nhaliday + πŸŽ“   66

National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track
Here we report five hiring experiments in which faculty evaluated hypothetical female and male applicants, using systematically varied profiles disguising identical scholarship, for assistant professorships in biology, engineering, economics, and psychology. Contrary to prevailing assumptions, men and women faculty members from all four fields preferred female applicants 2:1 over identically qualified males with matching lifestyles (single, married, divorced), with the exception of male economists, who showed no gender preference. Comparing different lifestyles revealed that women preferred divorced mothers to married fathers and that men preferred mothers who took parental leaves to mothers who did not.

Double-blind review favours increased representation of female authors: http://www.sciencedirect.com/science/article/pii/S0169534707002704
Double-blind peer review, in which neither author nor reviewer identity are revealed, is rarely practised in ecology or evolution journals. However, in 2001, double-blind review was introduced by the journal Behavioral Ecology. Following this policy change, there was a significant increase in female first-authored papers, a pattern not observed in a very similar journal that provides reviewers with author information. No negative effects could be identified, suggesting that double-blind review should be considered by other journals.

Teaching accreditation exams reveal grading biases favor women in male-dominated disciplines in France: http://science.sciencemag.org/content/353/6298/474
This bias turns from 3 to 5 percentile ranks for men in literature and foreign languages to about 10 percentile ranks for women in math, physics, or philosophy.
study  org:nat  science  meta:science  gender  discrimination  career  progression  planning  long-term  values  academia  field-study  null-result  effect-size  πŸŽ“  multi  publishing  intervention  biases 
july 2017 by nhaliday
Edge.org: 2017 : WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?
highlights:
- the genetic book of the dead [Dawkins]
- complementarity [Frank Wilczek]
- relative information
- effective theory [Lisa Randall]
- affordances [Dennett]
- spontaneous symmetry breaking
- relatedly, equipoise [Nicholas Christakis]
- case-based reasoning
- population reasoning (eg, common law)
- criticality [Cesar Hidalgo]
- Haldan's law of the right size (!SCALE!)
- polygenic scores
- non-ergodic
- ansatz
- state [Aaronson]: http://www.scottaaronson.com/blog/?p=3075
- transfer learning
- effect size
- satisficing
- scaling
- the breeder's equation [Greg Cochran]
- impedance matching

soft:
- reciprocal altruism
- life history [Plomin]
- intellectual honesty [Sam Harris]
- coalitional instinct (interesting claim: building coalitions around "rationality" actually makes it more difficult to update on new evidence as it makes you look like a bad person, eg, the Cathedral)
basically same: https://twitter.com/ortoiseortoise/status/903682354367143936

