Machines of Loving Grace (16 page)

McCarthy wanted to avoid the term “cybernetics” because he thought of Norbert Wiener, who had coined the term, as something of a bombastic bore and he chose to avoid arguing with him.
He also wanted to avoid the term “automata” because it seemed remote from the subject of intelligence.
There was still another dimension inherent in the choice of the term “artificial intelligence.”
Many years later in a book review taking issue with the academic concept known as the “social construction of technology,” McCarthy took pains to distance artificial intelligence from its human-centered roots.
It wasn’t about human behavior, he insisted.
¹⁴

The Dartmouth conference proposal, he would recall years later, had made no reference to the study of human behavior, “because [he] didn’t consider it relevant.”
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Artificial intelligence, he argued, was not considered human behavior except as a possible hint about performing humanlike tasks.
The only Dartmouth participants who focused on the study of human behavior were Allen Newell and Herbert Simon, the Carnegie Institute researchers who had already won acclaim for ingeniously bridging the social and cognitive sciences.
Years later the approach propounded by the original Dartmouth conference members would become identified with the acronym GOFAI, or “Good Old-Fashioned Artificial Intelligence,” an original
approach centered on achieving human-level intelligence through logic and the branch of problem-solving rules called heuristics.

IBM, by the 1950s already the world’s largest computer maker, had initially been involved in the planning for the summer conference.
Both McCarthy and Minsky had spent the summer of 1955 in the IBM laboratory that had developed the IBM 701, a vacuum tube mainframe computer of which only nineteen were made.
In the wake of the conference, several IBM researchers did important early work on artificial intelligence research, but in 1959 the computer maker pulled the plug on its AI work.
There is evidence that the giant computer maker was fearful that its machines would be linked to technologies that destroyed jobs.
¹⁶
At the time the company chief executive Thomas J.
Watson Jr.
was involved in national policy discussions over the role and consequences of computers in automation and did not want his company to be associated with the wholesale destruction of jobs.
McCarthy would later call the act “a fit of stupidity” and a “coup.”
¹⁷

D
uring those early years McCarthy and Minsky remained largely inseparable—Minsky’s future wife even brought McCarthy along when she took Minsky home to introduce him to see her parents—even though their ideas about how to pursue AI increasingly diverged.
Minsky’s graduate studies had been on the creation of neural nets.
As his work progressed, Minsky would increasingly place the roots of intelligence in human experience.
In contrast, McCarthy looked throughout his career for formal mathematical-logical ways to model the human mind.

Yet despite their initial difficulties, early on, the field remained remarkably collegial and in the hands of researchers with privileged access to the jealously guarded room-sized computers of the era.
As McCarthy recalls it, the MIT Artificial
Intelligence Laboratory came into being in 1958 after both he and Minsky had joined the university faculty.
One day McCarthy met Minsky in a hallway and said to him, “I think we should have an AI project.”
Minsky responded that he thought that was a good idea.
Just then the two men saw Jerome Wiesner, then head of the Research Laboratory on Electronics, walking toward them.

McCarthy piped up, “Marvin and I want to have an AI project.”

“What do you want?”
Wiesner responded.

Thinking quickly on his feet, McCarthy said, “We’d like a room, a secretary, a keypunch, and two programmers.”

To which Wiesner replied, “And how about six graduate students?”

Their timing would prove to be perfect.
MIT had just received a large government grant “to be excellent,” but no one really knew what “excellent” meant.
The grant supported six mathematics graduate students at the time, but Wiesner had no idea what they would do.
So for Wiesner, McCarthy and Minsky were a serendipitous solution.
¹⁸

The funding grant came through in the spring of 1958, immediately in the wake of the Soviet Sputnik satellite.
U.S.
federal research dollars were just starting to flow in large amounts to universities.
It was widely believed that the generous support of science would pay off for the U.S.
military, and that year President Eisenhower formed the Advanced Research Projects Agency, ARPA, to guard against future technological surprises.

The fortuitous encounter by the three men had an almost unfathomable impact on the world.
A number of the “six graduate students” were connected with the MIT Model Railway Club, an unorthodox group of future engineers drawn to computing as if by a magnet.
Their club ethos would lead directly to what became the “hacker culture,” which held as its most prized value the free sharing of information.
¹⁹
McCarthy would
help spread the hacker ethic when he left MIT in 1962 and set up a rival laboratory at Stanford University.
Ultimately the original hacker culture would also foment social movements such as free/open-source software, Creative Commons, and Network Neutrality movements.
While still at MIT, McCarthy, in his quest for a more efficient way to conduct artificial intelligence research, had invented computer time-sharing, as well as the Lisp programming language.
He had an early notion that his AI, when it was perfected, would be interactive and logical to design on a computing system shared by multiple users, rather than requiring users to sign up to use the computer one at a time.

