Page 22 - INC Magazine-November 2018

the algorithm learns.”
She points to a recent rollout of a
new Hearsay service that provides
automated quick text responses for
advisers and insurance agents to send
their clients. When the eight-year-old
company first introduced the service,
the algorithm came up with a few
eyebrow-raising suggestions. In one
case, it suggested an adviser wish his
client happy birthday. When the
client responded, “Thanks for the
kind thoughts,” the algorithm replied,
“Sounds good to me!” leaving the
client thinking the adviser wasn’t
paying attention or was slightly
unhinged. (Google’s new automated
email reply service has suffered from
even more bizarre response fails in
recent months.) As Hearsay’s human
employees and machine learning
refined the algorithm, they were able
to smooth out the rough edges around
the message prompts and create a set
of more appropriate responses.
The only way to achieve this
type of human-machine symbiosis,
efficient, and cheaper. It also has the replicate for decades to come?” though, is if humans don’t enter this
potential to help us do things that Machine learning can do many new relationship with fear—“the
would have been inconceivable tasks far better than humans, but it worst decision-making sentiment to
before,” says Dave Coplin, author of still takes humans to interpret its have,” observes Kristian Hammond,
The Rise of the Humans and CEO work, and apply the results in ways the co-founder of Narrative Science,
of the Envisioners, a futurist consul- that are strategic, empathetic, and a company that uses A.I. to create
tancy. “But unless humans under- creative. The key, says Shih, is real- natural-language reporting out of raw
stand how to make the best of it, we izing that the machine is just one data and statistics. When interactions
risk belittling the potential it offers.” resource humans can call upon, and are driven by fear, the emphasis shifts
that they, not the machine, have the to the technology, rather than the
skill set that makes the relationship business need for using the technol-
Redefining collaboration
truly useful. “It’s about being ogy. Hammond recommends assem-
Here’s what we do know: The more open-minded and having the ability bling a team comprising both data
robotic minds there are in the work- to delegate the right task to the architects and those in strategic busi-
place, the more companies will want machine,” Shih says. ness roles. “You want A.I. experts
workers who don’t think robotically. The best way to ensure that to be part of a broader initiative that
“We need to make sure that humans approach is to establish what those in speaks to who you want to be as a
develop complementary, not com- the industry call a “humans-in-the- company and how A.I. can shape the
peting, skills with the technology,” loop” relationship. Let the algorithm business,” he says.
says Coplin. “We wouldn’t try to do its thing, with people overseeing
outcalculate Excel, and we don’t try and refining it. “Machine learning is learning to trust the Machine
to remember more facts than Google. hard to get 100 percent right,” says
Instead, we need to consider: What Shih, but with such a process in place, If humans are going to regard
are the fundamentally human skills “you don’t have to be perfect. The machines as partners rather than as
that the computers will be unable to human intervenes in the process and adversaries, they need to have faith