there's, there's a whole bunch of details. But it's fun, but it's not like I'm building a new type of neural network, which has a new mathematics and a new computer to work. You know, that that's like, there's, there's more invention than that. But the reduction to practice, once you pick the architecture, you look inside and what do you see? Adders and multipliers and memories and, you know, the basics. So, computers is always this weird set of abstraction layers of ideas and thinking

• 1:18:43Jim Keller?

  that reduction to practice is transistors and wires and, you know, pretty basic stuff. And that's an interesting phenomenon. By the way, like factory work, like lots of people think factory work is rote, assembly stuff. I've been on the assembly line. Like, the people who work there really like it. It's a really great job. It's really complicated. Putting cars together is hard, right? And the car is moving and the parts are moving and sometimes the parts are damaged and you have to coordinate putting all the stuff together and people are good at it. They're good at it. And I remember one day I went to work and the line was shut down for some reason and some of the guys sitting around were really bummed because they had reorganized a bunch

• 1:19:25Jim Keller?

  of stuff and they were going to hit a new record for the number of cars built that day. And they were all glungho to do it. And these are big, tough buggers. And, you know, but what they did was complicated and you couldn't do it.

• 1:19:36Lex Fridman?

  Yeah, and I mean...

• 1:19:38Jim Keller?

  Well, after a while you could, but you'd have to work your way up because, you know, like putting the bright, what's called the brights, the trim on a car on a moving assembly line where it has to be attached 25 places in a minute and a half is unbelievably complicated. And, and, and human beings can do it. It's really good. I think that's harder than driving a car, by the way.

• 1:20:02Lex Fridman?

  Putting together, working...

• 1:20:03Jim Keller?

  Working in a factory.

• 1:20:05Lex Fridman?

  Too smart, people can disagree.

• 1:20:08Jim Keller?

  Yeah.

• 1:20:09Lex Fridman?

  I think driving a car...

• 1:20:11Jim Keller?

  We'll get you in the factory someday and then we'll see how you do.

• 1:20:14Lex Fridman?

  No, not for us. Humans driving a car is easy. I'm saying building a machine that drives a car is not easy. No, okay.

• 1:20:22Jim Keller?

  Okay.

• 1:20:23Lex Fridman?

  Driving a car is easy for humans because we've been evolving for billions of years.

• 1:20:27Jim Keller?

  Drive cars. Yeah, I noticed.

• 1:20:29Lex Fridman?

  To do.

• 1:20:30Jim Keller?

  The pale of the cars are super cool.

• 1:20:33Lex Fridman?

  Now, now you join the rest of the internet in mocking me.

• 1:20:37Jim Keller?

  I wasn't mocking you, I was just intrigued by your anthropology.

• 1:20:43Lex Fridman?

  Yeah.

• 1:20:44Jim Keller?

  I have to go dig into that.

• 1:20:45Lex Fridman?

  There's some inaccuracies there, yes. Okay, but in general,

• 1:20:52Lex Fridman?

  what have you learned in terms of thinking about passion, craftsmanship, tension, chaos? You know, the whole mess of it. What have you learned, have taken away from your time working with Elon Musk, working at Tesla, which is known to be a place of chaos, innovation, craftsmanship?

• 1:21:20Jim Keller?

  I really liked the way he thought. Like, you think you have an understanding about what first principles of something is, and then you talk to Elon about it, and you didn't scratch the surface. You know, he has a deep belief that no matter what you do, it's a local maximum. Right? And I had a friend, he invented a better electric motor, and it was like a lot better than what we were using. And one day he came by, he said, you know, I'm a little disappointed because, you know, this is really great, and you didn't seem that impressed. And I said, you know, when the super intelligent aliens come, are they gonna be looking for you? Like, where is he? The guy who built the motor?

• 1:21:58Lex Fridman?

  Yeah.

• 1:21:59Jim Keller?

