2Know Magazine: Sharing KM Knowledge
2Know: Sharing KM Knowledge
October 2020 - Magazine No. 253
October 2020 - Magazine No. 253
Edition:

Most children's schedules can be divided into two categories: school time and play time. Lately, they enjoy too much screen time. Well, what about organizations? Are some seasons more appropriate for gamification stages of learning processes and other organizational processes?

First of all, let us define gamification. Gamification refers to application of activities which incorporate a positive experiential dimension, through gaming components. Many organizations implement this concept by incorporating riddles or competitions as part of a course, a new system implementation, organizational change, as the basis for the development of new knowledge.

Gamification introduces elements of enjoyment, competition, engagement and even humor to knowledge sharing or transferring processes and thus ultimately contributes to the success of said processes.

It may seem that during times of extreme pressure and workload workers would be mentally unavailable for gaming activities. Furthermore, management may view it as a waste of time. However, these times actually call for a lighter and more experiential method for knowledge sharing and transferring.

A gamification break allows workers to escape stressful work and participate in a positive, lighter experience. Group activities enhances relationships among workers and between workers and the organization, themes which are occasionally neglected during times in which each worker is preoccupied with their situation.

 

We therefore recommend organizations to further incorporate gamification during stressful times, as long as limited activity time is maintained, and focused content is transmitted clearly. This will allow organizations to balance the gamification aspects and the business performance aspects.

 

 

 

Written By Yanina Dayan

Have you ever searched for certain information and didn't know where to start?

You assume it will appear in several places, in several platforms and multiple forms. Now, imagine you have a small chip running around, searching for said information in all locations, then presenting you with the most relevant results. Sounds dreamy, right?

Well, dream just a little bit longer- because it's nearly a reality.

 

Cognitive Search is a search engine technology which is AI based, emulating human thinking processes and thus improving search results.

A Cognitive Search differs from previously available search technologies as it incorporates enhanced AI capabilities, mainly Natural Language Processing (NLP) and Deep Machine Learning. These allow the system to optimally detect the highest result possible.

This is because typical search calculation yield many answers. A closed network may contain hundreds, possibly tens of thousands of results. It requires representing and evaluating each of these results. Optimization enables the machine to recognize the calculation with the highest result based on the information it processes, which in turn provides the recommended search result (Wired, 2017).

 

A fine example of a Cognitive Search engine is Azure Cognitive Search operated by Microsoft. It uses a template referred to as a reverse index to enable very fast searches through entire texts. A reverse index is compiled of the unique words which appear in each document, and the list of documents in which each word appears. Azure Cognitive Search's indexing process includes dividing the content of each document into separate words, creating a sorted list of all unique terms, then listing which document contains each word.

 

Google search is another example of a Cognitive Search. The Google search contains a processing feature based not only on local variables but also on users' search patterns. This, in turn, enables more precise search results based on location and previous searches. Put simply, the machine 'learns to know' the users and their search history and subsequently presenting them with the search results most respectively relevant to them.

 

Characteristics

 

  • Produces data of higher value than most complex and diverse databases. Uses all available organizational data, both internal and external, regardless of whether they are structured or not, and provides users with deeper insight so to enable them to make better business decisions. Cognitive Search allows this connection to provide comprehensive insights.
  • Provides relevant, context-dependent information. Finding relevant knowledge throughout all available organizational data requires cognitive systems which utilize NLP and can present unstructured data found in texts (documents, email, social media blogs, engineering reports, market studies, etc.) and rich media content (videos, recordings, etc.). Algorithms allow to perfect insights received from the data. Dictionaries and ontologies assist with the synonyms and links between the terms and concepts.
  • Improves constantly via use and incorporation of algorithms, which provides value added.

 

Applications

 

  • Marketers can use algorithm-based classification to allow prediction of whether customers react to a commercial campaign by analyzing the way they reacted to similar previous campaigns.
  • When we don't necessarily need to run a search query on the entire index, it is better to work with clusters. The idea is to limit the search to specific document groups, represented by 'clusters'. A cluster can also be used for analysis purposes. For example, marketing professionals can use different groups of their potential customer database and use these insights to develop focused marketing campaigns.
  • Regression- algorithms which predict numeral sequential values from data by learning the connections between input and output variables. For example, a financial professional would use regression to predict stock prices based on factors such as financial growth, trends or demographics. Regression can be used for creating apps which predict the traffic flow conditions according to weather.
  • Recommendations- another typical application involves multiple basic algorithms to create a recommendation engine which suggests users content that may interest them. "content-based recommendations" offer users personalized recommendations by matching their interests to the documents properties and descriptions. This is a common internet website feature. a well-known example would be Netflix, which recommends users content, based on an analysis of their previous picks.

 

In conclusion, thanks to technological advancement, Cognitive Searches usher in a new generation of search, enabling organizations to exceed the traditional search box and providing users with instant, relevant data in relevant context.

 

References

 

https://searchenterpriseai.techtarget.com/definition/cognitive-search

https://go.forrester.com/blogs/17-06-12-cognitive_search_is_the_ai_version_of_enterprise_search/

https://www.searchtechnologies.com/blog/why-cognitive-search

https://azure.github.io/LearnAI-KnowledgeMiningBootcamp/labs/lab-02-azure-cognitive-search.html

https://www.bravosquared.com/blogs/what-is-cognitive-search-and-why-is-it-important-for-searching-an-enterprise/

https://he.wikipedia.org/wiki/%D7%A2%D7%99%D7%91%D7%95%D7%93_%D7%A9%D7%A4%D7%94_%D7%98%D7%91%D7%A2%D7%99%D7%AA

 

Written by Rom Knowledgeware
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