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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.


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


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.

I just had to pass along this link from jwz’s blog.

If you want hits on your blog, write about an article that is being read by thousands or millions of people.  Some of those readers will Google terms from the article. Today I blogged very briefly about the NY Times article “Scientists See Promise in Deep-Learning Programs” and I was surprised at the number of referrals from Google.  Hmm, maybe I should blog about current TV shows (Big Bang? Mythbusters?), movies (Cloud Atlas?), and video games (Call of Duty?).   Carl suggested that I apply deep learning, support vector machines, and logistic regression to estimate the number of hits I will get on a post.  If I used restricted Boltzmann machines, I could run it in generative mode to create articles  :)      I was afraid if I went down that route I would eventually have a fantastically popular blog about Britney Spears.

In “Church: a language for generative models“, Goodman, Mansinghka, Roy, Bonawitz, and Tenenbaum introduce the probabilistic computer language “Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset.”  There will be a workshop on probabilistic programming at NIPS (which I first read about at the blog Statistical Modeling, Causal Inference, and Social Science).  Here is a cool tutorial.

I was reading “Around the Blogs in 80 Summer Hours” at Nuit Blanche and these two links caught my eye:

Topological Data Analysis

Implementation: BiLinear Modelling Via Augmented Lagrange Multipliers (BLAM)

In “Hashing Algorithms for Large-Scale Learning” Li, Shrivastava, Moore, and Konig (2011) modify Minwise hashing by storing only the $b$ least significant bits.  They use the $b$ bits from $k$ hashing functions as features for training a support vector machine and logistic regression classifiers.  They compare their results against Count-Min, Vowpal Wabbit, and Random hashes on a large spam database.  Their algorithm compares favorably against the others.


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