Link Bibliography

1. https://gwern.substack.com

2. “November 2016 News”, Gw­ern Bran­wen (2016-10-30):

   N/A

3. “Gwern.net newsletter archives”, Gw­ern Bran­wen (2013-12-01):

   Newsletter tag: archive of all issues back to 2013 for the gwern.net newsletter (monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews.)

4. “Changelog”, Gw­ern Bran­wen (2013-09-15):

   This page is a changelog for Gw­ern.net: a monthly re­verse chrono­log­i­cal list of re­cent ma­jor writings/changes/additions.

   Fol­low­ing my writ­ing can be a lit­tle diffi­cult be­cause it is often so in­cre­men­tal. So every mon­th, in ad­di­tion to my reg­u­lar /r/Gwern sub­red­dit sub­mis­sions, I write up rea­son­ably-in­ter­est­ing changes and send it out to the mail­ing list in ad­di­tion to a com­pi­la­tion of links & re­views (archives).

5. “/r/gwern subreddit”, Gw­ern Bran­wen (2018-10-01):

   A subreddit for posting links of interest and also for announcing updates to gwern.net (which can be used as a RSS feed). Submissions are categorized similar to the monthly newsletter and typically will be collated there.

6. https://www.patreon.com/gwern

7. “Why Tool AIs Want to Be Agent AIs”, Gw­ern Bran­wen (2016-09-07):

   Au­tonomous AI sys­tems (A­gent AIs) trained us­ing re­in­force­ment learn­ing can do harm when they take wrong ac­tions, es­pe­cially su­per­in­tel­li­gent Agent AIs. One so­lu­tion would be to elim­i­nate their agency by not giv­ing AIs the abil­ity to take ac­tions, con­fin­ing them to purely in­for­ma­tional or in­fer­en­tial tasks such as clas­si­fi­ca­tion or pre­dic­tion (Tool AIs), and have all ac­tions be ap­proved & ex­e­cuted by hu­mans, giv­ing equiv­a­lently su­per­in­tel­li­gent re­sults with­out the risk.

   I ar­gue that this is not an effec­tive so­lu­tion for two ma­jor rea­sons. First, be­cause Agent AIs will by de­fi­n­i­tion be bet­ter at ac­tions than Tool AIs, giv­ing an eco­nomic ad­van­tage. Sec­ond­ly, be­cause Agent AIs will be bet­ter at in­fer­ence & learn­ing than Tool AIs, and this is in­her­ently due to their greater agen­cy: the same al­go­rithms which learn how to per­form ac­tions can be used to se­lect im­por­tant dat­a­points to learn in­fer­ence over, how long to learn, how to more effi­ciently ex­e­cute in­fer­ence, how to de­sign them­selves, how to op­ti­mize hy­per­pa­ra­me­ters, how to make use of ex­ter­nal re­sources such as long-term mem­o­ries or ex­ter­nal soft­ware or large data­bases or the In­ter­net, and how best to ac­quire new da­ta. All of these ac­tions will re­sult in Agent AIs more in­tel­li­gent than Tool AIs, in ad­di­tion to their greater eco­nomic com­pet­i­tive­ness. Thus, Tool AIs will be in­fe­rior to Agent AIs in both ac­tions and in­tel­li­gence, im­ply­ing use of Tool AIs is a even more highly un­sta­ble equi­lib­rium than pre­vi­ously ar­gued, as users of Agent AIs will be able to out­com­pete them on two di­men­sions (and not just one).

8. “Internet WiFi improvement”, Gw­ern Bran­wen (2016-10-20):

   My lap­top in my apart­ment re­ceives In­ter­net via a WiFi re­peater to an­other house, yield­ing slow speeds and fre­quent glitch­es. I re­placed the ob­so­lete WiFi router and in­creased con­nec­tion speeds some­what but still in­ad­e­quate. For a bet­ter so­lu­tion, I used a di­rec­tional an­tenna to con­nect di­rectly to the new WiFi router, which, con­trary to my ex­pec­ta­tions, yielded a ~6× in­crease in speed. Ex­ten­sive bench­mark­ing of all pos­si­ble arrange­ments of laptops/dongles/repeaters/antennas/routers/positions shows that the an­ten­na+router is in­ex­pen­sive and near op­ti­mal speed, and that the only pos­si­ble im­prove­ment would be a hard­wired Eth­er­net line, which I in­stalled a few weeks later after learn­ing it was not as diffi­cult as I thought it would be.

