“If you made the slight effort of looking it up, you wouldn’t have tweeted it.”

“If you look at them, they have a fairly consistent character. They give the impression of being something that someone just thought of. You’re walking down the street, a thought comes to you, you tweet it.

If you thought for two minutes, or if you had made the slight effort involved in looking up the topic, you wouldn’t have sent it.”

On 140 character emails, and tweets, in Noam Chomsky, Power Systems: Interviews with David Barsamian, p. 105

[Update] As a solution [?], the same, on books:

“Reading a book doesn’t just mean turning the pages. It means thinking about it, identifying parts that you want to go back to, asking how to place it in a broader context, pursuing the ideas […] Reading a book is an intellectual exercise, which stimulates thought, questions, imagination.”


[Updated] Being a polymath is good for innovation

Or, “a man can do all things if he will.”

From Aeon Magazine:

“To come up with [innovative] ideas, you need to know things outside your field. What’s more, the further afield your knowledge extends, the greater potential you have for innovation.”

“intense study brings rewards that are impossible to achieve by casual application”

“Monopathy, or over-specialisation, eventually retreats into defending what one has learnt rather than making new connections.”

As well as examples of cross-disciplinary innovation, potential problems with the division of labor, and why children learn “all the time.”

Article at:

Update: A related article from Wired:

“The most exciting inventions occur at the boundaries of disciplines”

“As Robert Twigger noted, ‘Invention fights specialisation at every turn.’ ”

“Mathematics is a gift, an unbelievably useful tool for understanding our surroundings.”

“More generally, the world of business and entrepreneurship actively encourages those who see connections between disciplines. One who can recognize a relationship between two disparate fields of ideas will more likely come up with the next, big, new thing. That’s investment gold.”

Article at:

“Should we believe more in Big Data or in magic?”

From a Reuters column:

“Data analysis is very similar to performing magic. With great skill you can pull things together and create the perception of surprising relationships.”

“lack of talent is not just an impediment; it’s a potential source of danger.”

“Often what’s most interesting isn’t the statistical relationship itself, but the data that was required to find it.”

On “Crossover appeal”

Statistician Andrew Gelman makes an insightful remark, one to keep in mind not just when reading published scientific papers:

Levitt buttresses his argument with the statement, “Chris Goodall [the person who made the walking/driving comparison] is no right-wing nut; he is an environmentalist and author of the book How to Live a Low-Carbon Life.” How relevant is this? Even a “right-wing nut” could make a good point, right?

More to the point, I think we have to be careful about automatically trusting “crossover” arguments. Do we have to believe something, just because it comes from somebody who we wouldn’t expect to say it? I worry that this sort of crossover appeal is so appealing that otherwise-skeptical commentators (such as Levitt) forget their usual skepticism.

[Emphasis mine.]

“What are the words I’m supposed to use in this conversation?”

“What are the words I’m supposed to use in this conversation?” may be a common mode in which a less mature and non-risk-taking student operates when interacting with job recruiters.

And yet, this should not be the mindset of “a 20-year-old who should have bright ideas and enthusiasm,” formed in the liberal arts, according to a recruiter from a consulting company, cited in a recent NYT article, “How to get a job with a philosophy degree.”

The article has more on the tensions between education for it’s own sake, and “getting that degree [only] so that I can get a job,” including insight into “student branding.”

Ever seen a twitter-activity map? Then you must consider Big-Data bias…

“Over the weekend of Apple’s April 3 release of the iPad, 73% of circulated tweets were favorable toward the iPad, but 26% expressed disappointment that the iPad could not replace the iPhone, according to a study.”

If you’re not too careful, you could conclude that sentiment towards the iPad was largely favorable. But you would probably have made a biased conclusion.

This is the point that Harvard Business Review’s Kate Crawford makes in a recent article, “The Hidden Biases in Big Data.” With a data sample, it is always critical to ask whether the sample is representative of the target population.

Thus, considering the iPad sentiment example, a key questions is: are the people who tweeted about Apple’s iPad over that weekend (the sample) representative of all the people who have, or even could have, interacted with the iPad during that time (the target population)?

Some excerpts from the article:

  • Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves.
  • Data and data sets are not objective; they are creations of human design.
  • We get a much richer sense of the world when we ask people the why and the how not just the “how many”.

Read the article here.


Image: TheAtlantic.com