Machines of Loving Grace (42 page)

The term “CALO” was inspired by
calonis,
a Latin word meaning “soldier’s low servant,” or clumsy drudge, but the project also had a significant overlap with Engelbart’s original work that was funded by DARPA in the sixties and seventies.
CALO was intended to help an office worker with project management: it would organize workers’ email, calendars, documents, communication, schedules, and task management.
Eventually, there were a number of commercial spin-offs from the CALO project—a smart calendar, a personalized travel guide, and a game development and education company—but they all paled compared to the success of Siri.

L
ong before the Maker Movement—the Silicon Valley subculture extolling an inclusive do-it-yourself approach to technology—gained steam, Gruber’s Siri cofounder Adam Cheyer was drafted into that world by his mother.
As a child in a Boston suburb, he was restricted to just an hour of television each week, which offered him a brief glimpse of technology that whet his appetite for the latest toys.
When he asked his mother to buy toys for him, however, she responded by giving him a stack of cardboard inserts that cleaners used to stiffen shirts.
He resorted to tape, glue, and scissors to re-create the toys that he had seen on television, like robots and Rube Goldberg
contraptions.
It taught Cheyer that with a small dose of imagination, he could make anything he wanted.
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As a child he dreamed of becoming a magician.
He had read books about the great magicians and thought of them as inventors and tinkerers who tricked others by using technology.
Before he was ten he was saving his money to buy books and tricks from the local magic store.
Later, he realized that his interest in artificial intelligence was rooted in his love of magic.
His favorite eighteenth-century magicians and clockmakers led by Jacques de Vaucanson had built early automata: chess-playing and speaking machines and other mechanical humanoid robots that attempted to illuminate the inner workings of what he, like Gruber, would come to see as the most magical device of all—the human brain.
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Although Cheyer knew nothing of Engelbart’s legendary NLS, in 1987 he built his own system called HyperDoc while working as an artificial intelligence researcher with Bull, the aerospace firm, in France.
He integrated a documentation system into the editor the programmers were using to design their expert systems.
That update made it possible to simply click on any function or command to view a related online manual.
Having easy access to the software documentation made it simpler for developers to program the computers and reduce the number of bugs.
At the time, however, he was unfamiliar with the history of Doug Engelbart’s Augmentation Research Center in Menlo Park during the 1960s and 1970s.
He had moved to California to get a master’s degree in computer science, with a plan to move back to France after graduation.
It had been a fun sojourn in California, but the French computer firm would pay for his schooling only if he returned to Europe.

Not long before he was scheduled to return, however, he stumbled across a small blurb advertising a job in an artificial intelligence research laboratory at SRI.
The job sounded intriguing and he decided to apply.
Before flying to the Bay
Area for the interview, he read extensively on the work of all of the researchers in the group.
Between interviews he went into the bathroom to scan his notes in preparation for each appointment.
When he arrived, he knew everything that everyone had worked on, who they worked with, and what their views were on different issues.
His research paid off.
He was hired in the SRI Artificial Intelligence Center.

In the early 1990s, despite the AI Winter, SRI remained a thriving hub for commercial, military, and academic artificial intelligence research, and decades after Shakey, robots were still roaming the halls.
When Cheyer arrived at the laboratory, he received a small research grant from a Korean telecom lab run by the South Korean government.
The project funding was for a pen and voice control system for the office environment.
“Build us one of those,” they instructed him.

He decided to build a system that would make it easy to plug in additional capabilities in the future.
The system was named Open Agent Architecture, or OAA.
It was designed to facilitate what Cheyer thought of as “delegated computing.”
For example, if a computer needed to answer a question like, “What’s Bob’s email address?”
there was a range of ways that it could hunt for the answer.
Cheyer created a language that would make it possible for a virtual software assistant to interpret the task and hunt for the answer efficiently.

In designing his framework, he found that he was at the heart of a debate that was raging between artificial intelligence researchers and the rival human-computer interaction community.
One group believed the user needed to be in complete control of the computer and the other group envisioned software agents that could “live” in computer networks and operate on behalf of human users.
From the beginning Cheyer had a nuanced view of the ideal human-machine relationship.
He thought that humans sometimes like to control systems directly, but often they just want the system to do something on their behalf without bothering them with the details.
To that
end, his language made it possible to separate what the user wanted the system to do or find from how the task would be accomplished.

Within a year of arriving at SRI, Cheyer was focused on the challenge of actually building a working version of the Knowledge Navigator software avatar that John Sculley had extolled in a futuristic video in 1987.
Like Alan Kay, who started out by building “interim” Dynabooks, during the next two decades Cheyer repeatedly developed prototypes, each of which more closely approximated the capabilities of the Knowledge Navigator.
He was building software virtual robots, software assistants that were intended to act as much as partners as slaves.

By the end of 1993 he had designed a tablet PC resembling an iPad.
No one had developed a touch interface yet and so Cheyer had integrated pen input into his tablet, which allowed it to recognize both handwriting and user gestures, like drawing circles around certain objects to select them.
It also had the ability to recognize speech, largely because Cheyer had become adept at the technology equivalent of borrowing a cup of sugar from his neighbors down the hall.
He had persuaded the researchers at SRI’s Speech Technology and Research Laboratory to install a software connector—known as an API—for his tablet.
That allowed him to plug the mainframe-based speech recognition system into his system.
SRI’s speech technology—which was a research activity that had started with Shakey—would be spun out the next year as a separate start-up, Nuance Communications, which initially pioneered voice applications for call centers.
He did the same with SRI handwriting recognition technologies.
He built a demonstration system that used voice and pen input to approximate a software secretary.
It automated calendar tasks and handled email, contact lists, and databases, and he started experimenting with virtual assistance tasks, like using maps to find restaurants and movie theaters.