9. “On the Existence of Powerful Natural Languages”, Gw­ern Bran­wen (2016-12-18):

   De­signed for­mal no­ta­tions & dis­tinct vo­cab­u­lar­ies are often em­ployed in STEM fields, and these spe­cial­ized lan­guages are cred­ited with greatly en­hanc­ing re­search & com­mu­ni­ca­tion. Many philoso­phers and other thinkers have at­tempted to cre­ate more gen­er­al­ly-ap­plic­a­ble de­signed lan­guages for use out­side of spe­cific tech­ni­cal fields to en­hance hu­man think­ing, but the em­pir­i­cal track record is poor and no such de­signed lan­guage has demon­strated sub­stan­tial im­prove­ments to hu­man cog­ni­tion such as re­sist­ing cog­ni­tive bi­ases or log­i­cal fal­lac­i­es. I sug­gest that the suc­cess of spe­cial­ized lan­guages in fields is in­her­ently due to en­cod­ing large amounts of pre­vi­ous­ly-dis­cov­ered in­for­ma­tion spe­cific to those fields, and this ex­plains their in­abil­ity to boost hu­man cog­ni­tion across a wide va­ri­ety of do­mains.

10. /Statistical-notes#model-criticism-via-machine-learning

11. “Epigrams”, Gw­ern Bran­wen (2014-04-30):

    Witticisms, parodies, pointed observations, japeries, jocularity, Tom Swifties, examples of nominative determinism, and discursive drollery

12. /docs/genetics/correlation/2016-lo.pdf

13. /docs/genetics/correlation/2016-kendall.pdf

14. /docs/genetics/correlation/2016-riglin.pdf

15. http://www.nature.com/nature/journal/v536/n7614/abs/nature18642.html

16. “Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank”, Amy E. Tay­lor, Mar­cus R. Mu­nafò (2016-12-22):

    Background: Genetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.

    Aims

    To improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.

    Methods: We tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.

    Results: Genetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.

    Conclusion: Genetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

17. http://mbe.oxfordjournals.org/content/early/2016/12/20/molbev.msw283.full.pdf+html

18. http://www.nytimes.com/2016/12/23/science/inuit-greenland-denisovans.html

19. “Overcoming catastrophic forgetting in neural networks”, James Kirk­patrick, Raz­van Pas­canu, Neil Ra­bi­now­itz, Joel Ve­ness, Guil­laume Des­jardins, An­drei A. Rusu, Kieran Mi­lan, John Quan, Tiago Ra­mal­ho, Ag­nieszka Grab­ska-Bar­win­ska, Demis Has­s­abis, Clau­dia Clopath, Dhar­shan Ku­maran, Raia Had­sell (2016-12-02):

    The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.

20. https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/

21. “Value Iteration Networks”, Aviv Tamar, Yi Wu, Gar­rett Thomas, Sergey Levine, Pieter Abbeel (2016-02-09):

    We introduce the value iteration network (VIN): a fully differentiable neural network with a ‘planning module’ embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

22. “The Predictron: End-To-End Learning and Planning”, David Sil­ver, Hado van Has­selt, Mat­teo Hes­sel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Du­lac-Arnold, David Re­ichert, Neil Ra­bi­now­itz, An­dre Bar­reto, Thomas De­gris (2016-12-28):

    One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. The predictron consists of a fully abstract model, represented by a Markov reward process, that can be rolled forward multiple "imagined" planning steps. Each forward pass of the predictron accumulates internal rewards and values over multiple planning depths. The predictron is trained end-to-end so as to make these accumulated values accurately approximate the true value function. We applied the predictron to procedurally generated random mazes and a simulator for the game of pool. The predictron yielded significantly more accurate predictions than conventional deep neural network architectures.

23. “Monte Carlo tree search”, Eng­lish Wikipedia (2020-12-22):

    In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree.

24. “Convolutional Neural Fabrics”, Shreyas Sax­e­na, Jakob Ver­beek (2016-06-08):

    Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a "fabric" that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyper-parameters of a fabric are the number of channels and layers. While individual architectures can be recovered as paths, the fabric can in addition ensemble all embedded architectures together, sharing their weights where their paths overlap. Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset.