When MIT decided to do a survey on the wisdom of building a time-sharing system instead of immediately building what McCarthy had proposed, he decided to head west.
Asking university faculty and staff what they thought of computer time-sharing would be like surveying ditchdiggers about the value of a steam shovel, he would later grouse.
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He was thoroughly converted to the West Coast counterculture.
Although he had long since left the Communist Party, he was still on the Left and would soon be attracted to the anti-establishment community around Stanford University.
He took to wearing a headband to pair with his long hair and became an active participant in the Free University that sprang up on the Midpeninsula around Stanford.
Only when Russia crushed the Czech uprising in 1968 did he experience his final disillusionment with socialism.
Not long afterward, while arguing over the wisdom of nonviolence during a Free U meeting, one of the radicals threatened to kill McCarthy, and he consequently ricocheted permanently to the Right.
Not long afterward he registered as a Republican.

At the same time his career blossomed.
Being a Stanford professor was a hunting license for funding and on his way to Stanford he turned to his friend J.
C.
R.
Licklider, a former MIT psychologist, who headed ARPA’s Information Processing
Techniques Office beginning in 1962.
Licklider had collaborated with McCarthy on an early paper on time-sharing and he funded an ambitious time-sharing program at MIT after McCarthy moved to Stanford.
McCarthy would later say that he never would have left if he had known that Licklider would be pushing time-sharing ideas so heavily.

On the West Coast, McCarthy found few bureaucratic barriers and quickly built an artificial intelligence lab at Stanford to rival the one at MIT.
He was able to secure a computer from Digital Equipment Corporation and found space in the hills behind campus in the D.C.
Power Laboratory, in a building and on land donated to Stanford by GTE after the telco canceled a plan for a research lab on the West Coast.

The Stanford Artificial Intelligence Laboratory quickly became a California haven for the same hacker sensibility that had spawned at MIT.
Smart young computer hackers like Steve “Slug” Russell and Whitfield Diffie followed McCarthy west, and during the next decade and a half a startling array of hardware engineers and software designers would flow through the laboratory, which maintained its countercultural vibe even as McCarthy became politically more conservative.
Both Steve Jobs and Steve Wozniak would hold on to sentimental memories of their visits as teenagers to the Stanford laboratory in the hills.
SAIL would become a prism through which a stunning group of young technologists as well as full-blown industries would emerge.

Early work in machine vision and robotics began at SAIL, and the laboratory was indisputably the birthplace of speech recognition.
McCarthy gave Raj Reddy his thesis topic on speech understanding, and Reddy went on to become the seminal researcher in the field.
Mobile robots, paralleling Shakey at Stanford Research Institute, would be pursued at SAIL by researchers like Hans Moravec and later Rodney Brooks, both of whom became pioneering robotics researchers at Carnegie Mellon and MIT, respectively.

It proved to be the first golden era of AI, with research on natural language understanding, computer music, expert systems, and video games like Spacewar.
Kenneth Colby, a psychiatrist, even worked on a refined version of Eliza, the online conversation system originally developed by Joseph Weizenbaum at MIT.
Colby’s simulated person was known as “Parry,” with an obliquely bent paranoid personality.
Reddy, who had previous computing experience using an early IBM mainframe called the 650, remembered that the company had charged $1,000 an hour for access to the machine.
Now he found he “owned” a computer that was a hundred times faster for half of each day—from eight o’clock in the evening until eight the next morning.
“I thought I had died and gone to heaven,” he said.
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McCarthy’s laboratory spawned an array of subfields, and one of the most powerful early on was known as knowledge engineering, pioneered by computer scientist Ed Feigenbaum.
Begun in 1965, his first project, Dendral, was a highly influential early effort in the area of software expert systems intended to capture and organize human knowledge, and was initially intended to help chemists identify unknown organic molecules.
It was a cooperative project among computer scientists Feigenbaum and Bruce Buchanan and two superstars from other academic fields—Joshua Lederberg, a molecular biologist, and Carl Djerassi, a chemist known for inventing the birth control pill—to automate the problem-solving strategies of an expert human organic chemist.

Buchanan would recall that Lederberg had a NASA contract related to the possibility of life on Mars and that mass spectrometry would be an essential tool in looking for such life: “That was, in fact, the whole Dendral project laid out with a very specific application, namely, to go to Mars, scoop up samples, look for evidence of organic compounds,”
²²
recalled Buchanan.
Indeed, the Dendral project began in 1965 in the wake of a bitter debate within NASA over what the role of humans would be in the moon mission.
Whether to keep
a human in the control loop was sharply debated inside the agency at the dawn of spaceflight, and is again today, decades later, concerning a manned mission to Mars.

The original AI optimism that blossomed at SAIL would hold sway throughout the sixties.
It is now lost in history, but Moravec, who as a graduate student lived in SAIL’s attic, recalled years later that when McCarthy first set out the original proposal he told ARPA that it would be possible to build “a fully intelligent machine” in the space of a decade.
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From the distance of more than a half century, it seems both quixotic and endearingly naive, but from his initial curiosity in the late 1940s, before there were computers, McCarthy had defined the goal of creating machines that matched human capabilities.

Indeed, during the first decade of the field, AI optimism was immense, as was obvious from the 1956 Dartmouth workshop:

Not long afterward Minsky would echo McCarthy’s optimism, turning a lone graduate student loose on the problem of machine vision, figuring that it was a suitable problem to be solved as a summer project.
²⁵
“Our ultimate objective is to make programs that learn from their experience as effectively as humans do,” McCarthy wrote.
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