Jim Keller: The Future of Computing, AI, Life, and Consciousness | Lex Fridman Podcast #162 | Summary and Q&A

TL;DR

Jim Keller discusses the value of theory and engineering in building software and hardware systems, the future of computing, and his role as CTO of TensTorrent, a company that focuses on building scalable and efficient hardware for deep learning.

Key Insights

• 🔍 The value and effectiveness of theory versus engineering in building software or hardware systems. Engineering focuses on the reduction of known methods, while science aims to discover new solutions and understand unknown problems.
• 🚀 Innovation in computer design is primarily driven by engineering and the reduction of practice. However, breakthrough ideas can still occur and have a significant impact on the industry.
• 📝 Good software and hardware designs require a balance between theory and engineering. While theory constructs models that generalize how things work, engineering involves actually building and making things work in practice.
• ⚙️ Branch prediction is a key problem in computer performance, and a singular innovative idea can greatly improve this aspect. It combines elements of theory, engineering, and information theory.
• 🧩 Companies need to strike a balance between rewarding innovative ideas and focusing on basic engineering aspects. Neglecting the basics can hinder progress and lead to unsatisfactory results.
• 🔗 The success of a company's innovation lies in the craftsmanship of its engineering design. Attention to detail, quality materials, and a focus on the basics contribute to building great products.
• 🌱 Simplicity is valued in design, and too simple or too complex can have negative consequences. The right balance of simplicity and complexity enables efficient and effective design solutions.
• 🌌 The innovation and potential of hardware and software design are limitless. The continuous development of new technologies, methodologies, and thought processes pushes the boundaries of what is possible in computing.

Transcript

the following is a conversation with jim keller his second time in the podcast jim is a legendary microprocessor architect and is widely seen as one of the greatest engineering minds of the computing age in a peculiar twist of space-time in our simulation jim is also a brother-in-law of jordan peterson we talk about this and about computing artific... Read More

Questions & Answers

Q: What is the role of innovation in computer design, and how does it interact with the need for craftsmanship?

Innovation plays a crucial role in computer design, as breakthrough ideas can lead to significant advancements. However, it's important to balance innovation with craftsmanship to ensure that basic design principles are not neglected. Good engineering is often characterized by great craftsmanship, which requires attention to detail and a focus on the basics.

Q: How does the scalability of hardware impact the efficiency of deep learning systems?

Scalability is crucial in deep learning systems because it allows for the efficient execution of complex graph programs across multiple processors. By distributing the workload and leveraging the power of parallel processing, efficiency can be improved. However, scaling also presents challenges, such as data movement and scheduling, which need to be addressed to achieve optimal performance.

Q: What are the key factors in building efficient hardware for deep learning?

Efficiency in deep learning hardware involves designing systems that can execute graph programs natively, without the need for excessive software optimizations. The hardware should be able to handle matrix multiplications, convolutions, data manipulations, and data movements efficiently. Additionally, scalability and modularity are important considerations to ensure that the hardware can handle large-scale deep learning tasks.

Q: How does the concept of spatial computing fit into the future of hardware design?

Spatial computing, which involves performing computations on data in its original location, rather than moving the data to a central processor, is a promising direction for hardware design. Scaling the number of computers and allowing for computations to happen within the data space can significantly improve efficiency. However, there are still challenges to overcome, especially in terms of data movement and coordination between heterogeneous computers.

Summary

In this podcast conversation, Jim Keller, a legendary microprocessor architect and brother-in-law of Jordan Peterson, discusses various topics related to computing, artificial intelligence, consciousness, and life. He talks about the value and effectiveness of theory versus engineering in building good software or hardware systems, the role of innovation in computer design, the importance of craftsmanship in engineering, and the balance between perfection and practicality. He also shares his thoughts on the leadership styles of Steve Jobs and Elon Musk, the challenges of designing complex systems, and the beauty of well-designed modular systems.

Questions & Answers

Q: What is the value and effectiveness of theory versus engineering in building software or hardware systems?

Both theory and engineering have their value in building good software or hardware systems. Engineering involves the practical application of known methods and practices, while theory is focused on discovering new knowledge and solving unknown problems. Theory constructs models that generalize how things work, while engineering is concerned with the actual process of building things. For example, economists have models of how the economy works, but implementing economic policies requires engineering to put those models into practice. In computer design, most of the work is engineering, but new ideas and breakthroughs can also happen within the engineering space.

Q: Can you provide an example of an engineering breakthrough in computer design?

