Richard Karp: Algorithms and Computational Complexity | Lex Fridman Podcast #111 | Summary and Q&A

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July 26, 2020
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Lex Fridman Podcast
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Richard Karp: Algorithms and Computational Complexity | Lex Fridman Podcast #111

TL;DR

Richard Carp, a professor at Berkeley and a well-known figure in theoretical computer science, discusses the beauty of combinatorial algorithms, the P vs. NP problem, and the challenges of achieving human-level intelligence in machines.

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Questions & Answers

Q: What are some examples of combinatorial algorithms and their applications in real-world problems?

Some examples of combinatorial algorithms include the assignment problem, where individuals or objects need to be matched, scheduling problems, network flow optimization, and graph problems like finding cliques or independent sets. These algorithms have applications in resource allocation, logistics, network design, and many other areas where efficient optimization is needed.

Q: What is the significance of the P vs. NP problem in computer science?

The P vs. NP problem is one of the most important questions in theoretical computer science. Its resolution would have profound implications for algorithmic efficiency and cryptography. If it is proven that P is equal to NP, it would mean that efficient algorithms can be found for many challenging problems. On the other hand, if P is proven to be unequal to NP, it would suggest that there are fundamental limits to algorithmic efficiency and certain problems may be inherently hard to solve.

Q: How does reduction play a role in solving computational problems?

Reduction is a technique used to show that one problem is at least as hard as another. By reducing one problem to another, we can demonstrate that if the second problem can be solved efficiently, then the first problem can also be solved efficiently. This allows us to understand the complexity relationships between different problems and identify the hardest problems in a given class.

Q: Why is it challenging to achieve human-level intelligence in machines?

Human-level intelligence encompasses many complex cognitive abilities, such as reasoning, learning, emotion, intuition, and self-awareness. While current AI systems excel at specific tasks and exhibit impressive performance, they often lack the broader understanding and adaptability of human intelligence. The gap between specific task performance and general intelligence remains a significant challenge in AI research. Understanding the principles of human cognition and developing algorithms that can replicate these abilities is a complex and ongoing endeavor.

Summary

In this conversation with Richard Carp, a renowned professor at Berkeley and a key figure in theoretical computer science, the discussion delves into the history of theoretical computer science and the development of combinatorial algorithms. Carp shares his journey from being mesmerized by plain geometry to his work on computational algorithms. The conversation also touches upon the concepts of polynomial time algorithms, the classes P and NP, and the central question of whether P equals NP.

Questions & Answers

Q: What initially intrigued Richard Carp about plain geometry?

Carp was fascinated by the power and elegance of formal proofs in plain geometry. He found it compelling that pure reasoning could establish facts about geometry beyond dispute.

Q: Can Carp recall any specific problems or proofs from plain geometry that he found mesmerizing?

Carp shares Michael Rabin's story about finding the shortest distance between non-overlapping circles. Rabin's solution involved drawing a straight line between the centers of the circles, and Carp found it elegant and convincing.

Q: What does Carp find compelling about the elegance of geometric proofs?

Carp appreciates the ability to establish facts about geometry through pure reasoning. He also enjoys the challenge of solving puzzles in plain geometry, finding it more enjoyable than earlier mathematics courses.

Q: Was there anything about geometry itself that Carp particularly enjoyed?

Carp found the slightly visual component of geometry appealing, although he admits lacking three-dimensional vision. The ability to prove surprising facts, such as the sum of angles in a triangle being 180 degrees, was intriguing to Carp.

Q: Did Carp's interest in geometry impact his work on combinatorial algorithms?

Carp mentions relying more on algebraic properties and tools like linear programming and integer programming when designing algorithms. He doesn't have a strong high-dimensional visualization capability, so he leans more towards algebra and algebraic interpretation.

Q: How does Carp visualize algorithms and mathematical problems?

Carp explains that he envisions algorithms as involving a repetition of an inner loop. He visualizes the distance from the desired solution iteratively reducing until reaching the exact solution. The mechanics of the algorithm are usually simple, but seeing the successive approaches to the minimum or optimum fascinates Carp.

Q: Carp mentions working on the traveling salesman problem. What intrigued him about the problem and its progression towards the optimum?

Carp discusses the fascination of seeing a particular function that needed to be minimized, and the successive approaches to the minimum or optimum caught his attention. He found it captivating to witness the progress made towards the optimal solution while solving the problem.

Q: Don Knuth introduced the term "geeks" for people who enjoy contemplating the structure of computational processes. What does Carp think about this term and the beauty of mathematics?

Carp expresses his love for the orderly and systematic nature of innovative algorithms, finding it pleasing like shaping something beautiful and orderly through woodworking. He agrees that there's something compelling about mathematics and puzzle-solving, providing a sense of achievement and escape from the messiness of the real world.