  Probably not, you know? Like, but doing interesting work that's both innovative and, let's say, craftsmen's work on the current thing is really satisfying, and it's good. And that's cool. And then Elon was good at taking everything apart, and like, what's the deep first principle? Oh, no, what's really, no, what's really? You know, you know, that, that, you know, ability to look at it without assumptions, and how constraints is super wild.

• 1:22:31Jim Keller?

  You know, he built rocket ship and electric car and, you know, everything. And that's super fun, and he's into it, too. Like, when they first landed two SpaceX rockets to Tesla, we had a video projector in the big room, and like, 500 people came down, and when they landed, everybody cheered, and some people cried. It was so cool. Right? But how did you do that? Well...

• 1:22:55Jim Keller?

  It was super hard. And then people say, well, it's chaotic, really? To get out of all your assumptions, you think that's not going to be unbelievably painful? And is Elon tough? Yeah, probably. Do people look back on it and say, boy, I'm really happy I had that experience to go take apart that many layers of assumptions? Sometimes super fun, sometimes painful.

• 1:23:22Lex Fridman?

  That could be emotionally and intellectually painful, that whole process, just stripping away assumptions.

• 1:23:27Jim Keller?

  Yeah, imagine 99% of your thought process is protecting your self-conception. And 98% of that's wrong.

• 1:23:36Lex Fridman?

  Yeah.

• 1:23:36Jim Keller?

  Now you got the math right. How do you think you're feeling when you get back into that one bit that's useful? And now you're open and you have the ability to do something different? I don't know if I got the math right, it might be 99.9, but it ain't 50.

• 1:23:55Lex Fridman?

  Imagining it, the 50% is hard enough.

• 1:23:59Jim Keller?

  Yeah. Now, for a long time, I've suspected you could get better. Like you can think better, you can think more clearly, you can take things apart. And there's lots of examples of that. People who do that. So.

• 1:24:15Lex Fridman?

  And Elon is an example of that. Apparently. You are an example, so.

• 1:24:19Jim Keller?

  I don't know if I am. I'm fun to talk to.

• 1:24:23Lex Fridman?

  Certainly.

• 1:24:23Jim Keller?

  I've learned a lot of stuff.

• 1:24:25Lex Fridman?

  Right.

• 1:24:25Jim Keller?

  Well, here's the other thing is like, I joke, like, like I read books.

• 1:24:29Lex Fridman?

  Yeah.

• 1:24:29Jim Keller?

  And people think, oh, you read books? Well, no, I've read a couple of books a week for 55 years.

• 1:24:36Lex Fridman?

  Wow.

• 1:24:37Jim Keller?

  Well, maybe 50, because I didn't read, learn to read until I was eight or something. And and it turns out when people write books, they often take 20 years of their life where they passionately did something, reduce it to 200 pages. That's kind of fun. And then you go, you go online and you can find out who wrote the best books and who like, you know, that's kind of wild. So there's this wild selection process and then you can read it and for the most part, understand it. And then you can go apply it. Like I went to one company, I thought, I haven't managed much before. So I read 20 management books. And I started talking to him and basically compared to all the VPs running around. I'd run night, read 19 more management books than anybody else.

• 1:25:23Jim Keller?

  Wasn't even that hard.

• 1:25:25Lex Fridman?

  Yeah.

• 1:25:25Jim Keller?

  And half the stuff worked like first time. It wasn't even rocket science.

• 1:25:30Lex Fridman?

  But at the core of that is questioning the assumptions or sort of entering the thinking, first principles, thinking, sort of looking at the reality of the situation and using, using that knowledge, applying that knowledge.

• 1:25:45Jim Keller?

  So, yeah. So I would say my brain has this idea that you can question first assumptions. And but I can go days at a time and forget that. And you have to kind of like circle back to that observation.

• 1:25:59Lex Fridman?

  Because it is emotionally challenging.

• 1:26:02Jim Keller?