25. “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks”, Han Zhang, Tao Xu, Hong­sheng Li, Shaot­ing Zhang, Xi­ao­gang Wang, Xi­aolei Huang, Dim­itris Metaxas (2016-12-10):

    Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256×256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

26. “Stacked Generative Adversarial Networks”, Xun Huang, Yix­uan Li, Omid Pour­saeed, John Hopcroft, Serge Be­longie (2016-12-13):

    In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each feature hierarchy to encourage the representation manifold of the generator to align with that of the bottom-up discriminative network, leveraging the powerful discriminative representations to guide the generative model. In addition, we introduce a conditional loss that encourages the use of conditional information from the layer above, and a novel entropy loss that maximizes a variational lower bound on the conditional entropy of generator outputs. We first train each stack independently, and then train the whole model end-to-end. Unlike the original GAN that uses a single noise vector to represent all the variations, our SGAN decomposes variations into multiple levels and gradually resolves uncertainties in the top-down generative process. Based on visual inspection, Inception scores and visual Turing test, we demonstrate that SGAN is able to generate images of much higher quality than GANs without stacking.

27. https://pdfs.semanticscholar.org/053d/ec3537df88a1f68b53e33e1462a6b88066f6.pdf

28. http://humancompatible.ai/bibliography

29. http://projects.jsonline.com/news/2016/12/11/the-price-of-being-wrong.html

30. “Schiehallion experiment”, Eng­lish Wikipedia (2020-12-28):

    

    The Schiehallion experiment was an 18th-century experiment to determine the mean density of the Earth. Funded by a grant from the Royal Society, it was conducted in the summer of 1774 around the Scottish mountain of Schiehallion, Perthshire. The experiment involved measuring the tiny deflection of the vertical due to the gravitational attraction of a nearby mountain. Schiehallion was considered the ideal location after a search for candidate mountains, thanks to its isolation and almost symmetrical shape.

31. “The Strategic Consequences of Chinese Racism: A Strategic Asymmetry for the United States”, Anony­mous, Anony­mous (2013-01-07):

    Whether China and the United States are destined to compete for domination in international politics is one of the major questions facing DoD. In a competition with the People’s Republic of China, the United States must explore all of its advantages and all of the weaknesses of China that may provide an asymmetry for the United States. This study examines one such asymmetry, the strategic consequences of Chinese racism. After having examined the literature on China extensively, this author is not aware of a single study that addresses this important topic. This study explores the causes of Chinese racism, the strategic consequences of Chinese racism, and how the United States may use this situation to advance its interests in international politics.

    1. the study finds that xenophobia, racism, and ethnocentrism are caused by human evolution. These behaviors are not unique to the Chinese. However, they are made worse by Chinese history and culture.

    2. considers the Chinese conception of race in Chinese history and culture. It finds that Chinese religious-cultural and historical conceptions of race reinforce Chinese racism. In Chinese history and contemporary culture, the Chinese are seen to be unique and superior to the rest of the world. Other peoples and groups are seen to be inferior, with a sliding scale of inferiority. The major Chinese distinction is between degrees of barbarians, the “black devils,” or savage inferiors, beyond any hope of interaction and the “white devils” or tame barbarians with whom the Chinese can interact. These beliefs are widespread in Chinese society, and have been for its history…

    3. evaluates the 9 strategic consequences of Chinese racism.

       1. virulent racism and eugenics heavily inform Chinese perceptions of the world…

       2. racism informs their view of the United States…

       3. racism informs their view of international politics in three ways.

          • states are stable, and thus good for the Chinese, to the degree that they are unicultural.
          • Chinese ethnocentrism and racism drive their outlook to the rest of the world. Their expectation is of a tribute system where barbarians know that the Chinese are superior.
          • there is a strong, implicit, racialist view of international politics that is alien and anathema to Western policy-makers and analysts. The Chinese are comfortable using race to explain events and appealing to racist stereotypes to advance their interests. Most insidious is the Chinese belief that Africans in particular need Chinese leadership.

