BOSTON, March 26, 2020 /PRNewswire/ -- Innovation
leaders are seeking ways to use artificial intelligence (AI)
effectively to extract value and leverage data for maximum impact.
Lux considers natural language processing (NLP) and topic modeling
the AI tools of choice. These tools have the potential to
accelerate the front end of innovation across many industries, but
remain underutilized. According to Lux Research's new whitepaper,
"Improving the Front End of Innovation with Artificial Intelligence
and Machine Learning," NLP can improve processes including
technology landscaping, competitive analysis, and weak signal
NLP enables rapid analysis of huge volumes of text, which is
where most of the data driving innovation lives.
"When utilized effectively, machine learning can quickly mine
data to produce actionable insights, significantly decreasing the
time it takes for a comprehensive analysis to be performed. An
analysis that would have previously taken weeks can now be reduced
to days," said Kevin See, Ph.D., VP
of Digital Products for Lux Research.
The speed conferred through NLP is enabled by the
comprehensiveness of topic modeling, which extracts important
concepts from text while eliminating the human assumption and bias
associated with it. "Previously, an investigation was hindered by
either the limited knowledge or bias of the primary investigator,
both of which are mitigated when using machine learning. A
beneficial technology or idea is less likely to be missed due to an
error in human judgement," explained See.
There are many relevant applications that use machine learning
to leverage speed and comprehensiveness in innovation. Landscaping
is used to build a taxonomy that defines the trends for key areas
of innovation under a specific topic. Concept similarity can take
one piece of content and find other relevant articles, patents, or
news to accelerate the innovation process. Topic modeling can also
be used for competitive portfolio analysis when applied to a
corporation instead of a technology, or for weak signal detection
when applied to large data sets like news or Twitter.
When defining a successful AI and machine learning strategy,
there are a few key points to consider, including whether you'll
buy or build your technology, what data sources you'll use, and how
you'll leverage experts to define and interpret the data. It's also
important to adapt a culture of acceptance of these tools so that
valued human resources see them as an asset to their skills rather
than competition. "The confidence and speed AI and machine learning
bring to the decision-making process is enabling innovation to
happen at a more rapid pace than ever before, but don't think this
means humans are no longer needed," said See. People are still
necessary to define the starting points of an analysis, label
topics, and extract insights from the data collected. "It's clear
that a collaboration between humans and machines can generate
better results, benefiting all involved," See continued.
For more information, download a copy of Lux Research's
About Lux Research
Lux Research is a leading provider of tech-enabled research and
advisory services, helping clients drive growth through technology
innovation. A pioneer in the research industry, Lux uniquely
combines technical expertise and business insights with a
proprietary intelligence platform, using advanced analytics and
data science to surface true leading indicators. With quality data
derived from primary research, fact-based analysis, and opinions
that challenge traditional thinking, Lux empowers clients to make
more informed decisions today to ensure future success.
For more information, visit www.luxresearchinc.com, read our
blog, connect on LinkedIn, or follow @LuxResearch.
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SOURCE Lux Research