James Brien Comey Jr. (/ˈkoʊmi/; born December 14, 1960) is an American lawyer who was the 7th director of the Federal Bureau of Investigation (FBI) from 2013 until his dismissal in May 2017... In September 2013, President Barack Obama appointed Comey to the position of Director of the FBI.[7] In that capacity, he was responsible for overseeing the FBI's investigation of the Hillary Clinton email controversy. His role in the 2016 U.S. presidential election was highly controversial.[8] Some analysts and some Clinton supporters claim his decisions shortly before the 2016 election might have cost her the presidency,[9] particularly his decision to reopen the investigation into her emails less than two weeks before the election.[10][11][12] On June 14, 2018, DOJ inspector general Michael E. Horowitz released his report on the FBI's handling of the Clinton email investigation, which criticized Comey's actions during the 2016 election. https://en.wikipedia.org/wiki/James_Comey

for me, a word associated with Tennessee Williams' Cat On A Hot Tin Roof (boomed by Big Daddy as played by Burl Ives) There ain't nothin' more powerful than the odor of mendacity ... You can smell it. It smells like death. https://www.youtube.com/watch?v=GiiE-h9ZYag (more)

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.[2] Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. https://en.wikipedia.org/wiki/Deep_learning

Yann André LeCun[1] (/ləˈkʌn/ French pronunciation: ​[ləkɛ̃];[2] originally spelled Le Cun;[2] born July 8, 1960) is a French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics, and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University, and Vice President, Chief AI Scientist at Meta.[3][4] He is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets.[5][6] He is also one of the main creators of the DjVu image compression technology (together with Léon Bottou and Patrick Haffner). He co-developed the Lush programming language with Léon Bottou. LeCun received the 2018 Turing Award (often referred to as "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning.[7] The three are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning". https://en.wikipedia.org/wiki/Yann_LeCun

Yann LeCun on AI's Obvious Facts and Future. About the raging debate regarding the significance of recent progress in AI, it may be useful to (re)state a few obvious facts: (more)

Note-Taking process inspired by Niklas Luhmann Card-Deck approach. Similar to using a PrivateWiki (and WikiLog). (more)

Antifragile Writing course. How many notes and ideas get aisle, lost, or broken in what we write in our journal, notebooks, drafts, documents. or apps? How many concepts, stories, essays, not only survive the past of time but grow with it, get stronger, and generate new thoughts? Is there a writing capable to gain from the natural disorder of things? (more)

Note-Taking Apps

cf Schooling, goal of educating kids, school choice, Nutritionism (more tk, just creating page right now with bit pasted from a different page) (more)

Educating Kids is one of the big Nyc Challenges. (more)

Roy Fielding on URI, URL, WHATWG, interop. The WHATWG url spec defines a set of rules for interpreting references and placing them in a url data structure within browser memory. url != URL. href != URL. The spec says that this is somehow replacing URI, but it isn't even defining the same thing. The algorithms are designed to support 1997-era browser compatibility (even when there is no desire for that). The WHATWG spec uses the same name for (last I checked) five different concepts with five different sets of rules associated with them, each of which are very important for browser consistency. The specification is owned and controlled by four corporations, but isn't fully implemented by any of them. It aspires to be implemented.

Style of Splash Page copywriting that puts huge amount of info right on first page user sees, rather than splitting up into multiple connected pages. (more)

Home Page of a site driven by Internet Marketing. (more)

The uWSGI project aims at developing a full stack for building (and hosting) clustered/distributed network applications. Mainly targeted at the web and its standards, it has been successfully used in a lot of different contexts. Thanks to its pluggable architecture it can be extended without limits to support more platforms and languages. Currently, you can write plugins in C, C++ and Objective-C. The "WSGI" part in the name is a tribute to the namesake Python standard, as it has been the first developed plugin for the project. (more)

book series about a security cyborg who has hacked the chip that makes him controlled by his "owners" https://www.amazon.com/dp/B07FK8SNWY?binding=kindle_edition&ref=webs0e0 (more)

Method for visualizing the connections between pages in a WikiSpace, and ideally connections to other wiki spaces (Sister Sites). (more)

Last month is was bugging me that Mobile phones don't have return-to-owner info on the outside. Seems like an obvious need, regardless of whether people use "find/lock your missing phone" features. (more)

The Hawthorne effect is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed.[1][2] The effect was discovered in the context of research conducted at the Hawthorne Western Electric plant; however, some scholars feel the descriptions are apocryphal.[3] The original research involved workers who made electrical relays at the Hawthorne Works, a Western Electric plant in Cicero, Illinois. Between 1924 and 1927, the lighting study was conducted. Workers experienced a series of lighting changes in which productivity was said to increase with almost any change in the lighting. This turned out not to be true. https://en.m.wikipedia.org/wiki/Hawthorne_effect

Nov'2021: So we are now owners of a kitten, and will probably add a 2nd in the spring. (We originally intended to get both at the same time, which affected litter box planning.) (more)

Ron Jeffries: How to Impose Agile: ...with survival at stake, going “Agile” would be necessary (more)