learning from data

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.

The first lecture of Caltech's CS 156 Machine Learning course by Yaser Abu-Mostafa, introducing the learning problem and supervised, unsupervised and reinforcement learning. It is one of 18 lectures in the Learning from Data course.

A Business Insider investigation into the US boom in AI data centers, mapping where they are, who owns them, and how much power and water they consume. It looks at the costs borne by communities, with a focus on Virginia.

Tantacrul examines sonification, the practice of turning data into sound or music, and critiques how scientists and musicians, including NASA and CERN projects, use it well and poorly.

Andrew Ng presents unsupervised feature learning and deep learning, showing how algorithms can learn representations from unlabeled data instead of hand-designed features. He gives examples in vision and audio, including visualizations of features learned by successive layers.

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.

Raymond Hettinger explains Python's dataclasses module, showing the problem it solves and its key design decisions. He places it in a progression from tuples and dicts through named tuples and attrs.

The first lecture of Andy Pavlo's CMU 15-445 Intro to Database Systems (Fall 2019), covering course introduction and the relational model.

Official music video for Afroman's song Lemon Pound Cake, built from footage of a police raid on his home. It became a viral hit and was at the center of a lawsuit by the officers.

Machine Learning Street Talk interviews Paul Lessard about Categorical Deep Learning, which uses category theory and monads to make neural network architectures more interpretable and composable. They also discuss neural networks' limits in reasoning and unbounded computation.

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.

Benn Jordan investigates infrasound and low-frequency noise from data centers, including xAI's Colossus and a crypto mine, and reports his own experiments on the effects. The video covers the health and community impacts on nearby residents.

Max Tegmark's MIT CBMM talk on why deep learning works so well. He argues that the laws of physics and the symmetries in physical data make it compressible into small neural networks, tying physics to network structure.

John Ousterhout's Netdev 0x16 keynote arguing that nearly every aspect of TCP is wrong for the datacenter, from connections and streams to reliable delivery and congestion control. He presents the case for a replacement protocol.

Scott Manley examines why SpaceX, Google and others are looking at orbital data centers. He walks through the economics, heat rejection and solar power arguments, with links to the Lumen/Starcloud and Google Suncatcher papers.

Scott Manley explains the thermal balance of spacecraft and how a satellite could be configured to radiate away the heat from datacenter-scale computing in space. His rough numbers suggest it is feasible.

Geoff Hinton's 2010 Google Tech Talk on deep belief networks, learning layers of representation one at a time from unlabeled data, and extending them with multiplicative interactions.

An LGR Oddware episode looking at Sony's MDH-10 MD Data drive from 1995, which used MiniDisc for computer storage years before Hi-MD. The host tries out the drive with its original drivers and software.

James Powell's nearly two-hour PyData talk builds up Python's data model, decorators, generators and context managers, coding live in Vim. It aims to take intermediate programmers toward real Python expertise.

The first lecture of Andrew Ng's Stanford CS229 Machine Learning course, giving an overview of the course. It introduces supervised and unsupervised learning, learning theory and reinforcement learning.

Mike Acton of Insomniac Games argues that a program's purpose is transforming data, and shows how common C++ approaches work against that in performance-critical game development. He covers cache behavior, data layout and the limits of compilers.

The first lecture of David Silver's reinforcement learning course, introducing the field and its basic concepts. The course follows the Sutton and Barto textbook.

Juan Pedro Bolivar Puente explains immutable and persistent data structures in C++ and how they support value semantics. He presents the Immer library, rrb-trees, and a prototype text editor that opens a huge file instantly.

A five-minute lightning demo of VisiData, a terminal tool for exploring and manipulating data. It uses two months of NYC 311 complaint data to drill down, plot points on a virtual map and save selected rows.

John Oliver's Last Week Tonight segment explains how data brokers collect and sell personal information, including a stunt in which his team bought data to identify members of Congress.

Valve's John McDonald presents at GDC 2018 on how Valve uses deep learning (VACnet) to detect cheaters in Counter-Strike: Global Offensive. He covers training on human-reviewed replays and how model output is confirmed by manual review.

Lee Byron of Facebook explains how persistent immutable data structures work and how to use them in React apps with Immutable.js. He covers memoization benefits and avoiding coupling through shared mutable state.

The first episode of Google's Machine Learning Recipes series. It introduces machine learning and builds a first supervised-learning classifier in about six lines of Python using scikit-learn.

Rich Hickey, creator of Clojure and designer of Datomic, gives a JaxConf 2012 talk that takes apart traditional database architecture. He presents a different way to think about databases and explains the reasoning behind Datomic's fact-based design.

A GDC 2018 talk by Valve's John McDonald on VACNet, a deep learning system that analyzes CS:GO gameplay to detect cheaters. It covers how the model is trained on human-reviewed Overwatch cases and how its output feeds human moderation.

Mark Rober and Jabril build an app that uses machine learning to decode baseball sign sequences. The video also explains neural networks in a simple, accessible way.

Eager Space examines the practicality of putting data centers in orbit, walking through three scenarios with cost calculations and the launch cost reductions that would be required.

Scott Manley analyzes the idea of putting data centers in orbit, prompted by Lumen Orbit's funding. He examines the claimed advantage of constant solar power against launch cost, cooling and mass.

UC Berkeley EECS colloquium by OpenAI's John Schulman on reinforcement learning from human feedback. He covers why RL helps with truthfulness, hallucination, and the limits of behavior cloning.

Edward Kmett's 2013 talk at Prezi in Budapest on succinct data structures, which store data in close to the information-theoretic minimum space while supporting fast queries. It ties together the research and walks through his Haskell succinct library.

Jon Gjengset's 2020 PhD dissertation presentation at MIT on partial state in dataflow-based materialized views, the idea behind the Noria database. The Q&A is cut out.

A Google Tech Talk by Laurens van der Maaten, co-creator of t-SNE, on visualizing high-dimensional data by mapping it to two dimensions. It explains how the technique works and how it compares with other visualization approaches.

Dwarkesh Patel interviews Andy Matuschak on how he learns, why memorization matters for understanding, and why most learning tools fail. The conversation also covers handling confusion and the role of tutoring.

Linus Tech Tips documents a major RAID failure on its Whonnock server and the attempt to recover the lost data with a data recovery firm.

James Hamilton of AWS gives a Velocity 2010 keynote on datacenter infrastructure innovation, focused on power and cooling costs and a breakdown of where datacenter spending goes.