Alan Turing's proof of the impossible  Cal Newport and Lex Fridman  Summary and Q&A
TL;DR
Computer science and mathematical theory explore the concept of impossibilities and set limits on what algorithms can achieve.
Questions & Answers
Q: What is the speaker's specialty within computer science?
The speaker specializes in impossibility results within distributed algorithms, focusing on problems that cannot be solved by any algorithm.
Q: How does Turing's work connect to the foundation of theoretical computer science?
Turing's concept of computable numbers and his exploration of the halting problem laid the foundation for all of theoretical computer science, showcasing the limits of algorithmic solutions.
Q: Why are impossibility proofs satisfying in computer science?
Impossibility proofs make conclusive statements about problems, showing that no algorithm can ever solve them. This is philosophically interesting and satisfying, as it sets firm limitations on what can be achieved.
Q: How does mathematics and Cantor's diagonalization argument relate to computer science?
Mathematics, specifically Cantor's diagonalization argument, shows that there are infinitely more problems than there are algorithms. This means that most problems are not solvable by a computer.
Summary & Key Takeaways

Computer science and mathematical theory inspire deep thinking about productivity and explore various angles.

The speaker specializes in impossibility results within distributed algorithms, which highlight unsolvable problems.

The theoretical foundation of computer science is rooted in Turing's computable numbers and the halting problem.