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   I have slightly edited the post below by my friend the big O.
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    After all my complaining I have reached an actual argument about scientists or, better put, the science class.  They lack rigorous checks and balances.
    To think that science can effectively check itself for error and bias just because they’re supposed to is analogous to thinking that the house of representatives can effectively check itself for error and bias just because they’re supposed to. But we know that the job of congressman frequently attracts a type of personality. We know there is an attractive glory to accomplishment in government as there is an attractive glory to accomplishing something in science.  We know that money provides an incentivizing role in even the noblest of endeavors.
    And so in government we have branches whose explicit role is to check and balance one another.  And we have a press whose job is to check and seek error with the government itself. And we have a population who feel no embarrassment at checking everyone: we scrutinize the press, we critique the government in general, and we attack specific branches of government, all the while recognizing that government by the people (etc.) is prone to errors and biases specifically because it is a government run by people.
     This is not the attitude we have with the class of people who conduct science.  The scientist class has reached such a rarefied status that it lacks equivalent checks and balances. We expect scientists to nobly check themselves.  But i argue they cannot because they’re people.  We need more rigorous outside skepticism than we currently have.
    Science has the hardest arguments on earth to develop, prove, justify, and explain because the arguments of science are targeted at revealing something close to objective truth. there are more obstacles and unseen variables between scientific theory and proof than in any other field.  I think we would be better off to consider non-scientist (a “scientist” today being someone who is sanctioned by a university to be labeled as such) checking and balancing as part of – not apart from – the scientific process.
     I like the idea of retaining a unembarrassed and reasoned skepticism of the “truth” offered by scientists – particularly in the weak sciences – and instead accept effectiveness (e.g. when science becomes technology) as truth.  When something – a theory or an experiment – works in our daily lives we can label that something as “true enough to be effective” and realize that as an auspicious label.  The rest should invite continued checking and balancing from both in and outside the scientific class.
   Some links below
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  And then in another email, I added this as a response to my critics:
  The one thing I do not attack is the scientific method or reason.
   I say the scientific method is not currently being employed to an extent that it could be and we’re worse off for it. As evidence of this I see the very frequent conflation of science – which is an effective process – with scientists – whom I consider to be as flawed and for the same reasons as any other profession.  (That npr study conforms with my personal experience that scientists my be more justifying of their bias than others).  This conflation leads to a citizenry reluctant to be skeptical of scientists and scientific work because they fear they are questioning science.  They are not. they are a part (apparently unknowingly) of science and their reluctance to analyze, question, investigate, criticize scientists and scientist’s work leads to a weakening of science.

The peer review you cite is precisely what I am labeling inadequate.  What if a congressman suggested his law was good because it had been peer reviewed?  What if he said he had special training as a lawyer or an economist or a historian?  Would that be a satisfying rational not to have separate gov’t branches, press, or citizenry actively challenge his work?  Scientific peer review is equivalent to gov’t peer review – it is necessary but less adequate than broadening that peer review to others currently only limitedly involved or allowed in the process.

   Again:  re: the scientific method, I don’t believe that a science that includes only an academia certified science class does or even can adequately follow the scientific method to its fullest rigor anymore than congress can adequately run the government to its fullest rigor minus the critical analysis of outside agencies such as other branches of gov’t, the press, and citizenry.

Imitation is the greatest form of flattery.

 

  1. The just plain fun retro turbo Pascal editor.
  2. The Rush movie reminds me of the “Who is your arch-enemy?” post (see also 37 signals) and “The smackdown learning model“.
  3. The rule of three for re-usable code.
  4. The PCP Theorem and Zero Knowledge Proofs suggest avenues of attack on P vs NP
  5. God’s number is 20 (assuming God invented Rubik’s cube)
  6. Why Your Brain Needs More Downtime (Scientific American, Link from Carl)
  7. Keith’s Photos of Caves, Critters, and Nature

 

I wonder if there is some way to identify which internet queries are the most frequently asked and least frequently successfully answered.

