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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 episode is presented by Cash App. Download it & use code "LexPodcast": Cash App (App Store): https://apple.co/2sPrUHe Cash App (Google Play): https://bit.ly/2MlvP5w PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 2:12 - Difference between a computer and a human brain 3:43 - 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 CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman
In this introduction, Lex Fridman presents Jim Keller, a renowned microprocessor engineer with a rich history at AMD, Apple, Tesla, and Intel. Keller is celebrated for his groundbreaking work on various microarchitectures and instruction sets, making him a pivotal figure in the evolution of computing technology.
"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 K 7 K 8 K 12 and Xen microarchitectures..."
Lex discusses the sponsorship of the podcast by Cash App, highlighting its features such as sending money, buying Bitcoin, and investing in stocks. He emphasizes the app's commitment to supporting educational initiatives like FIRST, which inspires students to pursue engineering and technology.
"artificial intelligence podcast if you enjoy it subscribe on YouTube give it five stars an apple podcast follow on Spotify supported on patreon or simply connect with me on Twitter Alex Friedman spell..."
Jim Keller explores the fundamental differences and similarities between computers and human brains. He discusses the architecture of computers, focusing on memory and computation, and contrasts it with the interconnected nature of the human brain, emphasizing the complexity of neural networks.
"in similarities between the human brain and a computer with the microprocessors core let's start with a philosophical question perhaps well since people don't actually understand how human brains work..."
Keller delves into the foundational aspects of computer engineering, explaining how to build a computer from the ground up. He outlines the abstraction layers from atoms to transistors, logic gates, and processing elements, illustrating the complexity and organization required in computer design.
"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 it microarchitecture what's an instruction set architecture maybe even as fa..."
In this segment, Keller discusses the significance of instruction sets in computer architecture, particularly the x86 and ARM instruction sets. He explains how these sets define basic operations and how their stability over time has influenced modern computing.
"so when you when you build a computer you know first there's a target like what's it for look how fast does it have to be which you know today there's a whole bunch of metrics about what that is and t..."
Keller explains how modern computers execute instructions differently than older models. He describes the process of fetching multiple instructions, analyzing their dependencies, and executing them in parallel, highlighting the complexity and efficiency of contemporary computing systems.
"for relatively long periods of time instruction sets are stable so the x86 instruction said the arm instruction set what's an instruction set so it says how do you encode the basic operations load sto..."
This segment focuses on the concept of parallelism in computing, distinguishing between found parallelism and given parallelism. Keller elaborates on how modern processors handle large numbers of instructions and the challenges of executing dependent instructions efficiently.
"the the computer sort of has a bunch of bookkeeping tables it says what order CDs operations finishing or appear to finish him but to go fast you have to fetch a lot of instructions and find all the p..."
Keller discusses the intricacies of branch prediction in modern processors. He explains how predicting the outcome of branches can significantly enhance performance and the evolution of techniques from simple history-based methods to complex neural network-like systems.
"is it to discuss well how hard is it that's just transistor count right so once you crack the problem you say here's how you fetch ten instructions at a time here's how you calculated the dependencies..."
In this segment, Keller reflects on the increasing complexity of computing systems. He discusses the trade-offs between accuracy and performance in branch prediction and how modern systems utilize vast amounts of data to improve decision-making processes.
"effective okay so parallelism you can't paralyze branches or you can looking pretty you can what is predict a branch mean or what open take so imagine you do a computation over and over you're in a lo..."
Keller explains the consequences of incorrect branch predictions in computing. He describes how processors handle mispredictions and the performance costs associated with flushing the pipeline, emphasizing the importance of accurate predictions for efficient execution.
"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 ..."
Keller discusses the balance of art and science in computer design. He highlights the importance of intuition and creativity in making design decisions, while also acknowledging the rigorous analytical processes that underpin successful engineering.
"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 miss you took the wrong path you executed a bunch of s..."
In this segment, Keller addresses the mathematical definition of program correctness, emphasizing that a correct program should yield the same result upon multiple executions. He explores the implications of variability in outputs, particularly in the context of modern AI and graphics processing.
"because some people are very good at making those intuitive leaps it seems like the combinations of things some people are less good at it but they're really good at evaluating your alternatives right..."
In this segment, Keller discusses the implications of noise in AI calculations, where low precision data and noisy inputs lead to different outputs. He explores how algorithms can leverage this noise for faster results, challenging the expectation of deterministic answers in programming and the complexities of achieving reliable outputs in AI.
"problem so if you run a correct C program the definition is every time you run it you get the same answer yeah that well that's a math statement but that's a that's a language definitional statement s..."
Keller reflects on the human elements involved in computer design, comparing engineers to functional units in a computer. He discusses the importance of understanding how people work together and how organizational design can be viewed as an architectural problem, emphasizing the need for diverse talents in successful teams.