       4. the Chinese will make appeals to Third World states based on “racial solidarity,”…

       5. Chinese racism retards their relations with the Third World…

       6. Chinese racism, and the degree to which the Chinese permit their view of the United States to be informed by racism, has the potential to hinder China in its competition with the United States because it contributes to their overconfidence…

       7. as lamentable as it is, Chinese racism helps to make the Chinese a formidable adversary…

       8. the Chinese are never going to go through a civil rights movement like the United States…

       9. China’s treatment of Christians and ethnic minorities is poor…

    4. considers the 5 major implications for United States decision-makers and asymmetries that may result from Chinese racism.

    5. Chinese racism provides empirical evidence of how the Chinese will treat other international actors if China becomes dominant…

    6. it allows the United States to undermine China in the Third World…

    7. it permits a positive image of the United States to be advanced in contrast to China…

    8. calling attention to Chinese racism allows political and ideological alliances of the United States to be strengthened…

    9. United States defense decision-makers must recognize that racism is a cohesive force for the Chinese…

    …The study’s fundamental conclusion is that endemic Chinese racism offers the United States a major asymmetry it may exploit with major countries, regions like Africa, as well as with important opinion makers in international politics. The United States is on the right side of the struggle against racism and China is not. The United States should call attention to this to aid its position in international politics.

32. http://www.newyorker.com/magazine/2017/01/02/a-bigger-problem-than-isis

33. https://askakorean.blogspot.com/2016/12/the-ultimate-choi-soon-sil-gate.html

34. https://www.theatlantic.com/magazine/archive/2017/03/the-american-leader-in-the-islamic-state/510872/

35. http://www.newstatesman.com/science-tech/internet/2016/12/movie-doesn-t-exist-and-redditors-who-think-it-does

36. https://paleofuture.gizmodo.com/the-untold-story-of-napoleon-hill-the-greatest-self-he-1789385645

37. “Childhood forecasting of a small segment of the population with large economic burden.”, Caspi, Avshalom Houts, Re­nate M. Bel­sky, Daniel W. Har­ring­ton, Honalee Hogan, Sean Ram­rakha, Sand­hya Poul­ton, Richie Moffitt, Ter­rie E (2016):

    Policy-makers are interested in early-years interventions to ameliorate childhood risks. They hope for improved adult outcomes in the long run, bringing return on investment. How much return can be expected depends, partly, on how strongly childhood risks forecast adult outcomes. But there is disagreement about whether childhood determines adulthood. We integrated multiple nationwide administrative databases and electronic medical records with the four-decade Dunedin birth-cohort study to test child-to-adult prediction in a different way, by using a population-segmentation approach. A segment comprising one-fifth of the cohort accounted for 36% of the cohort's injury insurance-claims; 40% of excess obese-kilograms; 54% of cigarettes smoked; 57% of hospital nights; 66% of welfare benefits; 77% of fatherless childrearing; 78% of prescription fills; and 81% of criminal convictions. Childhood risks, including poor age-three brain health, predicted this segment with large effect sizes. Early-years interventions effective with this population segment could yield very large returns on investment.

38. /images/iq/2016-caspi-figure4-dunedin-highvslowiq-socialcosts.jpg

39. “The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development”, Daniel W. Bel­sky, Ter­rie E. Moffitt, David L. Cor­co­ran, Ben­jamin Domingue, HonaLee Har­ring­ton, Sean Hogan, Re­nate Houts, Sand­hya Ram­rakha, Karen Sug­den, Ben­jamin S. Williams, Richie Poul­ton, Avshalom Caspi (2016-06-01):

    A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children’s polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting GWAS sequence with life outcomes may provide targets for interventions to promote population-wide positive development. [Keywords: genetics, behavior genetics, intelligence, personality, adult development]

40. “Clustering of health, crime and social-welfare inequality in 4 million citizens from two nations”, Leah S. Rich­mond-Rak­erd, Stephanie D’­Souza, Signe Hald An­der­sen, Sean Hogan, Re­nate M. Houts, Richie Poul­ton, Sand­hya Ram­rakha, Avshalom Caspi, Barry J. Mil­ne, Ter­rie E. Moffitt (2020-01-20):

