Links

Genetics:

• Everything Is Heritable:

  • “GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium”⁠, Trampush et al 2017
  • “Prevalence and architecture of de novo mutations in developmental disorders”⁠, Deciphering Developmental Disorders Study 2017 (Screening for inherited or new/​​​de novo mutations would be a good way to start embryo selection.)
  • “A chemical genetic roadmap to improved tomato flavor”⁠, Tieman et al 2017 (media⁠; intensive phenotyping using taste panels to create flavor polygenic scores which can be used by commercial growers for molecular breeding)
  • “Apolipoprotein E4 is associated with improved cognitive function in Amazonian forager-horticulturalists with a high parasite burden”⁠, Trumble et al 2016 (media⁠; “An Ancient Cure for Alzheimer’s?”⁠; of course, different regions, and hence different groups, differ in parasite load.)
  • “Personalized Media: A Genetically Informative Investigation of Individual Differences in Online Media Use”⁠, Ayorech et al 2017

• Recent Evolution:

  • in Iceland: decrease in the education polygenic score 1910–1990, “Selection against variants in the genome associated with educational attainment”⁠, Kong et al 2017 (graph⁠; media)
  • in the US: decrease in the education polygenic score 1920–1960, “Mortality Selection in a Genetic Sample and Implications for Association Studies”⁠, Domingue et al 2016 (graph)

AI:

• “Deep Reinforcement Learning: An Overview”⁠, Li 2017
• “Wasserstein GAN”⁠, Arjovsky et al 2017 (a surprisingly small tweak fixes both mode collapse & divergence, yielding stable GANs which reliably fit datasets like anime faces in my experiments with the code⁠; underfitting/​​expressiveness seems to remain an issue, though)
• “Universal representations: The missing link between faces, text, planktons, and cat breeds”⁠, Bilen & Vedaldi 2017 (NNs are grossly overparameterized⁠, and you can share and boost learning by training the same NN on multiple tasks because tasks share a lot of information & form highly informative priors for each other.)

Statistics/​Meta-Science:

• Survey of P = NP results and progress⁠, Aaronson 2016
• “A Conceptual Introduction to Hamiltonian Monte Carlo”, Betancourt 2017 (HMC is critical to Bayesian neural networks & Stan)
• “What Does Any of This Have To Do with Physics?” (brutal memoir of contemporary graduate work in theoretical physics)
• “The Statistical Crisis in Science: The Cult Of P”⁠, Debrouwere 2016 (a readable overview of the problems with p-values/​​NHST)
• “A First Look at Reproducibility in Cancer Biology”
• “The Young Billionaire Behind the War on Bad Science” (John D. Arnold)
• “Meta-knowledge: Crowds aren’t as smart as we thought, since some people know more than others. A simple trick can find the ones you want”

Politics/​religion:

• “Non-Communication at GE: The Impacted Philosophers”⁠, pg111–124, ch7 of Business Adventures: Twelve Classic Tales from the World of Wall Street⁠, Brooks 1969 (the GE price-fixing scandal: emergent conspiracies from plausible denialability)
• Days of Rage: America’s Radical Underground, the FBI, and the Forgotten Age of Revolutionary Violence review
• the iPredict prediction market has closed (This is why we can’t have nice things: because even crippled prediction markets are “money laundering”.)
• “Blue Lies Matter: How Video Proved Cops Lie”

Psychology/​biology:

• “A Systematic Review of Personality Trait Change Through Intervention”⁠, Roberts et al 2016 (Messy—noticeable levels of publication bias, high heterogeneity— but results look plausible: 8-week+ interventions can improve emotional-stability/​​Neuroticism⁠, change Openness and Extraversion somewhat, but leave Conscientiousness largely unaffected.)
• Population dynamics in Puella Magi Madoka Magica

Technology:

• “Epigrams on Programming”⁠, Perlis 1982
• “Some Funny Things Happened on the Way to the Moon”⁠, Battin 2002 (technical & political/​​management problems in Apollo)

Economics:

• “The Cat’s-Meat Man” (how cats & dogs were fed before modern processed pet food: regular deliveries of low-grade meat, usually horse-meat from the surplus of millions per year; focusing on industrial districts where cats were heavily employed)
• “The High-Cost, High-Risk World of Modern Pet Care: A wave of corporatization is hitting the veterinary industry, but does a one-size-fits-all approach work?” (“The cost of veterinary care has risen even faster than the cost of human health care, more than doubling since 2000”; more importantly: why is this model successful in the most free market possible for healthcare?); “Killing Animals at the Zoo: At Danish zoos, surplus animals are euthanized—and dissected before the public”⁠; “The Ornithologist the Internet Called a Murderer”

Philosophy:

