precision engineering

Part 10 of Dan Gelbart's prototype-building lecture series, covering flexures: flexible joints that give precise, frictionless motion. It shows how a cheap piece of aluminum can become a nanometer-accurate mechanism.

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.

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.

An MIT colloquium by Dennis Whyte on power exhaust and first-wall cooling in fusion reactors, including recent innovations and the SPARC and ARC designs.

The opening lecture of Steve Brunton's Control Bootcamp, covering the kinds of control and why closed-loop feedback matters. It starts a series that follows the control chapter of the Brunton and Kutz data-driven science book.

Weber State's Professor Kelly explains the Toyota RAV4 Hybrid transaxle, including its planetary gearset eCVT and all nine operating modes. It also covers how the engine reaches nearly 40 percent thermal efficiency.

Sacha Arnoud, Waymo's Director of Engineering, gives a lecture in MIT's 2018 deep learning for self-driving cars course on the rise of machine learning in autonomous vehicles. It covers how Waymo uses deep learning and how it handles tricky driving situations.

Nathan Kutz presents the spatio-temporal dynamics of rotating detonation rocket engines, including combustion fronts that behave like solitons. He shows a bifurcation analysis of a multiscale model that captures the main features.

Historian and journalist Gwynne Dyer gives a long UBC talk on how climate change will reshape international politics. He covers food, borders, refugees and the possibility of unilateral geoengineering by affected nations.

First lecture of Stanford's CS106A Programming Methodology by Mehran Sahami, giving a course overview and starting on programming. The course teaches software engineering principles using Java.

MIT 22.01 lecture by Michael Short explaining the Chernobyl accident technically, including RBMK reactor positive feedback, xenon poisoning and the cascade of human errors during the test.

Bill Dally of NVIDIA gives a University of Washington lecture on trends in deep learning hardware. It covers numeric formats, specialized instructions, energy per operation and co-design of hardware and software.

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.

Energy scholar Vaclav Smil argues that global energy transitions are slow and that renewable targets should not rest on wishful thinking. He appraises how energy dependencies are likely to change and asks for using much less.

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.

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.

Rick Hartley gives a roughly two-hour live training on proper grounding in PCB design. He explains how energy actually flows in the space between conductors, and how that governs noise, EMI, return paths and signal reflections.

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.

Jeff Dean of Google describes his experiences building Google's systems and how its infrastructure evolved over time. The Stanford talk covers search, scaling tipping points and lessons from building large-scale software.

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.

An excerpt from Hal Abelson's opening SICP lecture introducing computer science and contrasting declarative and imperative knowledge. He argues that computer science is not really a science and is more like magic or engineering of ideas.