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— From the August 2015 issue

*The Transhuman Condition*

By John Markoff
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*By John Markoff, from* Machines of Loving Grace, *out this month from Ecco
Books. Markoff has been a technology and business reporter for the* New
York Times *since 1988.*

B

ill Duvall grew up on the peninsula south of San Francisco. The son of a
physicist who was involved in classified research at Stanford Research
Institute (SRI), a military-oriented think tank, Duvall attended UC
Berkeley in the mid-1960s; he took all the university’s
computer-programming courses and dropped out after two years. When he
joined the think tank where his father worked, a few miles from the
Stanford campus, he was assigned to the team of artificial-intelligence
researchers who were building Shakey.

Although *Life* magazine would later dub Shakey the first “electronic
person,” it was basically a six-foot stack of gear, sensors, and motorized
wheels that was tethered — and later wirelessly connected — to a nearby
mainframe. Shakey wasn’t the world’s first mobile robot, but it was the
first that was intended to be truly autonomous. It was designed to reason
about the world around it, to plan its own actions, and to perform tasks.
It could find and push objects and move in a planned way in its highly
structured world.

At both SRI and the nearby Stanford Artificial Intelligence Laboratory
(SAIL), which was founded by John McCarthy in 1962, a tightly knit group of
researchers was attempting to build machines that mimicked human
capabilities. To this group, Shakey was a striking portent of the future;
they believed that the scientific breakthrough that would enable machines
to act like humans was coming in just a few short years. Indeed, among the
small community of AI researchers who were working on both coasts during
the mid-Sixties, there was virtually boundless optimism.

But the reality disappointed Duvall. Shakey lived in a large open room with
linoleum floors and a couple of racks of electronics. Box-like objects were
scattered around for the robot to “play” with. Shakey’s sensors would
capture its environment and then it would “think” — standing motionless for
minutes on end — before moving. Even in its closed and controlled world,
the robot frequently broke down or drained its batteries after just minutes
of operation.

Down the hall from the Shakey laboratory, another research group, led by
computer scientist Doug Engelbart, was building a computer to run a program
called NLS — the oN-Line System. Most people who know of Engelbart today
know him as the inventor of the mouse. But the mouse, to Engelbart, was
simply a gadget to improve our ability to interact with computers. His more
encompassing idea was to use computer technologies to make it possible for
small groups of scientists, engineers, and educators to “bootstrap” their
projects by employing an array of ever more powerful software tools to
organize their activities and create a “collective I.Q.” that outstripped
the capabilities of any single individual. During World War II, Engelbart
had stumbled across an article by Vannevar Bush that proposed a
microfilm-based information-retrieval system called Memex to manage all of
the world’s knowledge. He realized that such a system could be assembled
with computers.

The cultural gulf between McCarthy’s artificial intelligence and
Engelbart’s contrarian NLS was already apparent to those on either side.
When Engelbart visited MIT to demonstrate his project, prominent AI
researcher Marvin Minsky complained that he was wasting research dollars on
a glorified word processor. But the idea captivated Bill Duvall. Before
long he switched his allegiance and moved down the hall to work in
Engelbart’s lab.

Late on the evening of October 29, 1969, Duvall connected the NLS system in
Menlo Park, via a data line leased from the phone company, to a computer
controlled by another young hacker in Los Angeles. It was the first time
that two computers connected over the network that would become the
Internet. Duvall’s leap from the Shakey laboratory to Engelbart’s NLS made
him one of the earliest people to stand on both sides of a line that even
today distinguishes two rival engineering communities. One of these
communities has relentlessly pursued the automation of the human
experience — artificial intelligence. The other, human-computer
interaction — what Engelbart called intelligence augmentation — has
concerned itself with “man-machine symbiosis.” What separates AI and IA is
partly their technical approaches, but the distinction also implies
differing ethical stances toward the relationship of man to machine.

D

uring the 1970s and 1980s the field of artificial intelligence drew a
generation of brilliant engineers, but it often disappointed them in much
the way that it had disappointed Duvall. Like him, many of these engineers
turned to the contrasting ideal of intelligence augmentation. But today, AI
is beginning to meet some of the promises made for it by SAIL and SRI
researchers half a century ago, and artificial intelligence is poised to
have an impact on society that may be greater than the effect of personal
computing and the Internet.

Although their project has now largely been forgotten, the designers of
Shakey pioneered computing technologies that are now used by more than a
billion people. The mapping software in our cars and our smartphones is
based on techniques the team first developed. Their A* algorithm is the
best-known way to find the shortest path between two locations. Toward the
end of the Shakey project, speech control was added as a research task;
Apple’s Siri, whose name is a nod to SRI, is a distant descendent of the
machine that began life as a stack of rolling sensors and actuators.

While Engelbart’s original research led directly to the PC and the
Internet, McCarthy’s lab did not provide a single dramatic breakthrough.
Rather, the falling costs of sensors, computer processing, and information
storage, along with the gradual shift away from symbolic logic and toward
more pragmatic statistical and machine-learning algorithms, have made it
possible for engineers and programmers to create computerized systems that
see, speak, listen, and move around in the world.

As a result, AI has been transformed from an academic curiosity into a
force that is altering countless aspects of the modern world. This has
created an increasingly clear choice for designers — a choice that has
become philosophical and ethical, rather than simply technical: will we
design humans into or out of the systems that transport us, that grow our
food, manufacture our goods, and provide our entertainment?

A

s computing and robotics systems have grown from laboratory curiosities
into the fabric that weaves together modern life, the AI and IA communities
have continued to speak past each other. The field of human-computer
interface has largely operated within the philosophical framework
originally set down by Engelbart — that computers should be used to assist
humans. In contrast, the artificial-intelligence community has for the most
part remained unconcerned with preserving a role for individual humans in
the systems it creates.