    Health and social scientists have documented the hospital revolving-door problem, the concentration of crime, and long-term welfare dependence. Have these distinct fields identified the same citizens? Using administrative databases linked to 1.7 million New Zealanders, we quantified and monetized inequality in distributions of health and social problems and tested whether they aggregate within individuals. Marked inequality was observed: Gini coefficients equalled 0.96 for criminal convictions, 0.91 for public-hospital nights, 0.86 for welfare benefits, 0.74 for prescription-drug fills and 0.54 for injury-insurance claims. Marked aggregation was uncovered: a small population segment accounted for a disproportionate share of use-events and costs across multiple sectors. These findings were replicated in 2.3 million Danes. We then integrated the New Zealand databases with the four-decade-long Dunedin Study. The high-need/high-cost population segment experienced early-life factors that reduce workforce readiness, including low education and poor mental health. In midlife they reported low life satisfaction. Investing in young people’s education and training potential could reduce health and social inequalities and enhance population wellbeing.

41. https://www.theatlantic.com/health/archive/2016/12/the-life-changing-magic-of-mushrooms/509246/

42. /docs/nootropics/2016-carhart-harris.pdf

43. /docs/nootropics/2016-nutt.pdf

44. /docs/nootropics/2016-griffiths.pdf

45. /docs/nootropics/2016-ross.pdf

46. /docs/nootropics/2016-carbonaro.pdf

47. /docs/nootropics/2016-barrett.pdf

48. /docs/iq/2016-sala.pdf

49. http://www.aging-us.com/article/YmhYbE6ipoL6ykpfz/text

50. /docs/biology/2016-havlicek.pdf

51. https://www.nngroup.com/articles/computer-skill-levels/

52. http://lesswrong.com/r/discussion/lw/odb/wikipedia_usage_survey_results/

53. http://goto.ucsd.edu/~nvazou/thesis/main.pdf

54. “Satisfiability modulo theories”, Eng­lish Wikipedia (2020-12-22):

    In computer science and mathematical logic, the satisfiability modulo theories (SMT) problem is a decision problem for logical formulas with respect to combinations of background theories expressed in classical first-order logic with equality. Examples of theories typically used in computer science are the theory of real numbers, the theory of integers, and the theories of various data structures such as lists, arrays, bit vectors and so on. SMT can be thought of as a form of the constraint satisfaction problem and thus a certain formalized approach to constraint programming.

55. “Dependent type”, Eng­lish Wikipedia (2020-12-22):

    In computer science and logic, a dependent type is a type whose definition depends on a value. It is an overlapping feature of type theory and type systems. In intuitionistic type theory, dependent types are used to encode logic's quantifiers like "for all" and "there exists". In functional programming languages like Agda, ATS, Coq, F*, Epigram, and Idris, dependent types may help reduce bugs by enabling the programmer to assign types that further restrain the set of possible implementations.

56. https://scarybeastsecurity.blogspot.com/2016/11/0day-exploit-compromising-linux-desktop.html

57. http://quinndunki.com/blondihacks/?p=3023

58. /docs/economics/2016-shortland.pdf

59. https://www.washingtonpost.com/news/monkey-cage/wp/2016/12/13/kidnapping-for-ransom-works-like-a-market-how-it-is-organized-is-surprising/

60. http://www.templetons.com/brad/robocars/compete.html

61. http://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1991/TempComp.html

62. “Randall Jarrell”, Eng­lish Wikipedia (2020-12-28):

    

    Randall Jarrell jə-REL was an American poet, literary critic, children's author, essayist, and novelist. He was the 11th Consultant in Poetry to the Library of Congress—a position that now bears the title Poet Laureate of the United States.

63. “A Beautiful Planet”, Eng­lish Wikipedia (2020-12-28):

    

    A Beautiful Planet is a 2016 American documentary film that explores Earth by showing IMAX footage, recorded over the course of fifteen months by astronauts aboard the International Space Station. It is narrated by actress Jennifer Lawrence.

64. /Movie-reviews#a-beautiful-planet

65. “Florence Foster Jenkins (film)”, Eng­lish Wikipedia (2020-12-28):

    

    Florence Foster Jenkins is a 2016 biographical film directed by Stephen Frears and written by Nicholas Martin. It stars Meryl Streep as Florence Foster Jenkins, a New York heiress known for her poor singing. Hugh Grant plays her manager and long-time companion, St. Clair Bayfield. Other cast members include Simon Helberg, Rebecca Ferguson, and Nina Arianda.

