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COMPUTERS AS HUMANS
The
next wave of computing will be about applications that are as intuitive and
capable as human beings. And IBM and a few startups are providing the
platform for this explosive opportunity
In a few months from now, or at least sometime next
year, a few IBM partners will release a series of software products that
will be unlike anything people have encountered so far. Instead of doing a
task for you through a software program, these products will prepare you
instead to do that task yourself. You could ask the computer about your
health, a home purchase, or a travel plan. You don’t need visits from sales
executives for product briefings. The computer will gently guide you to
make the right choice at the right time.
Currently, these products are being built around Watson, the famous IBM
computer that won the Jeopardy championship. To be precise, it is built
around a more compact and powerful machine than the Jeopardy champion. IBM
threw open this machine to programmers on the cloud three weeks ago to
build products and services in a few industries to begin with. IBM has
applications from 200 potential partners, including a few from India,
focused initially on healthcare, financial services and travel. “We are
trying to build an ecosystem of partners around Watson,” says Jay
Subrahmonia, vice-president of development and delivery, Watson Solutions
at IBM.
IBM calls it cognitive computing, to distinguish it from the more common
term, artificial intelligence. It is purportedly the next wave of
computing, infinitely more powerful and long-lasting than any other
computing wave we have seen. It changes the way we interact with computers,
the reason we use computers, and also the way we program computers. It is a
big business opportunity as well. Just the global healthcare market for
such systems is projected to increase from $201 million now to $239 billion
by 2019, according to the market research firm WinterGreen Research.
Next Computing Wave
Computers that we use now are fast but dumb. Those in the cognitive
computing era will understand the context in which they function. They will
also learn constantly and improve their capabilities. Yet, they won’t be
based on any single technology, as the current examples show. The core of
Watson is based on natural language processing (NLP), or the ability to
understand human languages. But it combines this ability with massive
computing resources and a host of other computing technologies like machine
learning, information retrieval and automated reasoning. Other
companies—many based in the Silicon Valley—are building different solutions
based on different technologies. Grok, a recent startup from Palm Pilot
founder Jeff Hawkins, mimics the human brain to predict anomalies in IT
systems. Palantir Technologies, based in Palo Alto, uses cognitive
analytics to predict suspicious or terrorist activities. ColdLight, based
in a small town in Pennsylvania, uses machine learning to examine thousands
of factors simultaneously, usually to identify fraud. All of them, and
hundreds of similar companies, are part of the coming cognitive computing
wave. The common theme: understanding data. In some ways, they are an
extension of the current wave of analytics and big data companies, but
there are some differences. Traditional analytics requires a human being to
ask a question. In cognitive computing, we get the answers without knowing
what to ask. Deloitte estimates the US cognitive computing market will
expand in five years from the current $1 billion to $50 billion. “Growth
usually takes place through labour and capital,” says Rajeev Ronanki, lead
for Deloitte’s cognitive computing practice. “Here it is related to machine
learning algorithms.” This difference can make the cognitive computing
market grow rapidly. “Traditional approaches are like giving the computing
system a fish,” says Ronanki, “whereas cognitive systems are akin to
teaching a computer how to fish.” This can cause a fundamental shift with
how markets grow. For IBM, taking Watson to the cloud was a nobrainer. Its
current price is not known, but it is considered too expensive as a
standalone system. The hardware cost alone of the machine that won Jeopardy
was $3 million, but Watson contains plenty of algorithms and data as well.
Putting it on the cloud would enable companies to pay as they use it.
Watson is also complex for even the brighte s t o f p ro g r a m m e r s. A
n Ap p l i c at i o n Programming Interface (API) on the cloud would
substantially simplify the task of programming, as the programmer would not
need to understand what is inside the box.
When Computers Talk, See...
So far, Watson has been used mainly to solve problems in healthcare. At
the Memorial Sloan-Kettering Cancer Center in New York, Watson goes through
millions of pages of cancer data and recommends the best treatment options
for patients. The sports goods company North Face uses Watson to provide
customers recommendations for the ideal gear for a trip. Over the next
year, Watson will seep into more industries as developers make applications
on the cloud.
IBM says that Watson will be among its fastestgrowing business ever. In the
near future, analysts expect Watson to drive the cognitive computing
industry as well. Yet the arena is busy with startups, some of whom claim
to have developed breakthrough technologies. Take Grok. It came out of a
project called Numenta started by Jeff Hawkins. Numenta is supposed to have
cracked how the b r a i n wo rk s ; i t s Cortical Lear ning Algorithm is
modelled on the neocortex, the part of the brain involved in higher
functions like reasoning, thought and language. Grok was spun off this year
from Numenta, which is now an open source project to advance the
technology.
Grok’s first product—in Beta stage—is to detect anomalies in IT systems and
prevent problems before they become manifest. Current products to detect
anomalies look for patterns exceeding a threshold. It is hard, if not
impossible, to detect anomalies below this threshold, which is usually the
case at night, when few people use the systems. Grok tries to solve this
problem combining three methods: learning online by itself, creating models
automatically, and recognising patterns. “Our models learn continuously,”
says Craig Vaughn, vicepresident of marketing and products at Grok. “They
keep changing as IT policies change.”
This ability to learn is at the heart of any cognitive computing system,
and distinguishes it from traditional analytics. It is relevant wherever
there is a fast data stream: retail, healthcare, travel, telecom. But not
if the data stream consists of images—computers cannot understand images.
This is why robots are so unreliable. “If robots could understand the
world,” says Dileep George, founder of Vicarious, “the benefits could be
enormous.” For example, we could have sent them inside the Fukushima
reactor to fix it.
Vicarious, near San Francisco, is among the many companies trying to make
computers see. George, an IIT graduate, has funding from top VCs like Peter
Thiel. It had a breakthrough recently; it cracked the Captcha, a set of
overlapping and contorted letters that humans can recognise easily but
computers find impossible to read. Vicarious created an algorithm that can
separate the overlapping letters with a high degree of accuracy. It is a
good step towards making computers understand the environment around them.
But there is a long way to go.
How is it Being Applied
Cancer treatment
Memorial Sloan-Kettering Cancer Center in New York is training IBM’s
Watson to absorb millions of pages of cancer-research results, and use it
to recommend ways of treatment
Online shopping
Fluid, an online shopping technology firm, is using Watson to enhance
customer shopping experience, by holding a conversation with them and making
recommendations
Hari Pulakkat ET131210
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