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!