66. “Florence Foster Jenkins”, Eng­lish Wikipedia (2020-12-28):

    

    Florence Foster Jenkins was an American socialite and amateur soprano who was known, and mocked, for her flamboyant performance costumes and notably poor singing ability. Stephen Pile ranked her "the world's worst opera singer ... No one, before or since, has succeeded in liberating themselves quite so completely from the shackles of musical notation."

67. “The Sound of Music (film)”, Eng­lish Wikipedia (2020-12-28):

    

    The Sound of Music is a 1965 American musical drama film produced and directed by Robert Wise, and starring Julie Andrews and Christopher Plummer, with Richard Haydn, Peggy Wood, Charmian Carr, and Eleanor Parker. The film is an adaptation of the 1959 stage musical of the same name, composed by Richard Rodgers with lyrics by Oscar Hammerstein II. The film's screenplay was written by Ernest Lehman, adapted from the stage musical's book by Lindsay and Crouse. Based on the 1949 memoir The Story of the Trapp Family Singers by Maria von Trapp, the film is about a young Austrian postulant in Salzburg, Austria, in 1938 who is sent to the villa of a retired naval officer and widower to be governess to his seven children. After bringing love and music into the lives of the family, she marries the officer and, together with the children, finds a way to survive the loss of their homeland to the Nazis.

68. “Fantastic Beasts and Where to Find Them (film)”, Eng­lish Wikipedia (2020-12-28):

    

    Fantastic Beasts and Where to Find Them is a 2016 fantasy film directed by David Yates. A joint British and American production, it is a spin-off of and prequel to the Harry Potter film series. It was produced and written by J. K. Rowling in her screenwriting debut, inspired by her 2001 "guide book" of the same name. The film features an ensemble cast that includes Eddie Redmayne, Katherine Waterston, Dan Fogler, Alison Sudol, Ezra Miller, Samantha Morton, Jon Voight, Carmen Ejogo, and Colin Farrell. It is the first installment in the Fantastic Beasts film series, and ninth overall in the Wizarding World franchise that began with the Harry Potter films.

69. “Going Clear (film)”, Eng­lish Wikipedia (2020-12-28):

    

    Going Clear: Scientology and the Prison of Belief is a 2015 documentary film about Scientology. Directed by Alex Gibney and produced by HBO, it is based on Lawrence Wright's book Going Clear: Scientology, Hollywood and the Prison of Belief (2013). The film premiered at the 2015 Sundance Film Festival in Park City, Utah. It received widespread praise from critics and was nominated for seven Emmy Awards, winning three, including Best Documentary. It also received a 2015 Peabody Award and won the award for Best Documentary Screenplay from the Writers Guild of America.

70. http://www.newyorker.com/magazine/2011/02/14/the-apostate-lawrence-wright

71. “Finding Dory”, Eng­lish Wikipedia (2020-12-28):

    

    Finding Dory is a 2016 American computer-animated adventure film produced by Pixar Animation Studios and released by Walt Disney Pictures. Directed by Andrew Stanton and written by Stanton and Victoria Strouse, the film is a sequel/spinoff to Finding Nemo (2003) and features the returning voices of Ellen DeGeneres and Albert Brooks, with Hayden Rolence, Ed O'Neill, Kaitlin Olson, Ty Burrell, Diane Keaton, and Eugene Levy joining the cast. The film focuses on the amnesiac fish Dory, who journeys to be reunited with her parents.

72. https://www.youtube.com/watch?v=yjl2tiGRqWU

73. https://www.youtube.com/watch?v=_BuWULqeAFM

74. https://www.dropbox.com/s/xi9cczafrsbr9bl/pinocchiop-itsmatsuriheycomeon-%E3%81%BC%E3%81%8F%E3%82%82%E5%B1%91%E3%81%A0%E3%81%8B%E3%82%89.ogg

75. https://www.youtube.com/watch?v=oypERR3HKOU

76. “Gwern.net newsletter (Substack subscription page)”, Gw­ern Bran­wen (2013-12-01):

    Subscription page for the monthly gwern.net newsletter. There are monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews. You can also browse the archives since December 2013.

77. https://old.reddit.com/r/gwern/.rss

78. “Reinforcement learning”, Eng­lish Wikipedia (2020-12-22):

    Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.