Q: Carp joined Harvard's computational lab, where the Mark I and Mark IV computers were built. Can Carp describe these computers?

Carp describes the Mark IV computer as a large room-sized machine with rows of relays, where one could walk around inside it. While bugs in the system could cause failures, Carp's experience with these machines was limited, as the lab eventually acquired a commercial computer called the UNIVAC 2000.

Q: Carp joined the computational lab in the mid to late 1950s. Did he foresee the significant impact computers would have on the world?

Carp acknowledges that there was a strong sense in the lab that computers were the wave of the future, but he couldn't have predicted the extent to which computers would become part of everyday life. He didn't anticipate personal computing or the prevalence of computers in various aspects of society.

Q: Was Carp more interested in computers as mathematical tools for analysis or as potential artificial intelligence machines?

Carp states that he was more interested in the underlying algorithms than the actual physical world implementation of computers. Artificial intelligence wasn't a significant focus for him, although he did read Alan Turing's paper on computing and intelligence.

Q: Do Carp's views align with the Turing test for determining machine intelligence?

Carp considers the Turing test too subjective and not the right way to calibrate the intelligence of an algorithm. He believes that the achievements in speech, robotics, and natural language processing currently fall short of the level of cognition seen in humans.

Q: Carp is doubtful about achieving human-level intelligence with computers. What are his reasons for this doubt?

Carp points out that current achievements in AI operate within a limited set of ground rules and for specific tasks. They lack the high-dimensional intuition, emotions, physical attributes, desires, and intuition that humans possess. He sees a significant disparity between AI accomplishments and the complexity of human intelligence.

Q: Can computational complexity theory help determine if achieving human-level intelligence with computers is possible?

Carp believes that the complexity theory's central question of whether P equals NP is crucial to understanding the limits of computational power. If P were equal to NP, it would mean every problem that is easy to check can be solved efficiently in polynomial time, but Carp is doubtful that this is the case based on the lack of polynomial time algorithms for well-studied problems.

Q: Carp explains polynomial time algorithms and the classes P and NP. Can he provide a simple definition of these terms?

Carp defines polynomial time algorithms as those with running time bounded by a polynomial in the size of the input. The class of problems solvable in polynomial time is called P. NP, on the other hand, refers to problems that are easy to check, but their efficient solution is challenging. It includes problems in which a solution's correctness can be verified quickly.

Q: What is the relationship between NP and P in terms of problem complexity?

Carp emphasizes that every problem in P is also in NP because if a problem can be solved efficiently, its solution can be verified efficiently as well. However, it's unclear whether problems in NP also lie in P. Carp explains that intuitively, NP encompasses a larger set of problems that are easy to check but may not be efficiently solvable.

Q: Are there any problems in NP that are equivalent to each other, possibly indicating whether P equals NP?

Carp mentions Stephen Cook's work on the satisfiability problem of propositional logic. This problem is as hard as any problem in class P, implying that a large number of problems within NP are equivalent. If P were equal to NP, either all or none of these problems would lie in P.

Q: What is the significance of P equaling NP or not in terms of solving combinatorial problems?

Carp explains that if P were equal to NP, it would mean most, if not all, standard combinatorial problems would have polynomial time solutions. On the other hand, if P is unequal to NP, most combinatorial problems, including those that have been extensively studied, would not have efficient polynomial time algorithms.

Takeaways

Richard Carp's conversation touches on the history of theoretical computer science, the beauty of mathematics, and the complexity of combinatorial algorithms. Carp's doubts about achieving human-level intelligence with computers and the central question of whether P equals NP highlight the ongoing quest to understand the limitations of computational power. While polynomial time algorithms offer efficiency, many well-studied problems remain without efficient solutions, contributing to the allure and mystery of computational complexity.

Summary & Key Takeaways

  • Richard Carp shares his fascination with the elegance and power of combinatorial algorithms, which involve discrete objects arranged to minimize a cost function or prove a combinatorial property.

  • He explains the concept of polynomial time algorithms (P) and non-deterministic polynomial time algorithms (NP), highlighting how problems in NP are easy to check but may be hard to solve efficiently.

  • Carp discusses the importance of the P vs. NP problem, which asks whether all problems that can be verified in polynomial time (NP) can also be solved in polynomial time (P).

  • He shares his doubts about P being equal to NP, citing the lack of efficient algorithms for many well-studied problems in NP, such as factoring large numbers.

  • Carp explores the concept of reduction, where one problem can be transformed into another, and how reductions have been used to map problems to the satisfiability problem and the clique and independent set problems in graph theory.

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