Unravelling the mysteries of Artificial Intelligence

What does it mean for Deep Learning to recently beat Go champion Lee Sedol? Or what did it mean back in 1997 for Deep Blue to beat chess champion Garry Kasparov? Is the purpose of AI to only demonstrate that it can win against humans, or is it much more than winning? Such wins demonstrate the capabilities of AI, and open up new avenues for the tools and techniques used. In the case of Deep Blue developed by IBM, it was better search and evaluation algorithms, combined with a supercomputer to defeat a world champion. Similar AI algorithms were then applied to other applications including search engines. AI community continued its fascination of winning in games involving intelligence, with IBM Watson turning out to be a winner of quiz show Jeopardy. Watson even received the first place prize of $1 million. The AI techniques such as Natural Language Processing and Machine Learning that Watson used to win the competition are today driving the Watson Cloud Platform to understan

Although, there is not a simple answer for what came first - Chicken or Egg?, in Machine Learning, there is an easy answer. Data came first before any function. Machine learning is all about learning from data. The learning algorithm tries to learn a function that can either classify the data into different categories, or learn the function itself that plots the data. There are two popular ways in which a Machine Learning algorithm can be taught to learn a function, but in both cases it needs data. Supervised Learning : We give the algorithm a lot of data with both input and output, and it learns the function. In case of regression problems, the function approximately plots the function that understands the data. In case of classification problems, the function tries to classify the data. Unsupervised Learning : We give the algorithm a lot of input data with no output, and it tries to find patterns in the data. The algorithm classifies the data based on the similarities