Terry Winograd was one of the first to see the two extremes clearly and to
consider their consequences. As a graduate student at MIT in the 1960s,
Winograd studied human language in order to build a software robot that was
capable of interacting with humans in conversation. During the 1980s, he
was part of a small group of AI researchers who engaged in seminars at
Berkeley with the philosophers Hubert Dreyfus and John Searle. The
philosophers persuaded Winograd that there were real limits to the
capabilities of intelligent machines. In part because of his changing
views, he left the field of artificial intelligence.

A decade later, as the faculty adviser for Google cofounder Larry Page,
Winograd counseled the young graduate student to focus on Web search rather
than more far-fetched technologies. Page’s original PageRank algorithm, the
heart of Google’s search engine, can perhaps be seen as the most powerful
example of human augmentation in history. The algorithm systematically
collected human decisions about the value of information and pooled those
decisions to prioritize search results. Although some criticized the
process for siphoning intellectual labor from vast numbers of unwitting
humans, the algorithm established an unstated social contract: Google mined
the wealth of human knowledge and returned it in searchable form to
society, while reserving for itself the right to monetize the results.

Since it established its search box as the world’s most powerful
information monopoly, Google has yo-yoed between IA and AI applications and
services. The ill-fated Google Glass was intended as a
“reality-augmentation system,” while the company’s driverless-car project
represents a pure AI — replacing human agency and intelligence with a
machine. Recently, Google has undertaken what it loosely identifies as
“brain” projects, which suggests a new wave of AI.

In 2012, Google researchers presented a paper on a machine-vision system.
After training itself on 10 million digital images taken from YouTube
videos, the system dramatically outperformed previous efforts at an
automated-vision network, roughly doubling their accuracy in recognizing
objects from a list of 20,000 distinct items. Among other things, the
system taught itself to recognize cats — perhaps not surprising, given the
overabundance of cat videos on YouTube — with a mechanism that the
scientists described as a cybernetic cousin to what takes place in the
brain’s visual cortex. The experiment was made possible by Google’s immense
computing resources, which allowed researchers to turn loose a cluster of
16,000 processors on the problem — though that number still, of course,
represented a tiny fraction of the billions of neurons in a human brain, a
huge portion of which are devoted to vision.

S

peculation about whether Google is on the trail of a genuine artificial
brain has become increasingly rampant. There is certainly no question that
a growing group of Silicon Valley engineers and scientists believe
themselves to be closing in on “strong” AI — the creation of a self-aware
machine with human or greater intelligence.

Whether or not this goal is ever achieved, it is becoming increasingly
possible — and “rational” — to design humans out of systems for both
performance and cost reasons. In manufacturing, where robots can directly
replace human labor, the impact of artificial intelligence will be easily
visible. In other cases the direct effects will be more difficult to
discern. Winston Churchill said, “We shape our buildings, and afterwards
our buildings shape us.” Today our computational systems have become
immense edifices that define the way we interact with our society.

In Silicon Valley it is fashionable to celebrate this development, a trend
that is most clearly visible in organizations like the Singularity
Institute and in books like Kevin Kelly’s *What Technology Wants* (2010).
In an earlier book, *Out of Control* (1994), Kelly came down firmly on the
side of the machines:

The problem with our robots today is that we don’t respect them. They are
stuck in factories without windows, doing jobs that humans don’t want to
do. We take machines as slaves, but they are not that. That’s what Marvin
Minsky, the mathematician who pioneered artificial intelligence, tells
anyone who will listen. Minsky goes all the way as an advocate for
downloading human intelligence into a computer. Doug Engelbart, on the
other hand, is the legendary guy who invented word processing, the mouse,
and hypermedia, and who is an advocate for computers-for-the-people. When
the two gurus met at MIT in the 1950s, they are reputed to have had the
following conversation:

minsky: We’re going to make machines intelligent. We are going to make them
conscious!

engelbart: You’re going to do all that for the machines? What are you going
to do for the people?

This story is usually told by engineers working to make computers more
friendly, more humane, more people centered. But I’m squarely on Minsky’s
side — on the side of the made. People will survive. We’ll train our
machines to serve us. But what are we going to do for the machines?

But to say that people will “survive” understates the possible
consequences: Minsky is said to have responded to a question about the
significance of the arrival of artificial intelligence by saying, “If we’re
lucky, they’ll keep us as pets.”

Until recently, the artificial-intelligence community has largely chosen to
ignore the ethics of systems that they consider merely powerful tools. When
I asked one engineer who is building next-generation robots about the
impact of automation on people, he told me, “You can’t think about that;
you just have to decide that you are going to do the best you can to
improve the world for humanity as a whole.”

AI and machine-learning algorithms have already led to transformative
applications in areas as diverse as science, manufacturing, and
entertainment. Machine vision and pattern recognition have been essential
to improving quality in semiconductor design. Drug-discovery algorithms
have systematized the creation of new pharmaceuticals. The same
breakthroughs have also brought us increased government surveillance and
social-media companies whose business model depends on invading privacy for
profit.

Optimists hope that the potential abuses of our computer systems will be
minimized if the application of artificial intelligence, genetic
engineering, and robotics remains focused on humans rather than algorithms.
But the tech industry has not had a track record that speaks to moral
enlightenment. It would be truly remarkable if a Silicon Valley company
rejected a profitable technology for ethical reasons. Today, decisions
about implementing technology are made largely on the basis of
profitability and efficiency. What is needed is a new moral calculus.

-- 
Cari Machet
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carimachet at gmail.com
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