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Most broadly, looking through the lens of computer science can teach us about the nature of the human mind


the meaning of rationality, and the oldest question of all:
how to live. Examining cognition as a means of solving the
fundamentally computational problems posed by our
environment can utterly change the way we think about
human rationality.
The notion that studying the inner workings of
computers might reveal how to think and decide, what to
believe and how to behave, might strike many people as
not only Wildly reductive, but in fact misguided. Even if
computer science did have things to say about how to
think and how to act, would we want to listen? We look at
the AIs and robots of science fiction, and it seems like
theirs is not a life any of us would want to live.
In part, that’s because when we think about computers,

we think about coldly mechanical, deterministic systems:
machines applying rigid deductive logic, making decisions
by exhaustively enumerating the options, and grinding out
the exact right answer no matter how long and hard they
have to think. Indeed, the person who first imagined
computers had something essentially like this in mind.
Alan Turing defined the very notion of computation by an
analogy to a human mathematician who carefully works
through the steps of a lengthy calculation, yielding an
unmistakably right answer.
So it might come as a surprise that this is not what
modern computers are actually doing when they face a
difficult problem. Straightfoiward arithmetic, of course,
isn’t particularly challenging for a modern computer.
Rather, it’s tasks like conversing with people, fixing a
corrupted file, or winning a game of Go—problems where
the rules aren’t clear, some of the required information is
missing, or finding exactly the right answer would require
considering an astronomical number of possibilities—that
now pose the biggest challenges in computer science. And
the algorithms that researchers have developed to solve
the hardest classes of problems have moved computers

away from an extreme reliance on exhaustive calculation.
Instead, tackling real—world tasks requires being
comfortable with chance, trading off time with accuracy,
and using approximations.
As computers become better tuned to real-world
problems, they provide not only algorithms that people
can borrow for their own lives, but a better standard
against which to compare human cognition itself. Over the
past decade or two, behavioral economics has told a very
particular story about human beings: that we are irrational
and error-prone, owing in large part to the buggy,
idiosyncratic hardware of the brain. This self—deprecating
story has become increasingly familiar, but certain
questions remain vexing. V\7hy are four-year-olds, for
instance, still better than million—dollar supercomputers at
a host of cognitive tasks, including vision, language, and
causal reasoning?
The solutions to everyday problems that come from
computer science tell a different story about the human
mind. Life is full of problems that are, quite simply, hard.
And the mistakes made by people often say more about the
intrinsic difficulties of the problem than about the fallibility of human brains. Thinking algorithmically about
the world, learning about the fundamental structures of
the problems we face and about the properties of their
solutions, can help us see how good we actually are, and
better understand the errors that we make.
In fact, human beings turn out to consistently confront
some of the hardest cases of the problems studied by
computer scientists. Often, people need to make decisions
while dealing with uncertainty, time constraints, partial
information, and a rapidly changing world. In some of
those cases, even cutting-edge computer science has not
yet come up with efficient, always-right algorithms. For
certain situations it appears that such algorithms might
not exist at all.
Even where perfect algorithms haven’t been found,
however, the battle between generations of computer
scientists and the most intractable real-world problems
has yielded a series of insights. These hard-won precepts
are at odds with our intuitions about rationality, and they
don’t sound anything like the narrow prescriptions of a
mathematician trying to force the world into clean, formal
lines. They say: Don’t always consider all your options.









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About Unknown

ZAKARIA AL BAZZAR, 19 yo, university student. love everything about new tech, and I'm sharing it with you :)
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