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Cognitive Computing and AI change the way knowledge management is done

A new era of cognitive computing is unfolding and its impact is already being felt across industries, from preventative maintenance at manufacturing plants to patient diagnoses at hospitals.

It also drives the rise of sophisticated chatbots ready to assist us across the connected world. The goal of cognitive computing is straightforward: to simulate human thought processes in a computerized model.

However, building, refining, and reaping business value from cognitive computing systems and linguistic AI is another story.

KMWorld recently held a roundtable webinar featuring John Chmaj, senior director, product strategy knowledge management, Verint; Jane Hendricks, senior product marketing manager, SDL; and David Seuss, CEO, Northern Light, who discussed how to harness cognitive computing and AI.

Knowledge drives support and service, Chmaj explained. Support and service within KM has a certain flavor. Customers and agents need fast, specific answers to potentially complex questions. Company/product knowledge is curated to drive support answers, interactions, and research, he said. There is a need to balance business outcomes with customer expectations and giving people the best information based on their understanding and entitlement.

Support search requirements include the ability to leverage generalized input to drive to specific answers and link people to answers, not just information. Search needs to match how we think, Chmaj said.

He recommended a cognitive indexing approach. This strategy offers a deep, real-time statistical correlation of the probable relations between terms (docs and queries). It gives companies the ability to layer in domain-specific focusing mechanisms (term relationships in Banking, Insurance, etc.) and add organization-specific terms to round out the context.

Cognitive search offers real-time rich indexing of content sources, enables “zero-click” predictive next-best answers, supports authoring automation—dynamic link generation, and enhances reporting—good search relevance drives better analysis.

“At its core, intelligence can be viewed as a process that converts unstructured information into useful and actionable knowledge. The scientific promise of artificial intelligence (AI)... is that we may be able to synthesize, automate and optimize that process, using technology as a tool to help us acquire rapid new knowledge in fields that would remain intractable for humans unaided,” Hendricks said.

Linguistic AI gives people and machines the ability to process, understand, and generate language better than through human effort alone, she said. Linguistic AI powers solutions for content transformation such as enterprise machine translation.

Northern Light uses Latent Semantic Indexing to model user interests and find recommendations, Seuss said. For each user Northern Light creates such a model of the documents they have downloaded and use it to look for other documents being added to the research collection that contain those topics with similar weightings.

In the actual implementation the model considers not just singular words but groups of words with synonyms and related terms that represent topics.

The whole process of topic creation is unsupervised, with the machine figures things out.

We are approaching the era when users will no longer search for information, they will expect the machine to find what they need on its own and they will expect the machine to summarize for them what they need to know, Seuss said.

Search is evolving to have an in-depth understanding of the searched material and the ways of knowing in the user’s knowledge domain.

An archived on-demand replay of this webinar is available here.

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