I've been occasionally helping a blind friend of mine with his PC since 2005. His screenreader talks at about 600wpm or more and after 20 years of practice I can't understand it.
I speedread and can read text faster than his computer's voice -- but not by much. It is very impressive, and very hard to use.
But I am the only sighted person he's met who can use his PC. I navigate Windows mostly by keyboard, and he has no mouse. It slows me down slightly but I can still use it.
(He does have a screen on his family computer, so sighted family and friends can watch films with him. His work one has no screen.)
For me, this was the key paragraph (the paper is basically a history of how the second "culture" mentioned here came to predominate in data science):
"In 2001, the renegade statistician Leo Breiman described 'two cultures' of using statistical models 'to reach conclusions from data.' The first 'assumes that the data are generated by a given stochastic data model.' The second 'uses algorithmic models and treats the data mechanisms as unknown.' This essay sketches the diverse sources of this second algorithmic culture, one stemming more from an engineering culture of predictive utility than from a scientific culture of truth."
As an historian, I was also interested by the part in the conclusion that drew a parallel between recent conceptual shifts among historians of science and data scientists.* Plus I just think it's cool that historians are now starting to really dig into the intellectual history of machine learning in the same way that they study, say, the history of biology or physics.
*This passage: "For at least three decades, professional historians of science have pushed against a vision of science modeled on theoretical physics; we now celebrate the diverse forms of knowledge focused on the careful empirical study of particular things. Exponents of data-focused computational science have a sur- prisingly similar evolution. Just as history of science embraced the study of the particular as it disconnected from a Cold War prioritizing of theory, the data sciences moved beyond the aggregates of mathematical statistics to draw upon granular data sets to characterize particular things—individual people, diseases, films. In a key manifesto celebrating the 'unreasonable effectivenessof data,' three Google researchers argued in terms that echo humanist denun- ciations of reductionist knowledge: “sciences that involve human beings rather than elementary particles have proven more resistant to elegant mathematics.” Something else is needed. 'Perhaps when it comes to natural language proces- sing and related fields, we’re doomed to complex theories that will never have the elegance of physics equations. But if that’s so, we should...embrace complexity and make use of the best ally we have: the unreasonable effective- ness of data.'"
I used to have a similar feeling about not "improving" enough, until I realised something: doing all that messing about (watching TV, films, reading books) makes you more human.
When I'm not at a local meet-up, or conference, most of my conversation revolves around non-programming stuff. Most people don't want to hear my opinions on Python, Django, Scala, JavaScript etc. but they do want to know if I saw that ludicrous display last night.
If you want to think of it in "self-improvement" terms, then imagine you're working on your personal database of trivia that you can talk about.
Hacker Cinema
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I've been occasionally helping a blind friend of mine with his PC since 2005. His screenreader talks at about 600wpm or more and after 20 years of practice I can't understand it.
I speedread and can read text faster than his computer's voice -- but not by much. It is very impressive, and very hard to use.
But I am the only sighted person he's met who can use his PC. I navigate Windows mostly by keyboard, and he has no mouse. It slows me down slightly but I can still use it.
(He does have a screen on his family computer, so sighted family and friends can watch films with him. His work one has no screen.)
For me, this was the key paragraph (the paper is basically a history of how the second "culture" mentioned here came to predominate in data science):
"In 2001, the renegade statistician Leo Breiman described 'two cultures' of using statistical models 'to reach conclusions from data.' The first 'assumes that the data are generated by a given stochastic data model.' The second 'uses algorithmic models and treats the data mechanisms as unknown.' This essay sketches the diverse sources of this second algorithmic culture, one stemming more from an engineering culture of predictive utility than from a scientific culture of truth."
As an historian, I was also interested by the part in the conclusion that drew a parallel between recent conceptual shifts among historians of science and data scientists.* Plus I just think it's cool that historians are now starting to really dig into the intellectual history of machine learning in the same way that they study, say, the history of biology or physics.
*This passage: "For at least three decades, professional historians of science have pushed against a vision of science modeled on theoretical physics; we now celebrate the diverse forms of knowledge focused on the careful empirical study of particular things. Exponents of data-focused computational science have a sur- prisingly similar evolution. Just as history of science embraced the study of the particular as it disconnected from a Cold War prioritizing of theory, the data sciences moved beyond the aggregates of mathematical statistics to draw upon granular data sets to characterize particular things—individual people, diseases, films. In a key manifesto celebrating the 'unreasonable effectivenessof data,' three Google researchers argued in terms that echo humanist denun- ciations of reductionist knowledge: “sciences that involve human beings rather than elementary particles have proven more resistant to elegant mathematics.” Something else is needed. 'Perhaps when it comes to natural language proces- sing and related fields, we’re doomed to complex theories that will never have the elegance of physics equations. But if that’s so, we should...embrace complexity and make use of the best ally we have: the unreasonable effective- ness of data.'"
I used to have a similar feeling about not "improving" enough, until I realised something: doing all that messing about (watching TV, films, reading books) makes you more human.
When I'm not at a local meet-up, or conference, most of my conversation revolves around non-programming stuff. Most people don't want to hear my opinions on Python, Django, Scala, JavaScript etc. but they do want to know if I saw that ludicrous display last night.
If you want to think of it in "self-improvement" terms, then imagine you're working on your personal database of trivia that you can talk about.