“Some humans would do anything to see if it was possible. If
you put a large switch in a cave somewhere, with a sign
saying ‘End-of-the-World Switch. PLEASEDONOTTOUCH’, the
paint wouldn’t even have time to dry.”
— Terry Pratchett, Thief of Time
I’ll print out this quote so I can point to it when I’m
discussing edge cases and someone tells me “nobody will do
that” or “it will never happen.” Famous last words.
Los Angeles and Hollywood are centers of movie studios,
upcoming celebrities, and American car culture. However, if
you drive west, you will end up in Santa Monica and Venice
where a different lifestyle takes place: surfing, outdoor
gyms, acrobatics, lifeguards, walks along the beach. And of
course, incredible sunsets.
Buy groceries, reply to emails, pick up the package from the
post office, organize the weekend trip, revise the insurance
premium. The list is like a hydra: I cross off one and two
more appear. It is, of course, my personal “to do” list. No
work, no side projects, just stuff that needs to be done for
life to proceed in a regular way.
I winced every time I glanced at the list. If I finished the
first couple of tasks—and this is usually all I could do in
one day—I would get no sense of accomplishment because dozens
of uncompleted tasks would stare back at me and remind me I
will never attain the peace of mind.
Why was just looking at the unfinished list bothering me? I
couldn’t pinpoint the problem until I heard about the
Zeigarnik effect. Named after the psychologist who first
studied it, the Zeigarnik effect shows that people will think
more about uncompleted or interrupted tasks than completed
Andrew Ng, a co-founder of Coursera and the chief scientist at
Baidu Research, recently gave a talk at Deep Learning
School. At one point in his presentation (10:16:42),
he described the current process of building desktop and
mobile apps in the industry: product managers and designers
iterate on ideas and interfaces before implementation.
However, with rising popularity of machine learning and its
complexity, it is difficult to grasp what is achievable with
those algorithms. Consequently, it is hard to write product
requirements or specification, so Andrew often receives the
question: “What can artificial intelligence or deep learning do?”
He offers two rules of thumb that, while not perfect or
complete, are helpful in thinking what is possible with
Anything that a person can do in less than one second.
Predicting an outcome of a sequence of events.
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