The same patterns appears everywhere. The language is different - it's also within the language.
These similarities reveals the correlation - as symmetries; laws/formulas/algorithms.
These symmetries reveal the not-yet-discovered-things-and-properties.
Simplicity enables Maximum events (Information) per Time or Energy,
and this efficiency emerge (self-organising).
Events that hasn't happened is a sort of potential energy. Or Entropy is the weight past events.
What is the shape of all knowledge ?
As xkcd and others have suggested, if you repeatedly follow the first link presented in an article, you’ll always end up at Philosophy.
We found that articles consistently flow from specific to broader topics—similar to the banana’s path from fruit to biology, with most paths converging on a few topics. When we measured accumulation and influence, the dominant articles fit in one of three categories: academic disciplines (left hand), abstract notions (middle hand), and modern topics (right hand).
SCIENTIFIC CONCEPTS GENERALCONCEPTS HUMAN CONCEPTS
You’ll find a handful of articles floating in a word cloud. Philosophy, as suspected, is especially dominant, ranking first in influence by two orders of magnitude. After Philosophy come broad areas of knowledge: biology, health care, and “web page”.
Copied from: Site ----
On January 21st, 2017, the Women’s March on Washington gathered enormous crowds in collective protest of the newly inaugurated President of the United States, Donald Trump.
Chart types: FT
Sleep - twitter
Division of tasks + Efficient cooperation (between tasks) --> Most Effective output
Cooperation requires trust
DATA VISUALISATION CATALOGUE
Comparing these tools:
Act & React
Balance Of Power : The Equal Stake Model : "Fortune for Unfortunate."
"Power Corrupts, and Absolute Power Corrupts Absolutely"
"Freedom consists of the Distribution of Power, and Despotism in it's Concentration"
Neural Network v.s. Deep Learning
Link: https://t.co/f3t5ntFJve (via: https://twitter.com/hashtag/NeuralNetworks?src=hash)
Neural networks (NNs), recently referred to as deep learning, only work "effectively" with data that is produced from a process of a continuous function.
1. Classify - give it a name
2. Cluster - group-name
Criminal faces (10% fail-rate?)
Read the black-board
Machine learning is what I know best, so let’s talk about that for a minute. A very common kind of task in machine learning is classification. Let’s say we want to look at a picture and predict whether it’s a picture of a dog or a cat. Our model might say something like “there’s a 80% chance this image is a dog, and a 20% chance it’s a cat.” Let’s say the correct answer is dog – how good or bad is it that we only said there was an 80% chance it was a dog? How much better would it have been to say 85%?
Shockingly high numbers
Most people are would never believe that 50%,
of all New Yorkers are in poverty for at least One year.
Fathom Information Design, in Boston, MA
Growth in Sales and Income (salaries)
Sell your personal data
Lessons to young scientists