more: https://www.edge.org/conversation/john_tooby-coalitional-instincts

interesting timing. how woke is this dude?
org:edge  2017  technology  discussion  trends  list  expert  science  top-n  frontier  multi  big-picture  links  the-world-is-just-atoms  metameta  πŸ”¬  scitariat  conceptual-vocab  coalitions  q-n-a  psychology  social-psych  anthropology  instinct  coordination  duty  power  status  info-dynamics  cultural-dynamics  being-right  realness  cooperate-defect  westminster  chart  zeitgeist  rot  roots  epistemic  rationality  meta:science  analogy  physics  electromag  geoengineering  environment  atmosphere  climate-change  waves  information-theory  bits  marginal  quantum  metabuch  homo-hetero  thinking  sapiens  genetics  genomics  evolution  bio  GT-101  low-hanging  minimum-viable  dennett  philosophy  cog-psych  neurons  symmetry  humility  life-history  social-structure  GWAS  behavioral-gen  biodet  missing-heritability  ergodic  machine-learning  generalization  west-hunter  population-genetics  methodology  blowhards  spearhead  group-level  scale  magnitude  business  scaling-tech  tech  business-models  optimization  effect-size  aaronson  state  bare-hands  problem-solving  politics 
may 2017 by nhaliday
China Overtakes US in Scientific Articles, Robots, Supercomputers - The Unz Review
gnon  commentary  trends  usa  china  asia  comparison  sinosphere  frontier  technology  science  innovation  robotics  automation  latin-america  india  russia  scale  military  defense  foreign-policy  realpolitik  great-powers  kumbaya-kult  thucydides  multi  hsu  scitariat  heavy-industry  news  org:nat  org:sci  data  visualization  list  infographic  world  europe  EU  org:mag  dynamic  ranking  top-n  britain  anglo  japan  meta:science  anglosphere  database  germanic  org:biz  rhetoric  prediction  tech  labor  human-capital  education  higher-ed  money  compensation  idk  org:lite  expansionism  current-events  πŸ”¬  the-world-is-just-atoms  πŸŽ“  dirty-hands  org:rec  org:anglo  speedometer  track-record  time-series  monetary-fiscal  chart  quality 
may 2017 by nhaliday
Taulbee Survey - CRA
- about 30% academic, 10% tenure-track for both ML and theory
- for industry flow, it's about 60% research for ML and 40% research for theory (presumably research in something that's not theory for the most part)
- so overall 60-70% w/ some kind of research career
grad-school  phd  data  planning  long-term  cs  schools  πŸŽ“  objektbuch  poll  transitions  progression 
february 2017 by nhaliday
Paperscape
- includes physics, cs, etc.
- CS is _a lot_ smaller, or at least has much lower citation counts
- size = number citations, placement = citation network structure
papers  publishing  science  meta:science  data  visualization  network-structure  big-picture  dynamic  exploratory  πŸŽ“  physics  cs  math  hi-order-bits  survey  visual-understanding  preprint  aggregator  database  search  maps  zooming  metameta  scholar-pack  πŸ”¬  info-dynamics  scale  let-me-see  chart 
february 2017 by nhaliday
Information Processing: Learn to solve every problem that has been solved
While it may be impossible to achieve Feynman's goal, I'm surprised that more people don't attempt the importance threshold-modified version. Suppose we set the importance bar really, really high: what are the most important results that everyone should try to understand? Here's a very biased partial list: basic physics and mathematics (e.g., to the level of the Feynman Lectures); quantitative theory of genetics and evolution; information, entropy and probability; basic ideas about logic and computation (Godel and Turing?); ... What else? Dynamics of markets? Complex Systems? Psychometrics? Descriptive biology? Organic chemistry?
hsu  scitariat  feynman  giants  stories  aphorism  curiosity  interdisciplinary  frontier  signal-noise  top-n  discussion  caltech  problem-solving  big-picture  vitality  πŸŽ“  virtu  big-surf  courage  πŸ”¬  allodium  nietzschean  ideas  quixotic  accretion  learning  hi-order-bits 
february 2017 by nhaliday
Thinking Outside One’s Paradigm | Academically Interesting
I think that as a scientist (or really, even as a citizen) it is important to be able to see outside one’s own paradigm. I currently think that I do a good job of this, but it seems to me that there’s a big danger of becoming more entrenched as I get older. Based on the above experiences, I plan to use the following test: When someone asks me a question about my field, how often have I not thought about it before? How tempted am I to say, β€œThat question isn’t interesting”? If these start to become more common, then I’ll know something has gone wrong.
ratty  clever-rats  academia  science  interdisciplinary  lens  frontier  thinking  rationality  meta:science  curiosity  insight  scholar  innovation  reflection  acmtariat  water  biases  heterodox  πŸ€–  πŸŽ“  aging  meta:math  low-hanging  big-picture  hi-order-bits  flexibility  org:bleg  nibble  the-trenches  wild-ideas  metameta  courage  s:**  discovery  context  embedded-cognition  endo-exo  near-far  πŸ”¬  info-dynamics  allodium  ideas  questions  within-without 
january 2017 by nhaliday
soft question - Thinking and Explaining - MathOverflow
- good question from Bill Thurston
- great answers by Terry Tao, fedja, Minhyong Kim, gowers, etc.

Terry Tao:
- symmetry as blurring/vibrating/wobbling, scale invariance
- anthropomorphization, adversarial perspective for estimates/inequalities/quantifiers, spending/economy

fedja walks through his though-process from another answer

Minhyong Kim: anthropology of mathematical philosophizing

Per Vognsen: normality as isotropy
comment: conjugate subgroup gHg^-1 ~ "H but somewhere else in G"

