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.

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 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.

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.

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.

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.