"have experimented with algorithms that say can get faster answers by being noisy like as the network starts to converge if you look at the computation graph it starts out really wide and it gets narro..."
Keller contrasts the difference between following recipes and achieving deeper understanding in engineering. He argues that true expertise goes beyond executing predefined steps, advocating for a comprehensive grasp of underlying principles that can lead to innovative solutions in technology and design.
"because almost everything going into monetary calculations is noisy so why the answers have to be so clear it's right so where do you stand by design computers for people who run programs so somebody ..."
In this segment, Keller discusses the intricate nature of human interactions in teams, likening it to computational problems. He highlights the challenges of managing diverse personalities and skills, and the importance of intuition in guiding team dynamics and decision-making processes.
"set of brilliant ideas that that were involved in well I find that description odd and I has two small children and I promise you they think it's hilarious this question yeah so I dude so I I'm I'm re..."
Keller emphasizes the need for simplicity in design, warning against the pitfalls of incremental complexity. He explains how rethinking and refactoring designs can lead to more efficient and effective solutions, advocating for a mindset that embraces fundamental redesigns every few years.
"after a lot of years of building computers where you sort of build them out of the transistors logic gates functional units come computational elements that you could think of people the same way so p..."
Keller reflects on Moore's Law and its implications for the future of technology. He discusses how continuous innovation and the interplay of various technological advancements contribute to the ongoing relevance of Moore's Law, despite predictions of its demise.
"right so do you know the difference between a recipe and understanding there's probably a philosophical description of this so imagine you can make a loaf of bread yeah the recipe says get some flour ..."
In this segment, Keller defines Moore's Law, explaining its historical context and significance in the tech industry. He discusses the doubling of transistor counts and the evolving nature of performance metrics, providing insights into how engineers perceive and adapt to the challenges of maintaining this trend.
"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 ..."
Keller explores the future of transistor technology, discussing the physical limits of miniaturization and the potential of quantum computing. He highlights the ongoing research and innovations that could redefine the capabilities of microprocessors and sustain the momentum of Moore's Law.
"everything for deeper understanding you never get anything done right 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 ..."
Keller elaborates on the multitude of innovations that contribute to Moore's Law, emphasizing that it is not solely about shrinking transistors. He discusses the various fields of science and technology that intersect to drive advancements in computing, illustrating the complexity of maintaining progress in the industry.
"like going towards the fundamental limits of physics sort of really getting into the core of the sighs well in terms of building the computer thinks simple think a little simpler so common practice is..."
Jim Keller explains that Moore's Law is not just about smaller transistors; it's a complex interplay of thousands of innovations, each with diminishing returns. He discusses how these innovations create an exponential curve of progress, emphasizing the importance of continuous invention in the microprocessor industry.
"think people think Moore's law is one thing transistors get smaller but actually under the sheets ours literally thousands of innovations and almost all those innovations have their own diminishing re..."
Keller delves into the physical limits of transistor size, discussing modern transistors and the potential for quantum effects at atomic scales. He highlights the ongoing research in quantum computing and the implications for future transistor designs, suggesting that we are not yet at the fundamental limits of physics.
"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 and you can place atoms if you want to it'..."
In this segment, Keller discusses the strategic mindset required for computer design in light of Moore's Law. He compares the evolution of transistor sizes to building with smaller bricks, emphasizing the need for innovative design approaches to manage increasing complexity and leverage the anticipated growth in transistor counts.
"to another two weeks 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 there's you have two stanc..."
Keller reflects on the challenges faced by design teams as transistor counts increase. He explains the necessity of abstraction layers and the importance of dividing tasks among teams to manage complexity effectively, highlighting the constraints of human cognition in large-scale projects.
"of it and also to cope with it like that's the thing people to understand it's like if I didn't believe in Moore's law and Moore's law transistors showed up my design teams were all drowned so what's ..."
This segment explores the evolution of computational techniques from simple arithmetic to complex algorithms used in AI. Keller discusses how modern computations involve hierarchical mathematics and the implications of data organization for future computing paradigms.
"abstraction layers is really high we used to build computers out of transistors now we have a team that turns transistors and logic cells and our team that turns them into functional you know it's ano..."
Keller discusses the transformative potential of AI and how it leverages advancements in computation. He contrasts traditional search algorithms with modern AI techniques, emphasizing the complexity and depth of computations required for tasks like image recognition.
"you know from simple equation to linear equations to matrix equations to it's a deeper kind of computation and the data sets are getting so big that people are thinking of data as a topology problem y..."
In this segment, Keller reflects on the fundamental mathematical operations that underpin computing. He discusses the enduring relevance of basic operations like addition and multiplication, while also considering the potential for quantum and analog computing to revolutionize these processes.
