Home' Technology Review : March April 2012 Contents Reviews 75
When Alan Turing was born 100 years
ago, on June 23, 1912, a computer
was not a thing—it was a person. Com-
puters, most of whom were women, were
hired to perform repetitive calculations
for hours on end. The practice dated back
to the 1750s, when Alexis-Claude Clairaut
recruited two fellow astronomers to
help him plot the orbit of Halley’s comet.
Clairaut’s approach was to slice time into
segments and, using Newton’s laws, cal-
culate the changes to the comet’s position
as it passed Jupiter and Saturn. The team
worked for five months, repeating the pro-
cess again and again as they slowly plotted
the course of the celestial bodies.
Today we call this process dynamic sim-
ulation; Clairaut’s contemporaries called
it an abomination. They desired a science
of fundamental laws and beautiful equa-
tions, not tables and tables of numbers.
Still, his team made a close prediction of
the perihelion of Halley’s comet. Over the
following century and a half, computational
methods came to dominate astronomy and
By the time Turing entered King’s Col-
lege in 1931, human computers had been
employed for a wide variety of purposes—
and often they were assisted by calculating
machines. Punch cards were used to control
looms and tabulate the results of the Ameri-
can census. Telephone calls were switched
using numbers dialed on a ring and inter-
preted by series of 10-step relays. Cash reg-
isters were ubiquitous. A “millionaire” was
not just a very rich person—it was also a
mechanical calculator that could multiply
and divide with astonishing speed.
All these machines were fundamentally
limited. They weren’t just slower, less reli-
able, and dramatically poorer in memory
than today’s computers. Crucially, the cal-
culating and switching machines of the
1930s—and those that would be introduced
for many years to come—were each built for
a specific purpose. Some of the machines
could perform manipulations with math,
some could even follow a changeable
sequence of instructions, but each machine
had a finite repertoire of useful operations.
The machines were not general-purpose.
They were not programmable.
Meanwhile, mathematics was in trouble.
In the early 1920s the great German
mathematician David Hilbert had pro-
posed formalizing all of mathematics in
Turing’s Enduring Importance
The path computing has taken wasn’t inevitable. Even today’s
machines rely on a seminal insight from the scientist who cracked
Nazi Germany’s codes.
By S IMSON L. GarfINkeL
exhibit in Gagosian Gallery locations world-
wide, follow the simple rule that no color
is repeated in a single picture. The appear-
ance of the result is dictated by the size of
the dots and of the canvas. Hirst’s titles are
all the names of pharmaceutical products,
implying that actual human emotions are
chemical in origin, just as the joy of the
pictures is created by artifice.
But Richter’s abstract pictures have much
more often been loose and what art histori-
ans call “painterly.” Some have been made
with the aid of a squeegee, a tool for smear-
ing, which is pulled over the work again and
again, unpeeling some sections of paint and
smudging others. This, too, is a process open
to chance. Richter describes it as a succes-
sion of yes/no decisions, a process of accept-
ing or rejecting what has happened until the
artist is satisfied by the result. Other than
that, he is not entirely in control. The paint-
ings can suggest a glimpse of sunlight fil-
tered through leaves, or reflections on water.
Richter has published a monumental
album of his photographic sources, enti-
tled Atlas, and he has made whole books
of photographs, such as Wald (2008), a little
masterpiece consisting of shots of a wood
near Cologne. The latter is, like so much of
his work, about how randomness—in this
case a dense tangle of trunks and twigs—can
generate beauty and a sort of order. But even
such important works as Wald and Atlas are
ancillary to his painting.
“I make a lot of photographs,” Richter
told me, “but I am not very interested in
photography as an art. They don’t touch
me that much.” What affects him most,
perhaps, is the Vermeer effect: the interac-
tion between the cool, apparently objective
image created by a piece of equipment—a
camera—and the free play of paint.
MaRTiN GayfoRd is chiEf aRT cRiTic foR B lo o MBERG
NEws. hE REvi EwEd david hockNEy’s NEw vidEo i N s Ta l -
laTioNs iN ThE sEpTEMBER/ocToBER 2011 issuE of Tech-
Machine learning Even before digital com-
puters existed, Turing described the fundamental
mathematical principles that would govern them.
see more of Richter’s work:
Mar12 Reviews.indd 75
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