Knowing

Sample Use Cases LLMs are increasingly here to stay despite the reservations . How can I use them to enhance my capabilities? Look out for the Gell-Man amnesia effect. You prompt the LLM on some subject you know well. You read the response and see the LLM has absolutely no understanding of either the facts or the issues. In any case, you read with exasperation or amusement the multiple errors in the response, and then ask it about something else, and read the response as if it’s more accurate than the baloney you just read....

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. More crimes are committed by black persons than by white persons, therefore each individual black person is not to be trusted?...

Stories of Your Life and Others. Ted Chiang. 2010. ISBN: 9781931520898 . Tower of Babylon #religious-fiction #divine-retribution-motif Story focused on Hillalum, who lived during the construction of the Tower of Babel . notes that the Hebrew school version was more elaborate than the Old Testament account, e.g. the tower is so tall that it takes a year to climb, and when a man falls to his death, no one mourns, but when a brick is dropped, the brick-layers weep because it will take a year to replace....

Mental Attitude While Learning Distinguish Mere Facts From Conclusions or Opinions Discriminate between mere statements of facts, necessary conclusions which follow therefrom, and mere opinions which they seem to render reasonable. There’s no need to perform an experiment to verify that the atomic weight of oxygen is 16. That the sum of the angles of a plane triangle equals two right angles is not a mere fact, but an inevitable truth....

Going to a talk is difficult for everyone because nobody understands the whole thing, but it’s especially difficult for undergraduates because they still expect to. is a rich resource for understanding scholarly literature. Browse it. Some of the listed items are familiar, e.g. Google Scholar, SCImago, Sci-Hub, but it’d be informative to zoom out to the larger picture, e.g. good alternatives to Google Scholar. Why Even Read Papers?...

The Relativity of Wrong. Isaac Asimov. chem.tufts.edu . Sep 1, 1989. John, when people thought the earth was flat, they were wrong. When people thought the earth was spherical, they were wrong. But if you think that thinking the earth is spherical is just as wrong as thinking the earth is flat, then your view is wronger than both of them put together. The Flat Earthers Earth’s curvature is nearly 0....

Brainless Slime That Can Learn by Fusing. Ed Yong. www.theatlantic.com . old.reddit.com . 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....

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....

Immeasurably Important. Morgan Housel. www.collaborativefund.com . 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....

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....

Rationality: From AI to Zombies [Yudkowsky, Eliezer]; 01. The Case for the Scout Mindset; Brandolini's Bullshit Asymmetry Principle; Calling Bullshit [INFO 270]; Informal Fallacies; Informal Fallacies; Overly Convenient Excuses; An Illustrated Book of Bad Arguments [Ali Almossawi]; 02. Developing Self-Awareness; Formal & Red Herring Fallacies; Formal & Red Herring Fallacies; On Bullshit [Frankfurt]; 03. Thriving Without Illusions; Against Rationalization; Deeper Into Bullshit; 04. Changing Your Mind; 05. Rethinking Identity; Gödel, Escher, Bach: An Eternal Golden Braid; In Defense of a Liberal Education; 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]; [Summary] (Asimov) The Relativity of Wrong;

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....

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....