Gautam Naik was born and brought up in Pune and currently stays in Connecticut USA. He has more than twenty-five years of Entrepreneurship experience in Global Software Services. His successes as an IT entrepreneur include leading sizable teams globally, serving clients globally including fortune500 companies, and a couple of VC investments/successful mergers. He graduated from IIT Kharagpur as an Instrumentation engineer. He is a technocrat and has documented best practices and showcased the products launched by companies like Microsoft, Novell, and few Pioneering open-source technologies in various international meets/events/seminars.

Discovery of new natural laws has for a long time relied on the inspiration of some genius. Recently, however, machine learning technologies, which analyze big data without human prejudice and bias, are expected to find novel natural laws. Here we demonstrate that our proposed machine learning, recursive-LASSO-based symbolic (RLS) regression, enables data-driven formulation of natural laws from noisy data. The RLS regression recurrently repeats feature generation and feature selection, eventually constructing a data-driven model with highly nonlinear features. This data-driven formulation method is quite general and thus can discover new laws in various scientific fields.

Lost amongst the hype and hyperbole surrounding machine learning today, especially deep learning, is the critical distinction between correlation and causation. Developers and data scientists increasingly treat their creations as silicon lifeforms "learning" concrete facts about the world, rather than what they truly are: piles of numbers detached from what they represent, mere statistical patterns encoded into software. We must recognize that those patterns are merely correlations amongst vast reams of data, rather than causative truths or natural laws governing our world. As machine learning has expanded beyond its roots in the worlds of computer science and statistics into nearly every conceivable field, the data scientists and programmers building those models are increasingly detached from an understanding of how and why the models they are creating work. To them, machine learning is akin to a black box in which you blindly feed different mixes of training data in one side, twirl some knobs and dials and repeat until you get results that seem to work well enough to throw into production.

AI has a long history of making fine tools, and an equally long history of trying to simulate human intelligence, without, I contend, really understanding what intelligence consists in: the ability to deal with the world, which presupposes having a stake in one's own being. The tools are very nifty, but I don't see how it is even possible to simulate having a stake in one's own being.