Artificial Intelligence Simplified
ZMEScience

Artificial Intelligence Simplified

Artificial Intelligence is seen in multiple ways. Here is an attempt to define Artificial Intelligence and its applications in layman language. Do share your inputs and comments.

Layman definition of Artificial Intelligence

Artificial Intelligence is something that tries to answer any question.

Broadly there are two types of Questions

  1. Questions for Solving a problem (Ask a pedestrian)
  2. Questions for Predicting the future (Ask a parrot)


Examples that fit Definition

1. Answering Machine

It is the most basic AI system. The problem with this AI system is that its database is limited and has same answer for everyone.

2. Google Search

Comprehends the question. Finds out the answers for almost everything which is available worldwide.

3. Chat Bots

Comprehends the question. Finds out the answers for the related business. Response relevance is higher due to pre-defined context.

4. GPS Navigation Software (Example: Google Maps)

Understands where you want to go. Uses satellite for maps and real time traffic information to show you the fastest route and predicts the time to reach destination.

What determines the Quality of AI?

1. Database Size

Size does matter. Answering Machine has a singular data set, a parrot has a few options. Google has the largest database.

2. Context

After a point, size does not matter. What matters is the ability to understand the context and bring relevant results for the user. Chat Bots are supposed to deliver better results than Google due to clarity of given context.

3. Real time updates

Even context can not sustain after a while. Reality needs real time updates for AI to be useful. Google Maps cannot survive without continuous updates.

Applications in HR

1. Solving the Problem

Chat Bots for Employee and Candidate Queries. These are easy to build. They require a decent database of questions asked by employees and candidates.

2. Predicting the Future

Answering questions like "when will I be promoted?"

Slightly harder to build, as they require career maps and real time updates on objective performance of individuals.

This is the Author's personal point of view and is not influenced by any person or organization. Image Credit to ZMEScience and SB

Supriya Mhatre

Manager Quality at Marsh McLennan

7 年

very well explained.......

Sunil Singh Rana

Polymath adept in Sales & Marketing Sciences | Stock market enthusiast | 80% Homo Economicus | Business Quizzer | @TheRatMark (The Rational Marketer)

7 年

The tricky one with application for HR is the Flight Risk Model developed for Hewlett Packard Enterprise which predict the risk of employee leaving the job :-)

Rajat Dak

Payment Specialist - L2 | Banking Domain | Customer Success

7 年
Prachet Jaisingh

Accomplished leader with 24 years of experience in Customer Relationship Management and Collection across Banking, Telecom and Real Estate sectors, driving strategic initiatives and fostering team excellence.

7 年

Very creative & good thought process behind each analysis. Great Sir ji.??

Kapil Chowdhary, MBA

Regional Finance Controller - Eastern Europe, Middle East & Africa (EEMEA) at Orange Business

7 年

the good part about your articles is that there is so much to read between the lines

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