Cheyer walked the halls and sampled the different projects
at the laboratory, like natural language understanding, speech recognition, cooperating robots, and machine vision.
SRI was his playground and he used it to mash together a remarkably disparate and rich set of computing systems and services—and he did it all before he saw his first Web browser.
The World Wide Web was just beginning to filter out into the world.
When the NCSA Mosaic browser, the first popular browser that brought the Web to the general public, finally did arrive, it felt like déjà vu.

Cheyer wanted to create an assistant that could provide a computer user with the kind of help he or she might expect to get from an attentive secretary.
Although he had started on his own, over the next six years he worked with a small team of programmers and designers and created more than four dozen applications, ranging from intelligent refrigerators that would find recipes and restock themselves to televisions that let you control your home, collaborative robots, and intelligent offices.
Ultimately the team would have a significant impact on mobile computing.
Fifteen years later, two members of his early research group were key technology executives overseeing the design of the Samsung Galaxy smartphone and three had gone on to Apple to deliver Siri.

Cheyer quietly earned a reputation inside SRI as the “next Engelbart.”
Eventually he became so passionate about Engelbart’s ideas that he kept a photo of the legendary computer scientist on his desk to remind him of his principles.
By the end of the 1990s Cheyer was ready for a new challenge.
The dot-com era was in full swing and he decided to commercialize his ideas.
The business-to-business Internet was exploding and everywhere there were services that needed to be interconnected.
His research was a perfect fit for the newly popular idea of loosely coupled control.
In a world of networked computers, software that allowed them to cooperate was just beginning to be designed.
He was following a similar path to Marty Tenenbaum’s, the AI researcher who had created CommerceNet,
the company for which Tom Gruber built ontologies.

One of a small group of Silicon Valley researchers who realized early on that the Internet would become the glue that connected all commerce, Cheyer went to a competitor called VerticalNet, where he created a research lab and was soon made VP of engineering.
Like Gruber, he was caught up in the dot-com maelstrom.
At one point VerticalNet’s market value soared to $12 billion on revenues of a little more than $112 million.
Of course it couldn’t last, and it didn’t.
He stayed for four years and then found his way back to SRI.

DARPA knocked on Cheyer’s door with an offer to head up Tony Tether’s ambitious national CALO effort, which DARPA anticipated would draw on the efforts of AI researchers around the country.
Usually DARPA would simultaneously fund many research labs and not integrate the results.
The new DARPA program, however, called for SRI to marshal all the research into the development of CALO.
Everyone would report to the SRI team and develop a single integrated system.
Cheyer helped write the initial DARPA proposal, and when SRI received the award, he became engineering architect for the project.
CALO was rooted firmly in the traditional world of first-generation symbolic artificial intelligence—planning and reasoning and ontologies—but there was also a new focus on what has been described as “learning in the wild.”

CALO had the trappings of a small Manhattan Project.
Over four hundred people were involved at the peak, and the project would generate more than six hundred research papers.
DARPA spent almost a quarter billion dollars on the effort, making it one of the most expensive artificial intelligence projects in history.
Researchers on the CALO project tried to build a software assistant that would possess humanlike adaptability, learn from the person it worked with, and change its behavior accordingly.

When CALO passed its annual system tests, DARPA was enthusiastic.
Tether awarded the project an excellence prize,
and some of the technology made the transition into navy projects.
But Adam Cheyer, as engineering architect, had experienced more than his share of frustrations.
John McCarthy had famously asserted that building a “thinking machine” would require “1.8 Einsteins and one-tenth the resources of the Manhattan Project.”
To put his estimate in perspective, since the Manhattan Project would cost more than $25 billion in current dollars, McCarthy’s estimate would mean that CALO was funded with less than one-tenth of what would be needed to build a thinking machine.

For Cheyer, however, the principal obstacle in designing CALO was not lack of funding.
Rather it was that DARPA tried to micromanage his progress.
Often unable to pursue its own agenda, the rest of the management team would shunt aside Cheyer’s ideas.
He had a difficult time shepherding the huge number of teams, each of which had its own priorities and received only a small amount of funding from the CALO project.
Cheyer’s entreaties to work together on a common project that integrated a huge swath of ideas into a new “cognitive” architecture largely fell on deaf ears.
The teams listened politely because they were interested in the next round of money, and they would deliver software, but they all wanted to pursue their own projects.
In the end there was no way that a large and bureaucratic program could have a direct impact in the real world.

To cope with his frustrations he laid out a series of side projects to work on in 2007.
They ranged from efforts to commercialize the CALO technology to the creation, with several friends, of an activists’ social network called change.org.
It would be a remarkably productive year for Cheyer.
With a graduate student, Didier Guzzoni, he used CALO technologies to build a new software development system that eventually became the foundation for Siri.
He also put together a small development team that started commercializing various other components of Siri for applications like smartphone calendars
and online news reading.
He also quietly helped to cofound Genetic Finance, a stealth machine-learning company that built a cluster of more than one million computers to solve financial problems such as predicting the stock market.