gowers: hidden things in basic mathematics/arithmetic
comment by Ryan Budney: x sin(x) via x -> (x, sin(x)), (x, y) -> xy
I kinda get what he's talking about but needed to use Mathematica to get the initial visualization down.
To remind myself later:
- xy can be easily visualized by juxtaposing the two parabolae x^2 and -x^2 diagonally
- x sin(x) can be visualized along that surface by moving your finger along the line (x, 0) but adding some oscillations in y direction according to sin(x)
q-n-a  soft-question  big-list  intuition  communication  teaching  math  thinking  writing  thurston  lens  overflow  synthesis  hi-order-bits  πŸ‘³  insight  meta:math  clarity  nibble  giants  cartoons  gowers  mathtariat  better-explained  stories  the-trenches  problem-solving  homogeneity  symmetry  fedja  examples  philosophy  big-picture  vague  isotropy  reflection  spatial  ground-up  visual-understanding  polynomials  dimensionality  math.GR  worrydream  scholar  πŸŽ“  neurons  metabuch  yoga  retrofit  mental-math  metameta  wisdom  wordlessness  oscillation  operational  adversarial  quantifiers-sums  exposition  explanation  tricki  concrete  s:***  manifolds  invariance  dynamical  info-dynamics  cool  direction 
january 2017 by nhaliday
Information Processing: Advice to a new graduate student
first 3 points (tough/connected advisor, big picture, benchmarking) are key:

1. There is often a tradeoff between the advisor from whom you will learn the most vs the one who will help your career the most. Letters of recommendation are the most important factor in obtaining a postdoc/faculty job, and some professors are 10x as influential as others. However, the influential prof might be a jerk and not good at training students. The kind mentor with deep knowledge or the approachable junior faculty member might not be a mover and shaker.

2. Most grad students fail to grasp the big picture in their field and get too caught up in their narrowly defined dissertation project.

3. Benchmark yourself against senior scholars at a similar stage in their (earlier) careers. What should you have accomplished / mastered as a grad student or postdoc in order to keep pace with your benchmark?

4. Take the opportunity to interact with visitors and speakers. Don't assume that because you are a student they'll be uninterested in intellectual exchange with you. Even established scholars are pleased to be asked interesting questions by intelligent grad students. If you get to the stage where the local professors think you are really good, i.e., they sort of think of you as a peer intellect or colleague, you might get invited along to dinner with the speaker!

5. Understand the trends and bandwagons in your field. Most people cannot survive on the job market without chasing trends at least a little bit. But always save some brainpower for thinking about the big questions that most interest you.

6. Work your ass off. If you outwork the other guy by 10%, the compound effect over time could accumulate into a qualitative difference in capability or depth of knowledge.

7. Don't be afraid to seek out professors with questions. Occasionally you will get a gem of an explanation. Most things, even the most conceptually challenging, can be explained in a very clear and concise way after enough thought. A real expert in the field will have accumulated many such explanations, which are priceless.
grad-school  phd  advice  career  hi-order-bits  top-n  hsu  πŸŽ“  scholar  strategy  tactics  pre-2013  scitariat  long-term  success  tradeoffs  big-picture  scholar-pack  optimate  discipline  πŸ¦‰  gtd  prioritizing  transitions  s:***  benchmarks  track-record  s-factor  progression 
november 2016 by nhaliday
Thoughts on graduate school | Secret Blogging Seminar
I’ll organize my thoughts around the following ideas.

- Prioritize reading readable sources
- Build narratives
- Study other mathematician’s taste
- Do one early side project
- Find a clump of other graduate students
- Cast a wide net when looking for an advisor
- Don’t just work on one thing
- Don’t graduate until you have to
reflection  math  grad-school  phd  advice  expert  strategy  long-term  growth  πŸŽ“  aphorism  learning  scholar  hi-order-bits  tactics  mathtariat  metabuch  org:bleg  nibble  the-trenches  big-picture  narrative  meta:research  info-foraging  skeleton  studying  prioritizing  s:*  info-dynamics  chart  expert-experience  explore-exploit 
september 2016 by nhaliday
Why Information Grows – Paul Romer
thinking like a physicist:

The key element in thinking like a physicist is being willing to push simultaneously to extreme levels of abstraction and specificity. This sounds paradoxical until you see it in action. Then it seems obvious. Abstraction means that you strip away inessential detail. Specificity means that you take very seriously the things that remain.

