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, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), 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
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A modern computer fetches a large number of instructions and then executes it in a way that gets the right answers. When building a computer, the target and required speed are taken into consideration, while different disciplines work together to make it happen.
A window of instructions needs to be predicted with 99% accuracy in order for it to be effective. 20 years ago, branches were predicted simply by recording the previous direction, but now it requires three to four orders of magnitude more bits.
The concept of deterministic programming is that every time you run a correct program, you get the same answer, contrary to 3D acceleration of graphics where you could run the same scene multiple times and get different answers.
The speaker draws similarities between people and the elements of computer architecture and talks about the interconnectedness and multiple levels of understanding involved in bread-making.
Moore's Law served as an inspiring beacon of what kind of amazing future brilliant engineers can build. However, 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.
The possibility of more powerful computers is exciting, but for some, shrinking computing performance is even more so. Due to this, designers are looking into optimizing architecture and using more transistors or not being swamped by their complexity.
In this episode, the hosts explore the building blocks of computers, transistors, and atoms, as well as the relationship between analog and digital computing, and how mathematics is linked to adds, subtracts, and multiplication, despite an infinite space.
The podcast host and guest discuss how humans and artificial intelligence build greater levels of abstraction and the emergence of deeply human things like consciousness and love from our brains.
Simulating the universe on a computer would require an absurd amount of computation, especially for complex calculations. The nature of physics and the existence of planning and emotional systems within a simulation further complicate the process.
Elon Musk predicts that autopilot and vehicle autonomy will experience exponential improvement in the future, but current systems may struggle with narrative-based driving scenarios.
The goal of building autonomous systems is to make them 10 times safer than people by focusing on things that machines are good at such as attention to details. Scrutinizing accidents and setting the bar of safety above human performance is the right philosophy to have in building autonomous systems.
The process of attaching the trim on a car on a moving assembly line is extremely complicated, requiring coordination and craftsmanship, with people who are good at it. The work is both innovative and satisfying, but also comes with challenges and constraints.
The possibility of solving autonomous driving within a few years is uncertain, and speech recognition has come a long way but still has its limitations.
Pursuing a life that is meaningful will lead to happiness and fulfillment. In addition, supporting STEM education through nonprofit organizations can inspire future generations to become leaders and innovators.