Ready for Robots?July/August 2019 193A MIND OF ITS OWN?
Ironically, even though McCarthy and
Minsky’s term entered the lexicon, the
most promising technique today,
called “deep learning,” is based on a
statistical approach that was anathema
to them. From the 1950s to the 1990s,
most o was about programming
computers with hand-coded rules. The
statistical approach, by contrast, uses
data to make inferences based on
probabilities. In other words, went
from trying to describe all the features
o a cat so that a computer could recog-
nize one in an image to feeding tens o
thousands o cat images to an algo-
rithm so the computer can gure out the
relevant patterns for itself. This “ma-
chine learning” technique dates back to
the 1950s but worked only in limited
cases then. Today’s much more elabo-
rate version—deep learning—works
exceptionally well, owing to staggeringWiener later backed away from his
most apocalyptic warnings. But today,
as has begun to invade almost every
aspect o life in developed societies,
many thinkers have returned to the big
questions Wiener started asking more
than hal a century ago. In Possible Minds,
25 contributors, including a number o
the most prominent names in the eld,
explore some o the eye-opening possi-
bilities and profound dilemmas that
presents. The book provides a fascinat-
ing map o ’s likely future and an
overview o the dicult choices that will
shape it. How societies decide to weigh
caution against the speed o innovation,
accuracy against explainability, and
privacy against performance will deter-
mine what kind o relationships human
beings develop with intelligent ma-
chines. The stakes are high, and there
will be no way forward in without
confronting those tradeos.DENIS
BALIBOUSE/ REUTERSThought experiment: an AI robot at a summit in Geneva, Switzerland, June 201723_Cukier_Blues.indd 193 5/20/19 3:37 PM