"of search for the I don't know if you looked at the inner layers of finding a cat it's not a search it's it's a set of endless projection so you know projection and here's a shadow of this phone yeah ..."
Keller articulates his belief that Moore's Law is far from dead, citing ongoing innovations in transistor technology and the emergence of new computational paradigms. He discusses the unpredictable nature of future advancements and the potential for transformative changes in the computing landscape.
"kind of thing and okay in that sense you can say yeah yeah you know I could see how you you might say if if you the funny thing is it's the difference between given search space and found search space..."
Jim Keller discusses the ongoing innovations in transistor technology, emphasizing the importance of nano wires and the potential for significant advancements in computation. He highlights the unpredictable nature of these developments and how they could reshape the future of computing.
"continues shrinking the transistor or is it another s-curve that steps in and it totally so dope shrinking the transistor is literally thousands of innovations right so there's so this they're all ans..."
Keller explains the concept of S-curves in technology, illustrating how each leap in performance leads to new computational paradigms. He reflects on the historical evolution from mainframes to mobile devices and the implications of emerging technologies like 5G.
"lot yes this innovation from just that shrinking yeah like a factor of a hundred salade yeah I would say that's incredible and it's totally it's only 10 or 15 years now you're smarter you might know b..."
In this segment, Keller shares his thoughts on the societal impact of technology, expressing a sense of responsibility and excitement about being a key architect of future innovations. He discusses the unpredictable nature of technological advancements and their effects on human interaction.
"that you're one of the key architects of this kind of futures you're not we're not talking about the architects of the high-level people who build the Angry Bird apps and flapping Angry Bird of who kn..."
Keller delves into the philosophical questions surrounding existence and the universe, comparing the disciplines of physics and philosophy. He reflects on the complexity of life and the challenges of understanding our place in the cosmos.
"unpredictable if I wasn't here somebody else are doing 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 kn..."
This segment explores the relationship between computation and consciousness. Keller discusses the potential for artificial intelligence to evolve and whether it can replicate the complexities of human thought and emotion.
"yeah I guess I guess I do tend to are significantly increases in complexity and I'm curious about how computation like like our world our physical world inherently generates mathematics it's kind of o..."
Keller contemplates the evolutionary trajectory of humanity in relation to technology. He questions whether humans are at the pinnacle of evolution or merely a stepping stone in a larger process of development.
"interesting compared to a POV plus see there's something reminiscent of that step from the basic operations of addition to taking a step towards new all networks that's reminiscent of what life on Ear..."
In this thought-provoking segment, Keller discusses the nature of consciousness and whether it can be replicated in machines. He reflects on the complexities of human experience and the potential for future advancements in artificial intelligence.
"the C++ program the Python Perl Network like somebody's you know people have calculated like how many operations does the brain do and something you know I've seen the number 10 to the 18th about bunc..."
Keller explores the intriguing idea that the universe itself may function as a computer. He discusses the complexities of quantum mechanics and the challenges of understanding the fundamental nature of reality.
"nobody really knows can you summarize it in a couple of couple of words many people have observed that organisms run at lots of different levels right if you got two neurons somebody said you'd have o..."
In this segment, Keller shares his insights on Moore's Law and its implications for the future of technology. He discusses the exponential growth of computational power and the potential for groundbreaking advancements in various fields.
"I don't think so do you think the universe is a computer I think he 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 calc..."
Keller reflects on his experience with Tesla's Autopilot and the potential for exponential improvements in vehicle autonomy. He discusses the challenges and opportunities in developing safe and efficient autonomous systems.
"interesting that's for sure that's right so what are your thoughts on Ray Kurzweil sense that exponential improvement and technology will continue indefinitely that is that how you see Moore's law do ..."
Jim Keller discusses the complexities of building autonomous vehicles, emphasizing that while computers excel at attention and data processing, they lack the nuanced understanding of human behavior necessary for safe driving. He contrasts the straightforward nature of driving with the intricate challenges posed by human interactions on the road, highlighting the need for advanced algorithms to interpret and respond to unpredictable situations.
"space of vehicle autonomy and you're a part of it and Elon Musk's and Tesla's vision well the computer you need to build was straightforward and you can argue well doesn't need to be 2 times faster or..."
Keller elaborates on the superiority of the human vision system in driving, explaining how humans can infer context and fill in gaps that current computer systems struggle with. He points out that while computers can detect objects, they lack the ability to understand scenes and anticipate human behavior, which is crucial for safe driving in dynamic environments.
"is we were 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 I think it's mostly a bigger problem you so..."
In this segment, Keller reflects on the potential of autonomous systems to surpass human capabilities in certain areas, such as attention and memory. He discusses how these systems can be designed to remember critical information and adapt to changes, ultimately leading to safer driving experiences. Keller emphasizes the importance of balancing human-like understanding with the computational power of machines.