  Well, it's hard to just keep it front and center because you know, you're you operate on so many levels all the time. And, you know, getting this done takes priority or, you know, being happy takes priority or, you know, screwing around takes priority like, like, like how you go through life is complicated.

• 1:26:19Lex Fridman?

  Yeah.

• 1:26:19Jim Keller?

  And then you remember, oh, yeah, I could really think first principles. Oh, shit, that's, that's tiring, you know, but you do for a while and that's kind of cool.

• 1:26:29Lex Fridman?

  So just as a last question in your sense, from the big picture, from the first principles, do you think you kind of answered already, but do you think autonomous driving is something we can solve on a timeline of years? So one, two, three, five, 10 years as opposed to a century?

• 1:26:50Jim Keller?

  Yeah, definitely.

• 1:26:52Lex Fridman?

  Just to linger on it a little longer, where's the confidence coming from? Is it the fundamentals of the problem, the fundamentals of building the hardware and the software?

• 1:27:03Jim Keller?

  As a computational problem, understanding ballistics, roles, topography, it seems pretty solvable. I mean, and you can see this, you know, like, like speech recognition for a long time, people are doing, you know, frequency and domain analysis and, and all kinds of stuff. And that didn't work for at all, right? And then they did deep learning about it and it worked great. And it took multiple iterations and.

• 1:27:33Jim Keller?

  You know, autonomous driving is way past the frequency analysis point. You know, use radar, don't run into things. And the data gathering is going up and the computation is going up and the algorithm understanding is going up. And there's a whole bunch of problems getting solved like that.

• 1:27:48Lex Fridman?

  The data side is really powerful, but I disagree with both you and Elon. I'll tell Elon once again, as I did before, that, that when you add human beings into the picture, the, it's no longer a ballistics problem. It's something more complicated, but I could be very well proven wrong.

• 1:28:07Jim Keller?

  Cars are highly damped in terms of rate of change. Like the steering and the steering system is really slow compared to a computer. The acceleration, the acceleration is really slow.

• 1:28:17Lex Fridman?

  Yeah, on a certain time scale, on a ballistics time scale, but human behavior, I don't know. I, I shouldn't say.

• 1:28:24Jim Keller?

  Human beings are really slow too. Weirdly, we operate, you know, half a second behind reality. Nobody really understands that one either. It's pretty funny.

• 1:28:33Lex Fridman?

  Yeah, yeah.

• 1:28:35Jim Keller?

  So.

• 1:28:37Lex Fridman?

  We'll be, we very well could be surprised. And I think with the rate of improvement in all aspects on both the compute and the, the software and the hardware, there's going to be pleasant surprises all over the place.

• 1:28:51Lex Fridman?

  Speaking of unpleasant surprises, many people have worries about a singularity in the development of AI. Forgive me for such questions.

• 1:29:00Jim Keller?

  Yeah.

• 1:29:01Lex Fridman?

  When, when AI improves exponentially and reaches a point of superhuman level general intelligence, you know, beyond the point, there's no looking back. Do you share this worry of existential threats from artificial intelligence from computers becoming superhuman level intelligent?

• 1:29:18Jim Keller?

  No, not really. You know, like we already have a very stratified society. And then if you look at the whole animal kingdom of capabilities and abilities and interests. And, you know, smart people have their niche and, you know, normal people have their niche and craftsmen have their niche and, you know, animals have their niche. I suspect that the domains of interest for things that, you know, astronomically different, like the whole, something got 10 times smarter than us and wanted to track us all down because what? We like to have coffee at Starbucks? Like, it doesn't seem plausible. No, is there an existential problem that, how do you live in a world where there's something

• 1:30:00Jim Keller?

  way smarter than you and you based your kind of self-esteem on being the smartest local person? Well, there's what? 0.1% of the population who thinks that? Because the rest of the population's been dealing with it since they were born. So, the breadth of possible experience that can be interesting is really big. And, you know, superintelligence seems likely, although we still don't know if we're magical. But I suspect we're not. And it seems likely that it'll create possibilities that are interesting for us. And its interests will be interesting for that, for whatever it is.

