ESTIMATED 2017 REVENUE
EARLY INTEREST IN AI
In 1984, Apple hired legendary
Xerox PARC computer scientist
Alan Kay to research potential
projects in the field.
Tech should serve humanity, not
the other way around.
“Siri isn’t just a voice assistant,”
said Craig Federighi, SVP of software engineering, during Apple’s
June developer event. “With Siri
intelligence, it understands context. It understands your interests. It understands how you use
your device. It understands what
you want next.”
Emotient, a facial-recognition
software startup, for around
$100 million, in 2016
The new chip in the iPhone 8 and
iPhone X, the A11 Bionic, incorporates machine-learning technology that allows developers to build
AI functions into their apps and
have them processed on the device.
WHY IT IS AHEAD
Although often perceived to be
behind, Apple has several hundred
million Siri users. As AI advances,
the company’s vociferous defense
of user privacy only resonates
more with customers.
says director of Sabre Studios Chad Callaghan, “see a future where agents are focused on highly
complex itineraries where you really want that person-to-person interaction, and the bot is
able to support more routine sorts of requests.”
MAKE YOUR BIG DATA MEANINGFUL
Around the turn of the last decade, a bit of technological jargon gained currency: “big data.”
Its buzziness reflected a new understanding that there was value in collecting, organizing,
and analyzing vast amounts of information about every aspect of a business, from manufacturing procedures to customer interactions. Yet it was far easier to hoard big data than to
figure out what to do with it. Many companies “kept collecting data for years and years and
years, and it sat on servers and collected dust,” says Mark Johnson, CEO of geographic AI
startup Descartes Labs. Enter artificial intelligence, which can identify patterns on a scale that
would flummox a mere mortal.
“Data is the food that feeds AI,” says Salesforce’s Casalaina. The more it consumes, the
But once they’d seen 100,000, they’d probably be better at identifying them than humans are.”
Even companies with plenty of data to mine often need to clean up messy databases (
Trulia’s Varma winces as he recalls a company that had stored a default time stamp of 00:00:00 on
Thursday, January 1, 1970, for every record), merge disparate repositories, and generally make
information algorithm-friendly. “The first thing to do is to take the data out of the databases,
make it freely available and accessible,” recommends Jean-François Faudi, senior innovation
manager at Airbus Defence and Space. For Airbus, that involved moving its satellite imagery to
Google Cloud. Now the company can use machine learning to distinguish between snow and
clouds—a feat that, it turns out, computers are more adept at accomplishing than humans.