Artificial Intelligence Makes Strides, but Has a Long Way to Go
December 04 2016 - 1:36PM
Dow Jones News
By Christopher Mims
Artificial intelligence is having a moment.
Startups that claim to be using AI are attracting record levels
of investment. Big tech companies are going all-in, draining
universities of entire departments. Nearly 140 AI companies have
been acquired since 2011, including 40 this year alone.
AI is showing up in our everyday lives, as voice-recognition
technology in our devices and image recognition in our Facebook and
Google accounts.
Now, Google parent Alphabet Inc., Amazon.com Inc. and Microsoft
Corp. are making some of their smarts available to other
businesses, on a for-hire basis. Want to make your app or gadget
respond to voice commands, and answer in its own "voice?" These
services can do that. Need to transcribe those conversations so
they can be analyzed? This new breed of services can do this and
many other things, from face recognition to identifying
objectionable content in images.
But wringing measurable utility from these new AI toys can be
hard. "Everyone wants to think the AI spring is going to blossom
into the AI summer, but I think it's 10 years away," says Angela
Bassa, head of the data-science team at
energy-intelligence-software company EnerNOC Inc.
Before switching to her new role, Ms. Bassa led a team at
EnerNOC that used AI techniques such as machine learning and deep
learning, which feed massive amounts of data into computer programs
to "train" them. But the company found that customers were more
interested in analytics than in the incremental value that
sophisticated AI-powered algorithms could provide.
AI, says Ms. Bassa, requires three things that most companies
don't have in sufficient quantities. The first is enough data.
Companies like Facebook, Amazon, Alphabet, General Electric Co. and
others are harvesting enormous amounts of data, but they are
exceptions.
The second is problems where making a small difference can
justify the expense of creating an AI system. If AI can improve the
fraud-detection system at a credit-card company by 1%, that could
be worth tens of millions of dollars. For a midsize manufacturer
that makes many different products, however, a 1% improvement in
productivity of a particular line might not justify the cost of
hiring a half-dozen highly paid engineers.
That leads to the third scarcity: People to build systems. The
war for AI talent is driving up the cost. "There are maybe 5,000
people in the world who can put together one of these
machine-learning systems in a way that saves money, even if only
incrementally," says Ms. Bassa.
This doesn't mean that AI is useless to businesses. But it does
suggest that AI is being oversold. Creating systems that can be
used for a variety of problems, and not just the narrow
applications to which AI has been put so far, could take decades.
Systems have to be built and trained. Like educating a child, this
takes time.
Most of what's available now are "pre-trained" systems, built by
companies like Microsoft, Amazon and Google, and reflecting the
data those companies have. Those companies have billions of images,
so they offer commercial image-recognition services for others.
Amazon, having compiled a vast trove of spoken language from its
Alexa personal assistant, offers services to process spoken
language -- and generate replies -- for others.
Some startups are beginning to offer broader AI systems that
require neither a machine-learning expert nor a pre-trained system
constructed by the likes of Google. Israel's n-Join sells
manufacturers a small box that collects data from machines on an
assembly line, and then uses machine learning to spot aberrations
that could presage a breakdown.
The key to n-Join's utility, says Guy Tsur, a senior
technologist at Strauss Group Ltd., one of Israel's largest
manufacturers of dairy products and an early n-Join customer, is
that it doesn't have to know the type of assembly line it's
attached to, or what the sensors feeding it data are measuring.
It's simply looking for correlations that indicate the
manufacturing process is operating differently than usual. It then
alerts its human supervisors, who can use their own experience and
judgment to diagnose a specific problem.
One thing an astute reader has noted by now is that none of
these triumphs and shortcomings of AI resemble the sci-fi visions
of machines taking over the world. Reflecting on my own brief
experience as an invertebrate neuroscientist, I'd say that today's
AI is at the jellyfish stage in the evolution of biological
intelligence. Real brains -- and genuine intelligence -- are so far
in the future as to be beyond any reasonable horizon of
prediction.
Or, as chief scientist and AI guru Andrew Ng of Chinese search
giant Baidu Inc. once put it, worrying about takeover by some kind
of intelligent, autonomous, evil AI is about as rational as
worrying about overpopulation on Mars.
Write to Christopher Mims at christopher.mims@wsj.com
(END) Dow Jones Newswires
December 04, 2016 13:21 ET (18:21 GMT)
Copyright (c) 2016 Dow Jones & Company, Inc.
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