#tools #writing
https://www.losethevery.com/
> "Very good english" is not very good english. Lose the very.

#datascience

I ran into this hilarious comment on pie chart in a book called The Grammar of Graphics.

“To prevent bias, give
the child the knife and someone else the first choice of slices.” 😱😱😱

#showerthoughts
As human beings, we read or hear about facts of something. These are our priors. Our belief is then updated based on observation of data, aka, likelihood. Some people abide by the priors, they are the prior-people, while others are more like likelihood-people and easily change their belief based on observations.
There is a third type. They combine priors and likelihood. Change belief based on likelihood is prone to biases in data. By combining priors and likelihood, they have a better chance of getting to the right conclusion.

#data #covid19

UK gov has an official covid 19 API. https://coronavirus.data.gov.uk/details/developers-guide#structure-metrics

I found this funny typo in the documentation. 😂 The first one should be cumCasesByPublishDateRate.

https://www.nature.com/articles/s41557-020-0544-y

> Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons

what the f
ECDC has been like this for at least 2h.
Am I time travelling back to 2000? AND this is about disease prevention and controll not useless blogs...

#data

Could you prevent a pandemic? A very 2020 video game
https://play.acast.com/s/nature/2020festivespectacular

#neuroscience

Source:
https://science.sciencemag.org/content/370/6523/1410.full
A gatekeeper for learning

> Upon learning a hippocampus-dependent associative task, perirhinal inputs might act as a gate to modulate the excitability of apical dendrites and the impact of the feedback stream on layer 5 pyramidal neurons of the primary somatosensory cortex.

😲 In some sense, perirhinal inputs are like config files for learning.

https://github.com/volotat/DiffMorph
#machinelearning #opensource

Differentiable Morphing

> Image morphing without reference points by applying warp maps and optimizing over them.

https://blog.waymo.com/2020/10/waymo-is-opening-its-fully-driverless.html

If you are in Phoenix.

#intelligence

https://www.economicprinciples.org/

How the economic machine works make easy

#machinelearning

A nice colloquium paper:
The unreasonable effectiveness of deep learning in artificial intelligence | PNAS
https://www.pnas.org/content/117/48/30033

#intelligence #paper #ML
Superintelligence Cannot be Contained: Lessons from Computability Theory
https://www.jair.org/index.php/jair/article/view/12202

> We argue that total containment is, in principle, impossible, due to fundamental limits inherent to computing itself. Assuming that a superintelligence will contain a program that includes all the programs that can be executed by a universal Turing machine on input potentially as complex as the state of the world, strict containment requires simulations of such a program, something theoretically (and practically) impossible.

Twitter suspends Sci-Hub account amid Indian court case - The Verge
https://www.theverge.com/2021/1/8/22220738/twitter-sci-hub-suspended-indian-court-case

补充一部讨论互联网审查的工作生产、全球影响、社交媒体加剧冲突等议题的德国纪录片 The Cleaners（原名 Im Schatten der Netzwelt，网络阴影之下）。Hans Block 和 Moritz Riesewieck 执导，他们也在 TED 上 讲述了 关于「数字清洁」（digital cleaning）的问题。不过我第一次看这部纪录片，是在 DW 的 YouTube 频道，分为 上 、下 两集播出，目前均已失效，原因不明。一部讲述内容被删除的影片，自身却（或被）删除，不论是因著作权还是其他原因都表现出足够的讽刺。Internet Archive 上仍可找到 播出日 的 存档 回看，也可 在此 下载观看。

#fun
Source: https://www.linkedin.com/posts/muhammed-kara_i-received-a-t-shirt-from-national-cyber-activity-6752966271626743808-yO6y/

#dev
Analysis of the NoSQL Landscape - All About the Code
http://blog.knuthaugen.no/2010/03/the-nosql-landscape.html

#productivity

I have been using Obsidian as my primary note-taking app for a while. It was a rough start. Linking notes was simply not in my workflow. In some sense, I am not familiar with my notes after a while. So I started to work on notes reviews every two weeks. On each notes review, I go through my notes inbox and spend some time connecting them with the the existing ones.

This is how my notes look like now. They are mostly well connected. (The cluster is because I have archived them as they are the notes for my previous position.)

I also borrowed the domain concept from dendron. I created folders with dot delimited domains. For example, I have this folder named inbox.ml which I use as my inbox for machine learning related notes. These notes will be distributed to a corresponding folder during my notes review.

Those notes worth publishing will then be distributed to my websites. For example, https://datumorphism.leima.is/ is for data science related notes.

#fun

https://observablehq.com/@mbostock/hertzsprung-russell-diagram

Mike Bostock made a Hertzsprung–Russell Diagram using d3.js. It looks so cool.