Теперь тут дневник 📔
Добро пожаловать в мою коллекцию инсайтов и вдохновения.
Добро пожаловать в мою коллекцию инсайтов и вдохновения.
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Просто стартап, который достает серебро и золото обратно из электроники 🫠: https://www.wired.com/2014/06/blueoak/
WIRED
This Startup Wants to Turn Your Old Electronics Into Solid Gold
About 50 million tons of electronic waste were generated worldwide in 2012. BlueOak Resources could make a fortune on it.
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On What Matters Volume Three (Derek P....pdf
1.6 MB
#сейчасчитаю
Дерек Энтони Парфит— британский аналитический философ, специализировавшийся на проблеме тождества личности, нормативной этике и метаэтике, и рациональности.
Дерек Энтони Парфит— британский аналитический философ, специализировавшийся на проблеме тождества личности, нормативной этике и метаэтике, и рациональности.
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И одобрительная ответочка от П. Сингера : https://academic.oup.com/book/3167
OUP Academic
Does Anything Really Matter?: Essays on Parfit on Objectivity
Abstract. Ethical subjectivism or skepticism, in one form or another, has dominated moral philosophy for the past eighty years. In On What Matters Derek Pa
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https://scholarlycommons.law.case.edu/cgi/viewcontent.cgi?article=1151&context=caselrev
Some Great Depression era’s cheers.
Enjoy
(Example: cut a whole pumpkin, remove seeds, pack with sugar, seal top back in place with paraffin. In 30 days the sugar and the meat of pumpkin would be transformed into high-powered gin. /case western reserve law review, vol. 6 ,… / )
Some Great Depression era’s cheers.
Enjoy
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In a meanwhile, me and @anton_zheltoukhov working on the research lab be like..
https://www.alignmentforum.org/posts/57wx7B3GQavvKkPne/ai-safety-camp-10 (11)
https://www.alignmentforum.org/posts/57wx7B3GQavvKkPne/ai-safety-camp-10 (11)
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https://www.astralcodexten.com/p/secrets-of-the-median-voter-theorem Scott Alexander is a crush ♡
Astralcodexten
Secrets Of The Median Voter Theorem
...
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Wisdom of the day:
\max \prod_{i} p(x_i | x_{1:i-1}) = \min \left( -\sum_{i} \log p(x_i | x_{1:i-1}) \right) = \min \mathcal{L}(x_{i:L})
Minimising the loss is the same as maximising the likelihood ✌🏼
(kinda obvious kinda damn solid)
\max \prod_{i} p(x_i | x_{1:i-1}) = \min \left( -\sum_{i} \log p(x_i | x_{1:i-1}) \right) = \min \mathcal{L}(x_{i:L})
Minimising the loss is the same as maximising the likelihood ✌🏼
(kinda obvious kinda damn solid)
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