nhaliday + init   173

trees are harlequins, words are harlequins — bayes: a kinda-sorta masterpost
lol, gwern: https://www.reddit.com/r/slatestarcodex/comments/6ghsxf/biweekly_rational_feed/diqr0rq/
> What sort of person thinks “oh yeah, my beliefs about these coefficients correspond to a Gaussian with variance 2.5″? And what if I do cross-validation, like I always do, and find that variance 200 works better for the problem? Was the other person wrong? But how could they have known?
> ...Even ignoring the mode vs. mean issue, I have never met anyone who could tell whether their beliefs were normally distributed vs. Laplace distributed. Have you?
I must have spent too much time in Bayesland because both those strike me as very easy and I often think them! My beliefs usually are Laplace distributed when it comes to things like genetics (it makes me very sad to see GWASes with flat priors), and my Gaussian coefficients are actually a variance of 0.70 (assuming standardized variables w.l.o.g.) as is consistent with field-wide meta-analyses indicating that d>1 is pretty rare.
ratty  ssc  core-rats  tumblr  social  explanation  init  philosophy  bayesian  thinking  probability  stats  frequentist  big-yud  lesswrong  synchrony  similarity  critique  intricacy  shalizi  scitariat  selection  mutation  evolution  priors-posteriors  regularization  bias-variance  gwern  reddit  commentary  GWAS  genetics  regression  spock  nitty-gritty  generalization  epistemic  🤖  rationality  poast  multi  best-practices  methodology  data-science 
august 2017 by nhaliday
Fear and Loathing in Psychology - The Unz Review
Warne and Astle looked at 29 best-selling undergraduate textbooks, which is where psychology students learn about intelligence, because less than 10% of graduate courses offer an intelligence option.

3.3% of textbook space is dedicated to intelligence. Given its influence, this is not very much.

The most common topics start well, with IQ and Spearman’s g, but do not go on to the best validated, evidence-led Cattell-Horn-Carol meta-analytic summary, but a side-stream, speculative triarchic theory from Sternberg; and a highly speculative and non-specific sketch of an idea about multiple intelligences Gardner. The last is a particular puzzle, since it really is a whimsical notion that motor skill is no different from analytical problem solving. All must have prizes.
Commonly, environmental influences are discussed, genetic ones rarely.

What Do Undergraduates Learn About Human Intelligence? An Analysis of Introductory Psychology Textbooks: https://drive.google.com/file/d/0B3c4TxciNeJZOTl3clpiX0JKckk/view

Education or Indoctrination? The Accuracy of Introductory Psychology Textbooks in Covering Controversial Topics and Urban Legends About Psychology: http://sci-hub.la/https://link.springer.com/article/10.1007/s12144-016-9539-7

Twenty-four leading introductory psychology textbooks were surveyed for their coverage of a number of controversial topics (e.g., media violence, narcissism epidemic, multiple intelligences) and scientific urban legends (e.g., Kitty Genovese, Mozart Effect) for their factual accuracy. Results indicated numerous errors of factual reporting across textbooks, particularly related to failing to inform students of the controversial nature of some research fields and repeating some scientific urban legends as if true. Recommendations are made for improving the accuracy of introductory textbooks.

Mapping the scale of the narcissism epidemic: Increases in narcissism 2002–2007 within ethnic groups: https://www.sciencedirect.com/science/article/pii/S0092656608000949

The increasing numbers of Asian-Americans at the UCs over time may have masked changes in narcissism, as Asian-Americans score lower on the NPI. When examined within ethnic groups, Trzesniewski et al.’s data show that NPI scores increased significantly between 2002 and 2007 at twice the rate of the yearly change found over 24 years in Twenge et al. (2008a). The overall means also show a significant increase 2002–2007. Thus the available evidence suggests that college students are endorsing progressively more narcissistic personality traits over the generations.

Birth Cohort Increases in Narcissistic Personality Traits Among American College Students, 1982–2009: http://journals.sagepub.com/doi/abs/10.1177/1948550609355719

Both studies demonstrate significant increases in narcissism over time (Study 1 d = .37, 1982–2008, when campus is controlled; Study 2 d = .37, 1994–2009). These results support a generational differences model of individual personality traits reflecting changes in culture.

could this just be a selection effect (more people attending)?
albion  scitariat  education  higher-ed  academia  social-science  westminster  info-dynamics  psychology  cog-psych  psychometrics  iq  intelligence  realness  biases  commentary  study  summary  meta:science  pinker  multi  pdf  survey  is-ought  truth  culture-war  toxoplasmosis  replication  social-psych  propaganda  madisonian  identity-politics  init  personality  psychiatry  disease  trends  epidemiology  public-health  psych-architecture  dimensionality  confounding  control  age-generation  demographics  race  christopher-lasch  humility  usa  the-west  california  berkeley  asia 
july 2017 by nhaliday
How do these "neural network style transfer" tools work? - Julia Evans
When we put an image into the network, it starts out as a vector of numbers (the red/green/blue values for each pixel). At each layer of the network we get another intermediate vector of numbers. There’s no inherent meaning to any of these vectors.

But! If we want to, we could pick one of those vectors arbitrarily and declare “You know, I think that vector represents the content” of the image.

The basic idea is that the further down you get in the network (and the closer towards classifying objects in the network as a “cat” or “house” or whatever”), the more the vector represents the image’s “content”.

In this paper, they designate the “conv4_2” later as the “content” layer. This seems to be pretty arbitrary – it’s just a layer that’s pretty far down the network.

Defining “style” is a bit more complicated. If I understand correctly, the definition “style” is actually the major innovation of this paper – they don’t just pick a layer and say “this is the style layer”. Instead, they take all the “feature maps” at a layer (basically there are actually a whole bunch of vectors at the layer, one for each “feature”), and define the “Gram matrix” of all the pairwise inner products between those vectors. This Gram matrix is the style.
techtariat  bangbang  deep-learning  model-class  explanation  art  visuo  machine-learning  acm  SIGGRAPH  init  inner-product  nibble 
february 2017 by nhaliday
Orthogonal — Greg Egan
In Yalda’s universe, light has no universal speed and its creation generates energy.

On Yalda’s world, plants make food by emitting their own light into the dark night sky.
greg-egan  fiction  gedanken  physics  electromag  differential  geometry  thermo  space  cool  curiosity  reading  exposition  init  stat-mech  waves  relativity  positivity  unit  wild-ideas  speed  gravity  big-picture  🔬  xenobio  ideas  scifi-fantasy  signum 
february 2017 by nhaliday
Genetics and educational attainment | npj Science of Learning
Figure 1 is quite good
Sibling Correlations for Behavioral Traits. This figure displays sibling correlations for five traits measured in a large sample of Swedish brother pairs born 1951–1970. All outcomes except years of schooling are measured at conscription, around the age of 18.

correlations for IQ/EA for adoptees are actually nontrivial in adulthood, hmm

Figure 2 has GWAS R^2s through 2016 (in-sample, I guess?)
study  org:nat  biodet  education  methodology  essay  survey  genetics  GWAS  variance-components  init  causation  🌞  metrics  population-genetics  explanation  unit  nibble  len:short  big-picture  behavioral-gen  state-of-art  iq  embodied  correlation  twin-study  sib-study  summary  europe  nordic  data  visualization  s:*  tip-of-tongue  spearhead  bioinformatics 
february 2017 by nhaliday
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