"somebody changes a given like that Akita robots and stuff somebody said is to maximize two Givens okay right so though having a robot pick up this bottle cap is ways you put a red dot on the top becau..."
Keller shares insights into the craftsmanship involved in building autonomous vehicles, likening it to the meticulous work of a violin maker. He discusses the challenges of designing affordable and effective autopilot systems, emphasizing the need for thoughtful engineering decisions and the balance between specialization and general-purpose computing.
"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 that I'd say that I tell people's like progress disappoints in th..."
Keller addresses the regulatory landscape surrounding autonomous vehicles, discussing the expectations for safety and the scrutiny from the public and media. He highlights the importance of focusing on real-world scenarios that lead to accidents and the need for regulations to adapt to technological advancements without stifling innovation.
"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 you know super doable just a wing guard on the autopilot team an..."
In this segment, Keller explores the unique challenges of developing computing systems for the automotive industry. He discusses the rapid evolution of machine learning algorithms and the need for automotive computers to remain adaptable while maintaining high performance. Keller emphasizes the importance of creating systems that are both effective and affordable for widespread use.
"people and for the most part those conversations were like what's the right thing to do to take the next step now elan is very interested also in the benefits of autonomous driving or freeing people's..."
Keller reflects on the artistry involved in engineering design, comparing it to craftsmanship. He discusses the intricate details that go into building autonomous systems and the satisfaction that comes from solving complex problems. Keller emphasizes the importance of creativity and innovation in engineering, particularly in the context of developing safe and effective autonomous vehicles.
"you say I know the math is always 8-bit integers and two 32-bit accumulators and the operations are the subsets of mathematical possibilities so although you know AI accelerators have a claimed perfor..."
Keller shares his experiences with factory work, highlighting the complexity and skill involved in assembling vehicles. He contrasts the perceived simplicity of factory tasks with the reality of the intricate processes required to build cars, emphasizing the expertise and craftsmanship of factory workers.
"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 you kn..."
In this segment, Keller discusses the differences between driving a car and building the systems that enable autonomous driving. He argues that while driving may seem easy for humans, the engineering challenges of creating a reliable autonomous system are significantly more complex. Keller emphasizes the need for a deep understanding of both human behavior and technological capabilities.
"they're all networked which has a new mathematics and a new computer at work do you know that that's like there's a there's more invention than that but the rejection to practice once you picked the a..."
Keller reflects on his time working with Elon Musk, discussing the innovative and chaotic environment at Tesla. He shares insights into Musk's approach to first principles thinking and the importance of challenging assumptions in engineering. Keller emphasizes the value of this mindset in driving innovation and overcoming obstacles in technology development.
"car is not easy ok ok driving a car is easy for humans because we've been evolving for billions of years drive cars yeah no juice the pail if the cars are super cool no now you join the rest of the in..."
In this segment, Keller shares his experience of reading management books to enhance his leadership skills. He emphasizes the value of questioning assumptions and applying knowledge gained from literature to real-world scenarios, illustrating how this approach can lead to effective management practices.
"yeah I imagine 99% of your thought process is protecting your self conception and 98% of that's wrong yeah now you got there math right no you think your feeling when you get back into that one bit th..."
Keller discusses the significance of first principles thinking in problem-solving and innovation. He reflects on how this mindset allows individuals to break down complex problems and find effective solutions, while also acknowledging the challenges of maintaining this perspective in daily life.
"like I talk like like I read books and people think oh you read books well no I brought a couple books awake for 55 years well maybe 50 cuz I didn't read learned read tall as H or something and and it..."
Keller expresses confidence in the potential for solving autonomous driving challenges within a decade. He discusses the fundamentals of the problem, including hardware and software advancements, and the importance of understanding human behavior in the context of autonomous systems.
"mean yes 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 data observation because..."
In this thought-provoking segment, Keller addresses concerns about existential threats posed by superintelligent AI. He shares his perspective on the implications of advanced AI, suggesting that the interests of superintelligent beings may not align with human fears, and discusses the complexities of human nature in relation to AI development.
"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..."
Keller reflects on the stratification of society and the varying capabilities of individuals. He discusses the philosophical implications of superintelligence and how it may not necessarily lead to conflict, emphasizing the diversity of human experiences and the potential for coexistence with advanced AI.
"speaking of unpleasant surprises many people have worries about a singularity in the development of AI forgive me for such questions you know what when AI improves exponentially and reaches a point of..."
In the concluding segment, Keller shares his thoughts on the meaning of life and the human experience. He emphasizes the importance of exploration and understanding our place in the universe, reflecting on the journey of life and the pursuit of knowledge as central themes.
"really big and you know super intelligence seems likely although we still don't know if we're magical but I suspect we're not and it seems likely that'll create possibilities that are interesting for ..."