# Knowing

Deals with the process of gaining a familiarity, awareness, or understanding of someone or something.

 Random Link ¯\_(ツ)_/¯ Jan 1, 1976 » Toward a Theory of Medical Fallibility 5 min; updated Sep 2, 2021 Medical care is like the opposite of moving fast and breaking things. If it’s so taboo to admit error, then that could make errors more common because fewer people are learning from past errors. Norms for Scientific Activity and the Sources of Error “Science” is taken to mean “Natural Science”. Internal norms derive from a cognitive pursuit of science. They are: Focus on the central rather than the peripheral problems of the science in in question.... Dec 21, 2016 » Brainless Slime That Can Learn by Fusing [The Atlantic] 2 min; updated Sep 2, 2021 Brainless Slime That Can Learn by Fusing. Ed Yong. https://www.theatlantic.com/science/archive/2016/12/the-brainless-slime-that-can-learn-by-fusing/511295/ . https://old.reddit.com/r/todayilearned/comments/b245af/til_scientists_put_slime_mold_over_a_map_of_tokyo/ . Dec 21, 2016. Building Transit Networks Can a cell learn? When a part of the plasmodium touches something attractive, e.g. food, it pulses more quickly and widens. If a part meets something repulsive, like light, it pulses more slowly and shrinks. The article regards this as flowing in the best possible direction without conscious thought.... Jul 1, 2018 » [Summary] (Morgan Housel) Immeasurably Important 1 min; updated Sep 2, 2021 Immeasurably Important. Morgan Housel. http://www.collaborativefund.com/blog/immeasurably-important/ . Jul 5, 2018. Filtering out information is an art, not a science, necessitated by the information overload that we live in. Watch out for the tendency to only preserve information that meshes with how we think the world should be. If you think the world is all art, you miss how much stuff is too complicated to think about intuitively. If you think the world is all data, you’ll miss how much is too complicated to summarize in a statistic.... Jun 15, 2021 » Thoughts on Academic Research 4 min; updated Sep 2, 2021 How to Read a Paper The First Pass (5 - 10 min) Objectives: category, context, validity of assumptions, contributions and quality of writing. Carefully read the title, abstract, and introduction. Read the section and sub-section headings, but ignore everything else Glance at the math to determine the underlying theoretical foundations Read the conclusions. Mentally tick off references that you’ve already read. Most of the papers will not make it beyond this step.... Nov 23, 2016 » What is Ergodicity? 3 min; updated Sep 2, 2021 A random process is ergodic if all of its statistics can be determined from a sample function of the process. That is, the ensemble averages equal the corresponding time averages with probability one. Role of Ergodicity in Human Inference A newspaper has previously printed some inaccurate information, therefore, the newspaper is going to publish inaccurate information in the future. Fair. Ensemble of published articles is more or less ergodic.... May 2, 2020 » On Learning 2 min; updated Sep 2, 2021 Choose your content. Some material not worth the effort. Learning comes from repetition, but sometimes you just don’t care whether you’ll remember what you’ve read. But even if you don’t remember the specifics, the effect on your model of the world persists. As your mental model evolves, re-reading books is beneficial because the material compiles differently. When reading, annotate connections from previous knowledge, unanswered questions and unjustified assumptions. Make flashcards of facts/quotes that you wish to analyze.... Aug 2, 2021 » Misconstructions and Misconceptions 1 min; updated Sep 2, 2021 A collection of instances in which I believed something that wasn’t true. A reminder to read not to believe, but to weigh and consider . The Four Color Theorem does not claim that 4 colors suffice to color a planar map. Instead, 4 colors are sufficient to color any planar graph so that no two vertices connected by an edge are colored with the same color. For any $$n$$, there is a map that requires at least $$n$$ colors.... Sep 8, 2018 » Knowing (26 items) Pop Quiz: How Well Do You Know the World?; Rationality: From AI to Zombies [Yudkowsky, Eliezer]; 00. Introduction; How to Study [Swain, George Fillmore]; Brandolini's Bullshit Asymmetry Principle; Calling Bullshit [INFO 270]; Factfulness [Hans Rosling; Anna Rosling Rönnlund; Ola Rosling]; Informal Fallacies; Informal Fallacies; Overly Convenient Excuses; An Illustrated Book of Bad Arguments [Ali Almossawi]; 01. The Proper Mental Attitude; Formal & Red Herring Fallacies; Formal & Red Herring Fallacies; On Bullshit [Frankfurt]; 02. Studying Understandingly; Against Rationalization; Deeper Into Bullshit; 03. A System of Study; 04. Proper Habits and Methods of Work; Why People Are [Epistematically] Irrational About Politics; Misconstructions and Misconceptions; Thoughts on Academic Research; On Learning; A Kind Word for Bullshit: The Problem of Academic Writing; The Fine Art of Baloney Detection [Sagan]; Oct 10, 2017 » Caveats on Similarity Learning 1 min; updated Mar 14, 2021 Similarity-based learning is intuitive and gives people confidence in the model. There is an inductive bias that instances that have similar descriptive features belong to the same class. Remarkably so. When I think of classifying things, my mind immediately goes to NN. Similarity learning has a stationary assumption, i.e. the joint PDF of the data doesn’t change (new classifications do not come up). This assumption is shared by supervised ML.... Apr 17, 2007 » The Black Swan [Taleb, Nicholas Nassim] Oct 10, 2017 » Caveats on Similarity Learning 1 min; updated Mar 14, 2021 Similarity-based learning is intuitive and gives people confidence in the model. There is an inductive bias that instances that have similar descriptive features belong to the same class. Remarkably so. When I think of classifying things, my mind immediately goes to NN. Similarity learning has a stationary assumption, i.e. the joint PDF of the data doesn’t change (new classifications do not come up). This assumption is shared by supervised ML.... Dec 5, 2013 » An Illustrated Book of Bad Arguments [Ali Almossawi] Apr 3, 2018 » Factfulness [Hans Rosling; Anna Rosling Rönnlund; Ola Rosling] Jan 1, 1917 » How to Study [Swain, George Fillmore] Feb 1, 2015 » Rationality: From AI to Zombies [Yudkowsky, Eliezer]