machine language

Ian Piumarta's 2007 Stanford EE380 lecture on building a dynamic programming environment from the ground up, from the Viewpoints Research Institute's work. It covers the design of a language and system with a recursive design, parsing through code generation, and the COLA architecture.

Robert Sapolsky's Stanford lecture on language, comparing human and animal communication, ape language projects like Koko and Kanzi, and how language is acquired. It is part of his human behavioral biology course.

Brian Kernighan gives a talk at the University of Nottingham on how to succeed in language design without really trying. He draws on his experience with awk and other languages.

A 15-minute clip of linguist Stephen Krashen discussing language acquisition, drawn from the 1980s. It presents his input hypothesis and the distinction between acquisition and learning.

A lecture from the 1980s by linguist Stephen Krashen on how people acquire language. He explains his comprehensible input hypothesis for second-language acquisition.

The first lecture of Stanford's Natural Language Processing with Deep Learning course, taught by Chris Manning and Richard Socher. It introduces NLP and word vectors, including SVD, skip-gram, CBOW, negative sampling and word2vec.

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.

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.

An RSA Animate in which Steven Pinker explains how the mind builds infinite meanings from finite language, and why people often speak indirectly. It covers ambiguity, politeness and mutual knowledge, including the emperor's new clothes example.

An excerpt of Richard Feynman's 1985 lecture Q&A about whether machines can think. He discusses the difficulty of defining what computers can never do and compares machine intelligence to machines that don't copy animals.

Anders Hejlsberg whiteboards how compilers are taught versus how modern ones are actually built. He explains the shift to compilers as services with incremental, error-tolerant, IDE-oriented design, as in Roslyn and TypeScript.

A recording of Richard Feynman's talk on the possibilities of manipulating matter at very small scales, widely seen as an early vision of nanotechnology. This is a later version of his 1959 "There's Plenty of Room at the Bottom" lecture.

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.

David Shaw of D. E. Shaw Research presents Anton, a special-purpose machine that speeds up biomolecular simulations about a hundred-fold. The lecture covers molecular dynamics and how simulation helps understand how drugs bind.

A Bell Labs film in which Dr. J.N. Shive uses a mechanical wave machine to demonstrate reflection, standing waves, resonance and impedance matching. He includes some equations but relies mostly on real-world examples a layperson can follow.

Cognitive scientist George Lakoff lectures on how political debates are framed through language and metaphors tied to values, and on the moral worldviews behind progressive and conservative politics. It was given in 2005 at UC San Diego.

The first lecture of Gilbert Strang's MIT 18.06 Linear Algebra course, covering the geometry of linear equations. It opens a full 35-lecture university course.

Simon Peyton Jones gives Churchill College's annual computer science lecture on Haskell's birth and evolution. He covers the most influential ideas, from laziness to monads and type system evolution, plus current developments and guesses about the future.

Tom Wildenhain's guest lecture for CMU's Esoteric Programming Languages course shows that PowerPoint transitions and slides are Turing complete without macros, building up to a mind-bending fractal ending.

The first lecture of Michael Sipser's MIT 18.404J Theory of Computation course, covering course mechanics, finite automata, regular languages and regular expressions. It proves closure of regular languages under union and begins concatenation.

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.

The final part of Dan Gelbart's 18-part Building Prototypes series, focused on mechanical design. It opens with a demonstration of stiffness and covers practical design principles for prototype machines.

Andrej Karpathy builds the GPT tokenizer from scratch, implementing byte pair encoding along with encode and decode. He shows how many odd LLM behaviors trace back to tokenization, and covers Unicode byte encodings along the way.

Leo Dirac explains how transformer models have replaced LSTMs in NLP. He gives background from bag of words through RNNs and LSTMs, then a technical deep dive into multi-headed self-attention and positional encoding, with sample code.

Geoffrey Hinton gives an MIT talk on what is wrong with convolutional neural networks and introduces his capsules project. The description points to the capsule network papers.

The first lecture of Stanford's CS231n course on convolutional neural networks for visual recognition. It surveys the history and key challenges of computer vision, from the Cambrian explosion and Hubel and Wiesel to ImageNet.

Lecture 7A from MIT 6.001 (SICP) by Abelson and Sussman introduces the metacircular evaluator, a Scheme interpreter written in Scheme. It shows how evaluating a program is itself a program.

Lecture 1B of MIT's 6.001 Structure and Interpretation of Computer Programs (1986) with Gerald Sussman, covering procedures, processes and the substitution model. It includes the line about the power of naming things.

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.

Lecture 7A of the 1986 MIT 6.001 SICP course, with Gerald Sussman presenting the metacircular evaluator, part 1. It shows how a Lisp evaluator can be written in Lisp itself.

Andrej Karpathy's three-hour-plus general-audience deep dive into how ChatGPT-style LLMs are built, covering the full training stack from pretraining to post-training. It also gives mental models for their quirks, like hallucination, and how to use them well.

Irving Finkel of the British Museum lectures at the Royal Institution on the invention and decipherment of cuneiform writing in Mesopotamia, and why it is so hard to translate.

A short Harvard Online animated lesson on how the mitochondrial electron transport chain works. It shows proton pumping and the machinery that drives ATP synthesis.

The first lecture of David MacKay's Cambridge course on information theory, pattern recognition and neural networks. It introduces the core ideas of his book Information Theory, Inference, and Learning Algorithms.

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.

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.

A roughly three-hour San Francisco workshop by Marshall Rosenberg on Nonviolent Communication, with full English subtitles added by a fan. He teaches the model with his jackal and giraffe language examples.

Chris Granger, creator of Light Table and the Eve language, gives a Berkeley data science lecture on what computation and programming might look like in ten years. He covers the path that led to Eve and ends with demos.

The first lecture of Stanford's CS107 Programming Paradigms, taught by Jerry Cain, giving an overview of the course. The course goes from C and assembly through C++, concurrency, Scheme and Python.

Ron Vale of UCSF and HHMI gives an introductory lecture on kinesin, dynein and myosin motor proteins. He covers their roles in cells and how they turn ATP hydrolysis into directed motion and force.