Abstraction vs. Radical Specificity: https://paulromer.net/abstraction-vs-radical-specificity/
books  summary  review  economics  growth-econ  interdisciplinary  hmm  physics  thinking  feynman  tradeoffs  paul-romer  econotariat  🎩  πŸŽ“  scholar  aphorism  lens  signal-noise  cartoons  skeleton  s:**  giants  electromag  mutation  genetics  genomics  bits  nibble  stories  models  metameta  metabuch  problem-solving  composition-decomposition  structure  abstraction  zooming  examples  knowledge  human-capital  behavioral-econ  network-structure  info-econ  communication  learning  information-theory  applications  volo-avolo  map-territory  externalities  duplication  spreading  property-rights  lattice  multi  government  polisci  policy  counterfactual  insight  paradox  parallax  reduction  empirical  detail-architecture  methodology  crux  visual-understanding  theory-practice  matching  analytical-holistic  branches  complement-substitute  local-global  internet  technology  cost-benefit  investing  micro  signaling  limits  public-goodish  interpretation 
september 2016 by nhaliday
soft question - How do you not forget old math? - MathOverflow
Terry Tao:
I find that blogging about material that I would otherwise forget eventually is extremely valuable in this regard. (I end up consulting my own blog posts on a regular basis.) EDIT: and now I remember I already wrote on this topic: terrytao.wordpress.com/career-advice/write-down-what-youve-dβ€Œβ€‹one

fedja:
The only way to cope with this loss of memory I know is to do some reading on systematic basis. Of course, if you read one paper in algebraic geometry (or whatever else) a month (or even two months), you may not remember the exact content of all of them by the end of the year but, since all mathematicians in one field use pretty much the same tricks and draw from pretty much the same general knowledge, you'll keep the core things in your memory no matter what you read (provided it is not patented junk, of course) and this is about as much as you can hope for.

Relating abstract things to "real life stuff" (and vice versa) is automatic when you work as a mathematician. For me, the proof of the Chacon-Ornstein ergodic theorem is just a sandpile moving over a pit with the sand falling down after every shift. I often tell my students that every individual term in the sequence doesn't matter at all for the limit but somehow together they determine it like no individual human is of any real importance while together they keep this civilization running, etc. No special effort is needed here and, moreover, if the analogy is not natural but contrived, it'll not be helpful or memorable. The standard mnemonic techniques are pretty useless in math. IMHO (the famous "foil" rule for the multiplication of sums of two terms is inferior to the natural "pair each term in the first sum with each term in the second sum" and to the picture of a rectangle tiled with smaller rectangles, though, of course, the foil rule sounds way more sexy).

One thing that I don't think the other respondents have emphasized enough is that you should work on prioritizing what you choose to study and remember.

Timothy Chow:
As others have said, forgetting lots of stuff is inevitable. But there are ways you can mitigate the damage of this information loss. I find that a useful technique is to try to organize your knowledge hierarchically. Start by coming up with a big picture, and make sure you understand and remember that picture thoroughly. Then drill down to the next level of detail, and work on remembering that. For example, if I were trying to remember everything in a particular book, I might start by memorizing the table of contents, and then I'd work on remembering the theorem statements, and then finally the proofs. (Don't take this illustration too literally; it's better to come up with your own conceptual hierarchy than to slavishly follow the formal hierarchy of a published text. But I do think that a hierarchical approach is valuable.)

Organizing your knowledge like this helps you prioritize. You can then consciously decide that certain large swaths of knowledge are not worth your time at the moment, and just keep a "stub" in memory to remind you that that body of knowledge exists, should you ever need to dive into it. In areas of higher priority, you can plunge more deeply. By making sure you thoroughly internalize the top levels of the hierarchy, you reduce the risk of losing sight of entire areas of important knowledge. Generally it's less catastrophic to forget the details than to forget about a whole region of the big picture, because you can often revisit the details as long as you know what details you need to dig up. (This is fortunate since the details are the most memory-intensive.)

Having a hierarchy also helps you accrue new knowledge. Often when you encounter something new, you can relate it to something you already know, and file it in the same branch of your mental tree.
thinking  math  growth  advice  expert  q-n-a  πŸŽ“  long-term  tradeoffs  scholar  overflow  soft-question  gowers  mathtariat  ground-up  hi-order-bits  intuition  synthesis  visual-understanding  decision-making  scholar-pack  cartoons  lens  big-picture  ergodic  nibble  zooming  trees  fedja  reflection  retention  meta:research  wisdom  skeleton  practice  prioritizing  concrete  s:***  info-dynamics  knowledge  studying  the-trenches  chart  expert-experience  quixotic 
june 2016 by nhaliday
10 reasons Ph.D. students fail
Once a student has two good publications, if she convinces her committee that she can extrapolate a third, she has a thesis proposal.