• “How To Be Good: An Oxford philosopher thinks he can distill all morality into a formula. Is he right?” (in memory of Derek Parfit)
• “Underprotection of Unpredictable Statistical Lives Compared to Predictable Ones”⁠, Lipsitch et al 2016

Books

Nonfiction:

• Site Reliability Engineering: How Google Runs Production Systems (not a book whose principles I ever expect to apply as intended, but it is fascinating to read about the technical problems & capabilities at Google-scale, the clever solutions applying rarefied computer science techniques, and the war stories about how things can go wrong. Inspired a little by it, I added a number of tests to my Gwern.net sync script.)
• Clever Hans⁠, Oskar Pfungst (on the famous Clever Hans⁠; review)

Fiction:

• The Anubis Gates⁠, Tim Powers (a delicious time-travel adventure, full of London color drawing on London labour and the London poor; a cyclopaedia of the condition and earnings of those that will work, those that cannot work, and those that will not work, Mayhew et al 1851⁠; ultimately, the elements don’t come together in as deep a package as Declare and I am a little disappointed that Powers doesn’t pull off nearly as subtle a ‘secret history’ as he does in that—while I hoped that things like the Dancing Monkeys or Ashbless were historically real, it turns out that Powers had to make them up. But still a fun reread.

  On a side note: in The Anubis Gates, magic is associated with the moon and normality with the earth & sun & Christianity; practitioners are pained and weakened by the touch of the earth and the growth of Christianity, which protects against & destroys magic (and so by the 1800s setting, magic has become almost useless), and gradually lose weight & float as they gravitate towards the moon. The wizened leader of the magicians is so many millennia old and steeped in magic that he would fall towards the moon, and must live in a domed building while upside down lest he fall to the moon like other magicians, rotating around the dome as the moon moves. Given this Chekhov’s gun, it is not surprising that eventually he does fall out of the dome and falls into the sky, presumably to his doom. What happens to him? The character sits around and moves normally while upside down, and neither bounces nor struggles to move; this suggests that an equivalent of Earth gravity now pulls him to the moon (or if we assume the Earth continues to pull, twice Earth gravity, never mind that the moon is much smaller—this is magic). After falling out of the dome, he will start accelerating upwards at ~9.8 m  /​ ​​ ​s²⁠; as there is air resistance, he will soon hit terminal velocity⁠, which for a human is ~54m/​​s, so after ~6s, he will stop accelerating and then begin accelerating slowly as the air thins out & offers less resistance, increasing by ~1% every 160 meters.

  Logically, one would expect him to impact the Moon at some extraordinary velocity as there will be effectively no terminal velocity for his body once out of the earth’s atmosphere, but he will have died long before of thirst, hypothermia⁠, or hypoxia (in increasing order of speed) and probably well before he hits the Armstrong limit of 19000m. At the Armstrong limit, even the water on one’s tongue boils. In any case, the magician will be moving so fast that 19000m is trivial and will be reached within ~5.8 minutes, leaving little time for starvation/​​thirst/​​hypothermia; by this point, he will have previously lapsed into unconsciousness and be suffering from cerebral hypoxia⁠, yielding brain death within 5–10 minutes. So in general, we can safely assume that less than 10 minutes after falling out of the dome, the magician is dead from hypoxia accelerated by hypothermia. But the body will keep on going. How long does it take to reach the Moon? The moon is on average 384400000 meters away, but the body will keep accelerating once it gets past the bulk of the earth’s atmosphere; assuming no more terminal velocity after 500 seconds, the body will reach the average distance of the moon after ~9351s or ~2.6h, traveling at something like 86795m/​​s or 86km/​​s. (Strictly speaking, he might miss the moon and have to spiral around, or might even never impact, but I can’t calculate the orbital mechanics there.) Being a wizened and now desiccated/​​frozen body, it probably doesn’t weigh anywhere over 50kg, but will still pack a major punch with a kinetic energy of 0.2*50*(86795^2) joules or 7.5 megajoules (for comparison 1 ton of TNT is ~4.2 GJ).

• The Complete Poems⁠, Randall Jarrell (review)

Film/TV

Live-action:

• Hackers (1995; review)
• This Is Spinal Tap (1984; review)

Animated:

• Kubo and the Two Strings (review)
• Haven’t You Heard? I’m Sakamoto
• One-Punch Man (review)

Music

Touhou:

• “Spacerując Ulicami Byłego Piekła” (Pizuya’s Cell feat. Miiharu; {2016}) [vocal]
• “Sudden Rain” (鯛の小骨 feat. Mei Ayakura; Your Song and My Small Love {C81}) [vocal]
• “Shanghai Teahouse ~ Chinese Tea” (Coro; 東方流星少女 ~Little Shooting Star~ {C76}) [classical]