A Sneak View At : Artificial Intelligence, Machine Learning And Deep Learning

AI is a very recent field in the history of science and human civilization.

- Murale Narayanan

Typically, only about sixty years old, it started with a very simple but fundamental quest: can machines think? Though this was pondered over by many leading scientists, it was Alan Turing who postulated practical theories on programmable and ‘intelligent’ machines. It was in the year 1956, a group of pioneering computer scientists including Marvin Minsky and John McCarthy started this field. In fact, it was John McCarthy, founder Stanford AI lab, coined the very term “artificial intelligence”, a stepping stone for the field - build machines that think.

As the first attempt, scientists drew inspiration from and started building machines that resemble and replicate to an extent, human thinking. The idea of human intelligence, include the ability to reason, see, hear, speak, move around and make decisions among others.

AI stemmed from a core and foundational dream many years ago – the thinking machines. Since inception, it proliferated into multiple subfields such as robotics, computer vision, natural language processing, and speech recognition to name few. Then a very important development happened around the 80s and 90s - a sister field called Machine Learning started to blossom; a field that combined statistics and mathematics with computer science to advance the era of ‘intelligent machines’, bringing us closer to AI. Fast forward to 21st century, deep learning with its deep roots into neurosciences, taking cues from human brain functionality, the neural networks have surpassed accuracies of earlier machine learning algorithms becoming the de-facto for many applications.