I'm on this mailing list where everybody is suddenly raving over     this new book "The       Information".  Amazon describes it as:
     
     In a         sense, The           Information is         a book about everything, from words themselves to talking drums,         writing and lexicography, early attempts at an analytical         engine, the telegraph and telephone, ENIAC, and the ubiquitous         computers that followed. But that's just the "History." The         "Theory" focuses on such 20th-century notables as Claude         Shannon, Norbert Wiener, Alan Turing, and others who worked on         coding, decoding, and re-coding both the meaning and the myriad         messages transmitted via the media of their times. In the         "Flood," Gleick explains genetics as biology's mechanism for         informational exchange--Is a chicken just an egg's way of making         another egg?--and discusses self-replicating memes (ideas as         different as earworms and racism) as information's own evolving         meta-life forms. Along the way, readers learn about music and         quantum mechanics, why forgetting takes work, the meaning of an         "interesting number," and why "[t]he bit is the ultimate         unsplittable particle." What results is a visceral sense of         information's contemporary precedence as a way of understanding         the world, a physical/symbolic palimpsest of self-propelled         exchange, the universe itself as the ultimate analytical engine.         If Borges's "Library of Babel" is literature's iconic cautionary         tale about the extreme of informational overload, Gleick sees         the opposite, the world as an endlessly unfolding opportunity in         which "creatures of the information" may just recognize         themselves. --Jason Kirk
     
     I don't know about you, but I can't piece together anything     meaningful other than "Wow wow wow!!!!!"
     
     I'm really curious to hear if anybody who reads the book actually     changes their opinion on anything as a result.  I fear a lot of     these books just have "something for everybody" such that you walk     away feeling stronger in your belief no matter what that belief is.      Sorta like MSG: it makes everything taste better, without having any     flavor by itself.  I'd love to hear somebody say "I've held this     passionate belief my entire life, but as a result of reading this     book I've changed my mind."
     
     
     Somewhat related, I spoke at a       conference recently, and the other presenters had these really     incredible, well-researched, inspiring presentations.  But I     realized afterwards that a major problem with so many of these broad     trend analyzes is they lack statistical relevance.
     
     For example, I find everybody talks about Twitter, Facebook, Google,     and a half-dozen mega names -- and then draws inferences based on     them.  But that's equivalent to "averaging the exceptions", which     just isn't a valid technique: the problem with outliers is they're     *outliers* and by definition defy the baseline trends.  They are too     few and too different to be summarized in any meaningful way.
     
     Rather, I think these business-fad, pop-psychology,     averaging-the-exception techniques just create hysteria and     excitement where perhaps none is really warranted.  Even if they're     100% "accurate", they're so incredibly imprecise as to be     non-actionable.  Said another way, even if you're totally right on     predicting the wave, if you can't say with any certainty the time     and magnitude when it will hit, it's not worth getting excited     about.
     
     Don't get me wrong, hysteria and excitement are great ways to sell     books or promote products.  But as the people being sold and     promoted *to*, it's in our interests to take these fantastic claims     -- each of which seems increasingly fantastic with increasing     frequency -- with a corresponding amount of skepticism and     composure.
     
     
   
avg(exception) = nothing
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