There is a big talk on Responsible AI for quite some time now. Every other AI player is building and offering solutions around it. Billions of experts are voicing their opinion and there are regulatory measures being setup. This article is my small addition or responsibility towards Responsible AI

Let’s start with defining responsible AI. There are multiple components of it such as Fairness, Explanability, Interpretability, Privacy (Regulatory) etc.

I am just giving my perspective on these different components, when we look at the solution developed using AI.


Let’s start with an example of Fairness. Sometime back many of…

“Jan 2019: Gartner says 80% of analytics insights will not deliver business outcomes through 2022 and 80% of AI projects will “remain alchemy, run by wizards” through 2020”

Why is this? At one side we are talking about Driverless cars and AI bots beating best players in the world, but when it comes to business outcomes, AI is failing.

Disclaimer: Below given is completely my point of view and you all are smart enough to accept and reject based on your natural intelligence.

Let’s start with design challenges of non-IT industries. This is where I was born and brought up…

This is an attempt to solve flow distribution modeling using Qubo Modeling technique, so as to run the algorithm on a Quantum Computer.

This blog is about my personal journey from manufacturing to IT and the similarity in its diversity…

Process Analogy

In manufacturing or IT world, we apply different techniques/frameworks to design or enhance processes and products. One such six sigma driven methodology in manufacturing world talks about 3 Ms: Man, Machine and Methods and then we had an extension with 4th M: Material.

Then I entered altogether a different world of IT, with a thought that I need to unlearn to learn. But what, IT world just changes these 3 Ms to People, Process and Technology. Thats great! Not so different which I thought so. …

Many of us already know what is bullwhip effect, but just to warmup and set the context let’s understand what and why of it.


Bullwhip effect in supply-chain means increase in variability(inventory) upstream of the supply-chain. Trying to keep it simple to communicate the point of view.


Bullwhip effect occurs when we become too reactive to demand (requirements) and amplify expectations at each step of upstream of the supply chain, causing overall variability or inventory in the system to go up.

Now let’s see if we can draw its analogy in AI.

Let’s start with the most important attribute of…

Few days back, I became a parent, and have realized that its way more beautiful and way more challenging to parent a child than to parent a machine.

When I say, parent a machine means teaching a machine, how to work. You know , I am also one of those, who is into the world of machine learning!

Why more Challenging?

Because Babies (humans) are smart and they have mind of their own.

Humans have 5 senses (of-course some may have sixth or beyond) which are inherent and that’s the beauty of nature or mankind. So the babies are always learning, through observations…

Today, let’s talk about the journey of a startup moving towards being a Unicorn, and how its journey is similar (if not exact) to Application Development Journey of following, NoOps to DevOps.

Hypothetical Scenario: I with 5 youngsters (fresh from College), floated a start-up 2 years back, on developing an yet another aggregator app, providing a dedicated service in one city for few people/customers.

As I was working with only 5 but excellent and bright young minds, I talked to them on a daily basis on the latest developments, and almost all of us got involved into all the functions…

I am not an expert of Behavioral Economics. Lets leave that to Richard Thaler and Daniel Kahneman of the world. This article is about how to avoid mistakes while predicting Irrationality, which covers more than half of the decisions that we take today, be it personally or professionally. Again I am not an personality expert, so I will limit my writeup on appications of AI in Nudge management around business decisions.

Lets take the example of applying AI in pricing. Before I define the problem statement, its necesaary for us to understand the concept of decoy pricing. You can read…

Today’s topic is Active Learning.

Its a branch of Machine Learning to seek the right observations/teachers for effective learning. Now that’s quite obvious right! Even in real life we try to seek right teaching collaterals or a teacher, for effective learning.

So how this is similar or different in active learning. Lets define a Machine Learning use-case to understand the concept.

Suppose, you want to develop a model to predict likely loan default cases. In total, you have 10000 data points/rows of data and have only 1000 labeled cases, out of this total data points.

As mentioned above, data is…

No, the blog is not on the movie “2 states”, neither its on “Kaun Banega Crorepati”, but the blog is on, one of the most interesting and emerging technology “Quantum Computing”.

Then, why KBC and 2 states in the heading? Lets quickly understand the context.

Suppose you are on the hot seat of KBC and playing for one crore, the final question. You are totally confused between two options from the given 4 answers. In the classical world you can figure out the right answer by choosing either of the 2 options in sequence i.e. you can choose only one…

Rahul Kharat

Explorer | Teacher | AI Consultant, CXO Advisory | 18 Patents | 2 International Publications | |

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