Companies Are Increasingly Using AI Tools That Deal with Human Language

Dealing with human language automatically – “natural language processing” (NLP, which can interpret text or speech) – is evident in products such as Amazon’s Alexa or Google Assistant. But that’s the tip of the iceberg. NLP can be used by companies to deal with customers or employees in natural language that simplifies interactions, overcoming the limitations of the Graphical User Interface on mobile devices, the the frustration of a long series of menus in customer service, navigating through endless pages on a web site, or dealing with complex and time-consuming enterprise software.

In addition, NLP can make unstructured data in company documents or voice files accessible, providing answers quickly. One major asset is recorded customer service calls, a database where NLP and speech recognition technology can yield key insights into what customers are asking and what frustrates them.

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AVIOS is providing a forum for understanding these opportunities and seeing how companies are using them to advantage, the Conversational Interaction Conference in San Jose, California, March 11-12. “The conference focuses on delivering both Information and Insight,” Scholz emphasized.

While the core NLP and speech recognition technology can be mysterious – machine learning using Deep Neural Networks is a key contributor to advances – tools and services that help companies use the technology have matured; deployment is relatively easy without an R&D or major data collection project and without hiring specialists. “Company executives I’ve spoken with are often surprised by the ease of deploying these advanced technologies and the efficiencies they provide,” said Bill Scholz, President of AVIOS, an industry organization devoted to helping move language technologies into the commercial mainstream.

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Topics covered at the conference include:

Intelligent digital assistants
Tools for building and deploying automated speech-interactive and text-interactive bots
Examples of deployed systems and lessons learned

Customer service automation using AI technology
IVR prompts that simply ask why the customer is calling
Intelligent chatbots on web site or mobile apps
Reaching customers through mobile personal assistants and home speakers

Using AI to make employees more efficient
“Augmented Intelligence”
Digital assistants make use of enterprise software more effective and accurate

New AI channels
Tools to work through major personal assistants
Interactive conversational ads

Understanding unstructured “big data”
Creating applications that interact using human language
Supporting NLP and speech technology

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