problem solving

Timothy Gowers thinks aloud, in real time, as he tries to understand why Pascal's determinant equals 1. He does not fully solve it in this episode, but shows his exploratory process.

Richard Hamming's 1995 recording of his talk 'You and Your Research', drawn from his graduate engineering course. He discusses how great scientists pick important problems and build careers, drawing on his years at Bell Labs.

Database pioneer Michael Stonebraker argues that 'big data' is really at least four different problems: big volumes with small analytics, big analytics, velocity, and variety. He comments on where each market is heading.

The first episode of Michael Sandel's Harvard course on moral reasoning, opening with the trolley problem and the choice between killing one to save five. Sandel draws out student arguments and introduces utilitarian and other approaches to justice.

The final lecture of Richard Hamming's Learning to Learn course, from 1995, based on his well-known talk 'You and Your Research'. It covers how to do important work and build a career.

Donald Knuth's 2017 Christmas Tree Lecture at Stanford about a combinatorial geometry problem whose computed numbers matched a striking formula. He tells the story of the conjecture and its sequel.

Noam Chomsky's 2011 University of Oslo lecture on Newton's contributions to the study of mind. He argues Newton abandoned mechanical explanation and lowered the bar for scientific intelligibility, with consequences for the limits of human understanding. Q&A starts at 45:33.

An excerpt from Russell Barkley's lecture on ADHD, focusing on inhibition, impulsivity and emotional regulation. It explains ADHD as a problem of response inhibition and emotional impulsivity.

James Mickens of Harvard gives a funny lecture at HBS's Future Assembly arguing that blockchains are a very bad idea. He covers their trust, legal and practical shortcomings for real-world problems.

A lecture by Richard Feynman on how computers work, using the analogy of a filing clerk that becomes progressively dumber and faster. It also covers heuristics and how computers handle data and solve problems in finite time.

Alan Kay's Stanford Startup School lecture covers his career at Xerox PARC and how to think about inventing the future. He goes through what came out of PARC and why companies fail to capitalize on such ideas.

Neil Postman's 1997 lecture at College of DuPage, based on his book, argues that Americans treat technology as a national religion and depend on it to solve larger human problems. It includes audience questions.

The introductory lecture of Richard Hamming's capstone course, The Art of Doing Science and Engineering: Learning to Learn. It focuses on style of thinking and on educating rather than training, using technical material only as illustration.

Erik Demaine's MIT 6.006 lecture introducing dynamic programming through Fibonacci numbers and shortest paths. It covers memoization, subproblems and optimal substructure.

A Royal Institution event with Sean Carroll and Jennifer Ouellette on what happens if you fall into a black hole, covering the firewall paradox, the information problem and the holographic principle.

Segment co-founder and CEO Peter Reinhardt recounts how his company found product-market fit, including how a product grew out of a small feature of another. It is a lecture from Stanford's Startup School course.

Turing Award winner Michael Stonebraker gives the 2018 Donald B. Gillies Memorial Lecture at Illinois. He discusses the gap between academic research and industry problems, publishing incentives, and the influence of large industry users on research.

Michael Betancourt's lecture at MLSS Iceland 2014 on efficient Bayesian inference with Hamiltonian Monte Carlo. It covers MCMC concepts and motivation and the problems of sampling in high-dimensional distributions.

Peter Norvig's UBC distinguished lecture on how massive amounts of simple data, such as for language modeling, can yield understanding that hand-built models could not.

Y Combinator CEO Michael Seibel lectures at Startup School on building a product people want: identifying the problem, the customer, an MVP and which customers to pursue first.

The introductory lecture of Stephen Boyd's Stanford Convex Optimization I course (EE 364A). It outlines convex sets, functions and problems, and the course's coverage of least-squares, linear and semidefinite programming, duality and interior-point methods.

A December 2005 guest lecture by Mark Zuckerberg in Harvard's CS50, discussing Facebook's early days, its technical choices and computer science. It captures him as a 21-year-old founder before Facebook's rise.