All of the #Prophet documentation is generated from Jupyter notebooks, and we even run Python and R code in the same notebook to show that results are equivalent. Jupyter is the best implementation so far of Knuth’s literate programming concept. https://t.co/paVamPqz9V
— Sean J. Taylor (@seanjtaylor) August 6, 2019
Drew Endy, one of the OG synthetic bio folks, has a great talk about this targeting programmers.https://t.co/wunLeUyv5R
— Stephen Malina (@an1lam) August 6, 2019
The human mind treats a new idea the same way the body treats a strange protein; it rejects it. — P. B. Medawar
— hardmaru (@hardmaru) August 5, 2019
From “Publishing a new idea” https://t.co/0Dz3fkAQeG
You are misunderstanding the nature of influence. It is not driven by argument. There's a lot of great research showing how humans make decisions - it's important to be familiar with it in order to learn the real lessons of history. (eg Hitler was given a global media platform)
— Jeremy Howard (@jeremyphoward) August 4, 2019
A dream you dream alone is only a dream.
— hardmaru (@hardmaru) August 4, 2019
A dream you dream together is reality.
— John Lennon
Not being given space in the New York Times is not suppression of speech. If it was, most of us (including me) would be having our speech suppressed. Being in NYTimes provides Thiel with additional reach & legitimacy.
— Rachel Thomas (@math_rachel) August 3, 2019
This new paper clustering the global labor network is really rad. It's cool how much structure pops out when you apply the right model. Great work @vagabondjack @yy et al.!https://t.co/HbugVlTCjj pic.twitter.com/F4YVfNPJY7
— Sean J. Taylor (@seanjtaylor) August 2, 2019
Universal Laws of the World
— hardmaru (@hardmaru) August 2, 2019
• In a world with 7 billion people the odds of a one-in-a-billion event are pretty good.
• When a measure becomes a target, it stops being a good measure.
• No scientific discovery is named after its original discoverer.https://t.co/bbXZi4SCJD
And now a video of the @nytimes panel is available online: "Why You Should Care About Privacy"https://t.co/QvWfucQ1lV
— Rachel Thomas (@math_rachel) August 1, 2019
Academia rewards hyper-specialization. Scholars narrow their interests until only a handful of people in the world can understand them.
— Arvind Narayanan (@random_walker) August 1, 2019
That means you can make ground-breaking discoveries and contributions if you have even an undergrad-level fluency in two or more fields.
Ian Hathway finds the unsurprising result that since 2015 there's less funding of startups in spaces occupied by Google, Facebook and Amazon. Also unsurprising, but disgraceful, he finds that economists paid by Facebook cooked books to find the opposite https://t.co/xFfjgmJ5ee
— Tim Wu (@superwuster) July 31, 2019
Stitch Fix’s data science-centric culture is a case study for how to build infrastructure if you want to make analysis convenient and of consistent quality. https://t.co/IRud9rkebZ
— Brandon Rohrer (@_brohrer_) July 31, 2019
