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KM leveraging AI: Here we come

1 July 2021

Dr. Moria Levy

Hi all. Today I am excited. In our AI KM course, we reached today the set of sessions dealing with the one million dollar question: How can KM leverage AI projects? First, let me be clear; I am not speaking only about AI text-based projects; I refer to ALL AI projects.


That was the dream from almost day one. Two years ago at KMGN, we decided that we must bring new added value to KM and through KM to organizations. So we set a task force including Art, Molly, Balaji, Eric, Krishnhan, Vincent, Hary, Randhir, and myself.  One of the main ways we understood, leading us to leverage the KM Value proposition, will be through linking new technologies to KM. And AI was tagged definitely as the central new technology we should look into. However, it didn't take too long before we learned that AI is missing so many capabilities and expertise that KM cannot solve yet can offer a great deal to improve.


These led to the idea to produce this course. Note: don't worry- it won't end here- we also set plans for an additional class on KM's new methodologies next year.


Where can KMrs' improve AI projects? For example, in the first steps of knowledge engineering, along the way with content management, helping the recurring task of analyzing unsuccessful behavior of the machine through systematic lessons learned sessions; and much more.


Today, June 29, we spoke about content services. Dr. Annie Green led us through some critical issues. We KMrs' have knowledge, experience, and expertise as to required content services. Classification is one of the core elements of every machine learning-based project- whether supervised or non-supervised. There is no big-data project that can be handled without content services. Practically, we help in:


  1. Ensuring that all relevant content service aspects are covered in all stages of capturing/ingesting, managing, processing, storing, preserving, and delivering.

  2. Connecting and aligning the existing organization's KM methodologies and technologies set for content services to serve the AI projects.

  3. Validating that new software tools do support the required data sources.

  4. Addressing data cleansing known issues

and in every other content services aspect. We are doing it already over twenty years.


Will it be easy? Probably not. Big data and AI staff are probably not aware of KMr's and their potential added value. Some believe that they must mess by themselves with the data as it is a significant part of any project of the kind.


So, KMrs'- Start learning the AI terms for content services.


And big data staff- Please dare and enable us to join in. We will not replace you; instead, we offer to bring in our experience and expertise, and at the end of the day, we will probably smile together. Cheers!😊😊


 This post was initially published in LinkedIn


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