Few days back I was talking to a group of friends at work around availability of data and challenge of using it to generate insights. One of the take-away from this short discussion was that apart from the expertise to use the tools i.e. the technical know-how Business folks should be aware about the applicability so that they can guide the implementation and reap benefits. This short article is an attempt is to create a short guide.
Lets first of all clarify the doubts between the 3 related terms in the field of analytics
Artificial Intelligence : Anywhere we wanted the computers or machines to perform intelligent tasks. All the techniques e.g. Linear regression, Discriminat Analysis etc are traditional (now) analytical techniques. All these techniques have some kind of constraints or assumptions
Machine Learning are set of techniques which are more advanced and could solve non linear problems as well. These are robust to the assumptions.
Deep learning are closest to mimic human learning since these are based on how brain functions. With these techniques seemingly difficult tasks e.g. Lip reading where ML outperformed humans.
As per McKinsey report – Feedforward Neural Network and Convolutional Neural Networks have potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries.
This is just the begining…watch this..(Click on full screen button)