I bring this up because my three most popular blog posts are:

  1. 100 Most useful Theorems and Ideas in Mathematics
  2. Standard Deviation of Sample Median
  3. “Deep Support Vector Machines for Regression Problems”

The second one about the Median was originally posted to answer the question “What has the most variability: the sample mean or the sample median?”  But, I think that most of the people who are referred to this post from Google are trying to find the answer to “What is the standard deviation of the sample median?” The second question is quite practical for people doing statistics and it is difficult to find an answer to this question on the internet.  An approximation to the answer is given in the post, but the post really was not designed to answer this question, so I imagine that many people read the article and find the approximation to be insufficient or they don’t even understand the approximation given.  So I wonder if there are many questions posed to Google that remain unanswered after Googling. It would be a great service if such questions could be answered by an expert and indexed by Google.

The 100 theorems post and the deep support post are similar.  Both are fairly short simple answers to potential internet queries. The 100 theorems post is an answer to “What are the most useful theorems in mathematics?” and the “deep support” article answers the very technical AI question “What is a deep support vector machine?”

Maybe I should more posts like those.

 

Cheers, Hein

The last week of classes and finals week were kind of brutal both for me and my students.  (It wasn’t that bad, but it was a lot of work.)  So, I’m taking some time off blogging.  Cheers, Hein

This is just too cute.  Type

 

data:text/html, <html contenteditable>

 

into the URL tab and then type in the blank page.

 

Courtesy of Jose Jesus Perez Aguinaga  https://coderwall.com/p/lhsrcq  !

 

In “Does Luck Matter More Than Skill?“, Cal Newport writes

<success of a project> = <project potential> x <serendipitous factors>,

where <project potential> is a measure of the rareness and value of your relevant skills, and the value of the serendipitous factors is drawn from something like an exponential distribution.

and

If you believe that something like this equation is true, then this approach of becoming as good as possible while trying many different projects, maximizes your expected success.

Indeed, we can call this the Schwarzenegger Strategy, as it does a good job of describing his path to stardom. Looking back at his story, notice that he tried to maximize the potential in every project he pursued (always “putting in the reps”). But he also pursued a lot of projects, maximizing the chances that he would occasionally complete one with high serendipity. His breaks, as described above, all required both rare and valuable skills, and luck. And each such project was surrounded in his life by other projects in which things did not turn out so well.

If success is measured in dollars, then I bet the distributions of <serendipitous factors> have fat 1/polynomial tails because there are a lot of people with great skills, but the wealth distribution among self-made billionaires is something like C/earnings^1.7.  For many skills, like probability of hitting a baseball, the amount of skill seems to be proportional to log(practice time) plus a constant.  For other skills, like memorized vocabulary, the amount of skill seems proportional to (study time)^0.8 or the Logarithmic Integral Function.  Mr Newport emphasizes the “rareness” of skill also.  Air is important, but ubiquitous, so no one charges for it despite it’s value.  In baseball, I imagine that increasing your batting average a little bit can increase your value a lot.  I wonder what the formulas for <project potential> are for various skills.  If we could correctly model Newport’s success equation, we could figure out the correct multi-armed bandit strategy for maximizing success.  (Maybe we could call it the Schwarzenegger Bandit Success Formula.) You may even be able to add happiness into the success formula and still get a good bandit strategy for achieving it.

According to this graph

Figure 10 From THE LONG-TERM IMPACTS OF TEACHERS: TEACHER VALUE-ADDED AND STUDENT OUTCOMES IN ADULTHOOD by Chetty, Friedman, and Jonah E. Rockoff

high quality elementary school teachers increase the lifetime earnings of their students by about $200,000 per child.

 

“Aaron Swartz (1986-2013)”

Check out the Nuit Blanche posts “Predicting the Future: The Steamrollers” and “Predicting the Future: Randomness and Parsimony” where Igor Carron repeats the well known mantra of Moore’s law that always seems to catch us by surprise. Carron’s remarks on medicine surprised me but also I thought, “I should have guessed that would happen” while reading the articles.

At the top 500 website, I notice that the main CPUs are made only by four companies: IBM, Intel, AMD, and Nvidia.  HP was squeezed out in 2008, leaving only four players.  It makes me wonder if the trend toward fewer manufacturers will continue.  Also, the both the #1 super computer and #500 did not keep up with the general trendline over the last two or three years.  On the other hand, the average computational power of the top 500 has stayed very close to the trendline which increases by a factor of 1.8 every year.

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