Once a student has three publications, she has defended, with reasonable confidence, that she can repeatedly conduct research of sufficient quality to meet the standards of peer review. If she draws a unifying theme, she has a thesis, and if she staples her publications together, she has a dissertation.
advice  grad-school  phd  techtariat  planning  gotchas  scholar  πŸŽ“ 
may 2016 by nhaliday
For potential Ph.D. students
Ravi Vakil's advice for PhD students

General advice:
Think actively about the creative process. A subtle leap is required from undergraduate thinking to active research (even if you have done undergraduate research). Think explicitly about the process, and talk about it (with me, and with others). For example, in an undergraduate class any Ph.D. student at Stanford will have tried to learn absolutely all the material flawlessly. But in order to know everything needed to tackle an important problem on the frontier of human knowledge, one would have to spend years reading many books and articles. So you'll have to learn differently. But how?

Don't be narrow and concentrate only on your particular problem. Learn things from all over the field, and beyond. The facts, methods, and insights from elsewhere will be much more useful than you might realize, possibly in your thesis, and most definitely afterwards. Being broad is a good way of learning to develop interesting questions.

When you learn the theory, you should try to calculate some toy cases, and think of some explicit basic examples.

Talk to other graduate students. A lot. Organize reading groups. Also talk to post-docs, faculty, visitors, and people you run into on the street. I learn the most from talking with other people. Maybe that's true for you too.

Specific topics:
- seminars
- giving talks
- writing
- links to other advice
advice  reflection  learning  thinking  math  phd  expert  stanford  grad-school  academia  insight  links  strategy  long-term  growth  πŸŽ“  scholar  metabuch  org:edu  success  tactics  math.AG  tricki  meta:research  examples  concrete  s:*  info-dynamics  s-factor  prof  org:junk  expert-experience 
may 2016 by nhaliday
Answer to What is it like to understand advanced mathematics? - Quora
thinking like a mathematician

some of the points:
- small # of tricks (echoes Rota)
- web of concepts and modularization (zooming out) allow quick reasoning
- comfort w/ ambiguity and lack of understanding, study high-dimensional objects via projections
- above is essential for research (and often what distinguishes research mathematicians from people who were good at math, or majored in math)
math  reflection  thinking  intuition  expert  synthesis  wormholes  insight  q-n-a  πŸŽ“  metabuch  tricks  scholar  problem-solving  aphorism  instinct  heuristic  lens  qra  soft-question  curiosity  meta:math  ground-up  cartoons  analytical-holistic  lifts-projections  hi-order-bits  scholar-pack  nibble  giants  the-trenches  innovation  novelty  zooming  tricki  virtu  humility  metameta  wisdom  abstraction  skeleton  s:***  knowledge  expert-experience 
may 2016 by nhaliday
Scott Aaronson Answers Every Ridiculously Big Question I Throw at Him
Great interview. The discussion of how his research went early in his career and what he thinks about free will is particularly interesting. Should really get around to reading that paper of his.
physics  profile  quantum  interview  research  prof  πŸŽ“  reflection  popsci  aaronson  tcstariat  org:mag  org:sci 
may 2016 by nhaliday
Deliberate Grad School | Andrew Critch
- find a flexible program (math, stats, TCS)
- high-impact topic
- teach
- use freedom to visibly accomplish things
- organize seminar
- get exposure to experts
- learn how productive researchers work
- remember you don't have to stay in academia
academia  grad-school  advice  phd  reflection  expert  long-term  πŸŽ“  high-variance  aphorism  hi-order-bits  top-n  tactics  strategy  ratty  core-rats  multi  success  flexibility  metameta  s:*  s-factor  clever-rats  expert-experience 
may 2016 by nhaliday
Work hard | What's new
Similarly, to be a β€œprofessional” mathematician, you need to not only work on your research problem(s), but you should also constantly be working on learning new proofs and techniques, going over important proofs and papers time and again until you’ve mastered them. Don’t stay in your mathematical comfort zone, but expand your horizon by also reading (relevant) papers that are not at the heart of your own field. You should go to seminars to stay current and to challenge yourself to understand math in real time. And so on. All of these elements have to find their way into your daily work routine, because if you neglect any of them it will ultimately affect your research output negatively.
- from the comments
advice  academia  math  reflection  career  expert  gowers  long-term  πŸŽ“  aphorism  grad-school  phd  scholar  mathtariat  discipline  curiosity  πŸ¦‰  nibble  org:bleg  the-trenches  meta:research  gtd  stamina  vitality  s:**  info-dynamics  expert-experience 
april 2016 by nhaliday

bundles : academe ‧ growth ‧ stars

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