What Should an Executive Know about Machine Learning? "Reinforcement Learning"

The age of advanced data analytics & cognitive computing has enabled the use of complex algorithms across a wide range of business problems, industries, verticals etc.

These advancements have led to usage of innumerable jargon on Machine Learning, data modelling, AI and RPA. Given, we understand that these machine learning models & complex algorithms solve abstract business problems (of course, with probability), improve performance, & create a differentiation positioning for their user. The challenge is that these algorithms, mostly, act like a black box & too much dependency upon them for decision making, without knowing their boundary conditions, can lead to innumerable risks.

So, understanding Machine Learning in simple English can help us all to make a better choice on our dependency upon such algorithms.

Machine Learning basically can be categorized under three buckets:

Reinforcement Learning

This learning enables software or your data model to self-determine the ideal behavior, ceteris paribus (i.e. other things equal), so that the performance of the software or the data model is maximized.

This model works as an interaction between two elements – the learning agent (your software/ data model) and the environment.

The reinforcement signal is sent to the learning agent as an award for every correct action which the learning agent takes. This mechanism repeats and the continuous rewards improves the agents’ learning of its environment. So, if you are thinking it’s more like a trial & error way of learning, then you are right!

Case in point #1

You are devising a data model for a robot, which will help tourists navigate a historical place.

While the robot is in learning stage, every time the robot takes 10 steps on the left, it is hit by a wall. So, now, after multiple iterations, it knows that 10 steps is dangerous. Hence, the next time, instead of taking 10 steps, it will understand that the passage on the left is 4 (for e.g.) steps away.

Case in point #2

Your robot is designed to store/retrieve products for optimized space utilization in a warehouse.

Suppose the warehouse is divided into blocks. Now the robot is able to place 100 items in block A, but for every 101(st) parcels, the entry to that block is jammed. So, the rewards system from the environment (in this case), entry to the blocks, will enhance its learning of the layout of the warehouse.

The above two examples are very crude way to understand how these algorithm works.

Supervised Learning & Unsupervised Learning

These two are very often heard in business meetings, sold in sales pitches of software vendors etc. To make it simpler, I have written a complete article on both these terminologies. You can read it here

In my next post, I shall try to break down these three buckets into simpler sub-buckets, categorizing many more jargon into these three buckets.

Understanding basics of ML, its components & use cases can lead you to be an innovator & a disruptor in your industry. Start small but start learning!