Fortune

FOREWORD
SPRINGTIME
FORA.I.
ON FEB. 24, 1956, ARTHUR LEE SAMUEL played a game of checkers on
television. His opponent: a 36-bit vacuum tube computer made by
International Business Machines.
Samuel, then a 55-year-old researcher at IBM, had painstakingly
assigned each of the 64 squares on the checkerboard a diferent set of
machine-word identifiers, and done the same with each piece on each
square. Then he programmed his IBM 701 computer to think: that is, to
consider a few possible checkers moves “by evaluating the resulting board
positions much as a human player might do,” Samuel would later write.
“ ‘Looking ahead’ is prepared for by computing all possible next moves,
starting with a given board position,” he explained. For each of those
potential moves, the computer would redraw the board in its electronic
brain with “the old board positions being saved to facilitate a return
to the starting point,” and then the process would repeat. When the
indicated move didn’t result in a better outcome, the IBM 701 would try
another one, and so forth, until the machine was successful.
In short, Samuel had programmed the “digital computer to behave in
a way which, if done by human beings or animals, would be described as
involving the process of learning,” he said.
Before the televised match, IBM president Tom Watson—the namesake
of IBM’s current-model thinking machine—predicted his company’s
shares would soar after the demonstration. They did just that.
What followed was a heady period of excitement during the 1950s and
1960s, when computer scientists sketched out dreams and designs of
advanced problem-solving machines that, in many cases, mirrored the
robotic creations of science fiction novels.
What followed that, however, was a long reality check—a period that,
it turns out, was far less “cyborg” than incremental science. Over the next
half-century, many of the most ambitious fantasies of artificial intelligence
were often met with prosaic, real-world limitations. The progress
was genuine, of course—from mathematical breakthroughs to major
advances in computing power. But still the perceived failures led to long
fallow periods that many in the field dubbed “A.I. winters.”
Well, it’s spring again in the realm of A.I.—and the ambition (and
hype) are blooming big-time. Big companies are now feverishly gobbling
up startups—firms that are teaching machines to master the idiosyncrasies
of human conversation, to expertly recognize the world around
them, and to instantly scan terabytes of data to discover patterns that no
mere mortal could recognize.
www.t.me/velarch_official
Yes, a lot has changed, as
we explore in our A.I. Special
Report this issue (see the package
of articles that begins on
page 54). But as writers Jonathan
Vanian and Vauhini Vara
show, it’s time for another reality
check. As the capacity of these
algorithms grows exponentially,
so do the questions about their
potential biases and their risky
assumptions. It’s an ongoing
lesson that requires our own
attempt at deep learning. The
success of this latest stage of A.I.
development may depend on it.
Which brings me to our cover
story. As “thoughtful” as some
machines are becoming, there
are none (yet) that can mimic
the spark of creative energy at
the heart of entrepreneurship.
Creating a company and building
it may be one of the most
human things that humans do.
Which is one of many reasons
I find Adam Lashinsky’s feature
story on Jack Ma and Huateng
“Pony” Ma—the mightily ambitious
founders, respectively,
of Alibaba and Tencent—such
an intoxicating read. In “Ma
vs. Ma” (see page 72), Adam
expertly depicts what may be the
biggest corporate battle in China
while taking us inside an Internet
ecosystem that may spread
rapidly around the world.
For a grand tour of intelligence,
both artificial and real,
read on. And please, as always,
let us know what you think.
CLIFTON LEAF
Editor-in-Chief,Fortune
@CliftonLeaf
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FORTUNE.COM // JULY.1.18