One example of an engineering breakthrough in computer design is improved branch prediction. Branch prediction is a key problem in computer performance, as computers are limited by the predictability of the path of branches and the predictability of the locality of data. Someone came up with a better way to do branch prediction, published a paper on it, and now every computer in the world uses it. This breakthrough idea improved the performance of computers and solved a significant problem in computer design.

Q: Is the paper on branch prediction an example of theory or engineering?

The paper on branch prediction encompasses both theory and engineering aspects. While there is theoretical information presented in the paper, such as information theory, the focus shifts to the engineering aspect when it comes to implementing the method discussed in the paper. Once the method was known, it became an engineering problem to design and build the hardware required for efficient branch prediction.

Q: In large design teams, what percentage of people see their work as engineering versus design and invention?

In many large design teams, there is a mix of people who see their work as engineering, design, or invention. Companies often reward employees for filing patents, which encourages innovation and invention. However, this can sometimes lead to a situation where everyone is focused on coming up with something new, often neglecting the basics of engineering and craftsmanship. Many breakthroughs and innovations actually come from improving and perfecting the simple foundational elements, rather than constantly pursuing new ideas.

Q: What are the challenges faced by big companies in balancing research and development in engineering and design domains?

Big companies often face challenges in balancing research and development efforts between engineering and design domains. To get promoted or recognized, employees are often required to come up with something new, leading to a focus on invention and innovation. However, this can neglect the basics, such as the quality of tools, software validation methods, and foundational elements. It's essential to prioritize both engineering and design aspects, as well as maintaining a balance between innovation and perfecting the existing building blocks.

Q: Do you think it was possible for Intel to pivot and win the mobile market?

Pivoting and winning the mobile market would have been a challenging task for Intel. As a big company with a focus on custom high-end processors, Intel's mindset and approach were more suitable for the PC and server market. The mobile market required a different set of skills, strategies, and approaches that Intel struggled to adapt to. Additionally, the mobile market was already dominated by companies like ARM, which had established themselves as leaders in mobile processor design. Pivoting successfully in such a competitive landscape would have required major changes in Intel's culture, strategies, and product development approaches.

Q: How would you compare the leadership styles of Steve Jobs and Elon Musk?

Steve Jobs and Elon Musk have different leadership styles. While both are known for their brilliance and passion, Steve Jobs was more focused on ideas, design, and intuition. He had a knack for selecting and working with talented individuals and translating his ideas into engineering products. On the other hand, Elon Musk is more engineering-centric and hands-on with the details. He has a strong passion for understanding and working on the technical aspects of his projects and believes in pushing the boundaries of what is possible. Both leadership styles have their value and can lead to remarkable innovations and breakthroughs.

Q: Is perfection the enemy of the good in hardware and software engineering?

Perfection can sometimes hinder progress in hardware and software engineering. A balance needs to be struck between striving for perfection and the practicality of getting things done. Too much emphasis on perfection can lead to delay and prevent the timely release of products or solutions. On the other hand, a complete disregard for quality and pursuit of perfection can result in flawed or unstable systems. The creative tension between striving for perfection and delivering practical solutions often leads to the best outcomes in engineering.

Q: What makes a design beautiful in computing hardware or software?

In computing hardware or software, a beautiful design often involves the thoughtful and effective use of abstraction layers. These layers allow different components to work together harmoniously while remaining independent and unaware of the specifics of other components. A well-designed system will have well-defined interfaces and modularity, enabling individual components to be developed, tested, and improved independently. Beautiful design is about finding the right balance between the limits of human cognitive capacity and the complexity of the modular system being built, resulting in a system that is both elegant and functional.

Q: Is there a limit to the complexity of systems that can be built with modular design?

There is a limit to the complexity of systems that can be effectively designed with modular design. As complexity increases, it becomes increasingly challenging for any individual or team to comprehend the system as a whole. Even in systems like Twitter, which appears to be a single computing system, it is built on numerous components and layers that no single person fully understands. The challenge is to strike a balance between the modularity required for efficient design and development and the need for integration and coordination among the various components and subsystems.

Summary & Key Takeaways

• Jim Keller discusses the dichotomy between theory and engineering in building software and hardware systems, emphasizing the importance of both in creating good designs.

• He explains how computer design is primarily driven by engineering and reduction of practice, but occasionally breakthrough ideas can lead to significant advancements.

• Keller highlights the role of modularity and craftsmanship in engineering, and the need to balance innovation with attention to basic design principles.