• 1:30:43Jim Keller?

  It's not obvious why its interests would somehow want to fight over some square foot of dirt or, you know, whatever, you know, the usual fears are about.

• 1:30:54Lex Fridman?

  So you don't think it'll inherit some of the darker aspects of human nature?

• 1:30:58Jim Keller?

  Depends on how you think reality's constructed. So, for whatever reason, human beings are in, let's say, creative tension and opposition with both our good and bad forces. Like, there's lots of philosophical understanding to that. Right? I don't know why that would be different.

• 1:31:19Lex Fridman?

  So you think the evil is necessary for the good? I mean, the tension?

• 1:31:24Jim Keller?

  I don't know about evil, but, like, we live in a competitive world where your good is somebody else's, you know, evil. You know, there's the malignant part of it, but that seems to be self-limiting, although occasionally it's, it's super horrible.

• 1:31:44Lex Fridman?

  But yes, there's a debate over ideas and some people have different beliefs and that that debate itself is a process so that arriving at something.

• 1:31:54Jim Keller?

  Yeah, and why wouldn't that continue?

• 1:31:56Lex Fridman?

  Yeah. But you don't think that whole process will leave humans behind in a way that's painful? Emotionally painful, yes, for the one, for the 0.1%, they'll be.

• 1:32:07Jim Keller?

  Why isn't it already painful for a large percentage of the population? And it is, I mean, society does have a lot of stress in it about the 1% and about the this and about the that, but you know, everybody has a lot of stress in their life about what they find satisfying and, and you know, know yourself seems to be the proper dictum and pursue something that makes your life meaningful seems proper.

• 1:32:33Jim Keller?

  And there's so many avenues on that, like, there's so much unexplored space at every single level.

• 1:32:41Jim Keller?

  You know, I'm somewhat of, my nephew called me a jaded optimist.

• 1:32:48Jim Keller?

  And, you know, so it's.

• 1:32:50Lex Fridman?

  There's a beautiful tension in that, in that label. But if you were to look back at your life and could relive a moment, a set of moments, because there were the happiest times of your life outside of family, what would that be?

• 1:33:11Jim Keller?

  I don't want to relive any moments. I like that. I like that situation where you have some amount of optimism and then the anxiety of the unknown.

• 1:33:23Lex Fridman?

  So you love the unknown, the mystery of it?

• 1:33:26Jim Keller?

  I don't know about the mystery, it sure gets your blood pumping.

• 1:33:30Lex Fridman?

  What do you think is the meaning of this whole thing? Of life? On this pale blue dot?

• 1:33:38Jim Keller?

  It seems to be what it does.

• 1:33:42Jim Keller?

  Like, the universe, for whatever reason, makes atoms, which makes us, which we do stuff.

• 1:33:51Jim Keller?

  And we figure out things and we explore things and.

• 1:33:54Lex Fridman?

  That's just what it is.

• 1:33:56Jim Keller?

  It's not just.

• 1:33:58Lex Fridman?

  Yeah, it is. Jim, I don't think there's a better place to end it. It's a huge honor and.

• 1:34:06Jim Keller?

  Well, that's super fun.

• 1:34:08Lex Fridman?

  Thank you so much for talking today.

• 1:34:09Jim Keller?

  All right, great.

• 1:34:10Lex Fridman?

  Thanks for listening to this conversation and thank you to our presenting sponsor Cash App. Download it, use code LexPodcast, you'll get $10 and $10 will go to FIRST, a STEM education nonprofit that inspires hundreds of thousands of young minds to become future leaders and innovators. If you enjoy this podcast, subscribe on YouTube, give it five stars on Apple Podcasts, follow on Spotify, support it on Patreon, or simply connect with me on Twitter. And now let me leave you with some words of wisdom from Gordon Moore. If everything you try works, you aren't trying hard enough. Thank you for listening and hope to see you next time.