Natural Language Processing: An Untapped AI Tool for Innovation?
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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 detection.
NLP can enable 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,” See added. 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.