biological computing

A Stanford EE380 seminar by Michael P. Frank of Sandia National Laboratories on generalized reversible computing. It surveys unconventional computing approaches as transistor scaling nears its limits and energy efficiency stops improving.

Chris Shore of the Centre for Computing History explains how Colossus was built, how it worked, and how it affected the course of World War II. It is an hour-long museum lecture on the codebreaking computer.

A 2013 lecture in which Vitalik Buterin explains what quantum computers are and what they can and cannot do. He then discusses what they could mean for Bitcoin's cryptography.

A lecture by Commodore Grace Hopper on the future of computing, posted by MIT Lincoln Laboratory. It includes her well-known nanosecond wire demonstration and humorous comparisons of device latencies.

Robert Sapolsky's Stanford lecture on the biological underpinnings of religiosity, from his human behavioral biology course. It covers theories linking religion to stress, and schizotypal traits.

Douglas Crockford's first Yahoo lecture puts JavaScript in historical context. He traces the language's structure and quirks back through the early decades of computer science and programming language history.

A long Mill Computing lecture on the Mill CPU's compiler toolchain, covering the specializer that targets family members, scheduling and latency passes, and the belt. It explains how the compiler handles a wide-issue, exposed-pipeline design.

The second talk in the Mill CPU series by Mill Computing, covering the Belt, the Mill's operand-passing model that replaces general registers. It explains the cost of register renaming in conventional wide CPUs and how the Belt reaches high instruction-level parallelism.

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.

Part two of Alan Kay's Stanford Startup School lecture on inventing the future. He draws on his time at Xerox PARC and elsewhere to discuss how to think about new technology and what makes ideas useful.

John Gustafson presents posits, a proposed drop-in replacement for IEEE 754 floats that is simpler in hardware and more flexible in precision. The seminar includes a live demo and machine learning results.

Part one of Capt. Grace Hopper's 1982 NSA lecture on the future of data, hardware, software and people. It includes her humor, her nanosecond demonstration and views on scaling and resisting old ways of doing things.

Hennessy and Patterson's 2018 Turing Award lecture at ISCA, A New Golden Age for Computer Architecture. It covers domain-specific hardware, security, open instruction sets and agile chip development.

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.

Stanford professor Robert Sapolsky lectures on depression as a real biological disease. He covers its biological and psychological causes, how it differs from ordinary sadness, and ideas such as learned helplessness. He ties these together into one account.

The first lecture in Martin Kleppmann's 8-lecture distributed systems series from the University of Cambridge undergraduate course. It introduces the subject, and accompanying lecture notes are available.

Admiral Grace Hopper holds up a length of wire to show how far light travels in a nanosecond, about 11.8 inches. She explains how she made the idea concrete to people who could not picture a billionth of a second.

A 73-minute Stanford talk by Robert Sapolsky drawn from his book Behave. It surveys the biological factors behind human behavior at its best and worst.

Geoffrey Hinton's 2025 Royal Institution Discourse on what AI has learned from biological intelligence and how it might surpass it. He explains neural networks and argues that human language use resembles what backpropagation-trained networks do.