Artificial Intelligence has been a very popular area among computer scientists, researchers, and developers for many years. It is the capability to act intelligently and autonomously by machines in generic to more specific scenarios. We have come a long way in this field but there is still a long way to go to achieve a point where machines can think or act intelligently in a similar way to the human mind. For a few years now, we have been listening to the buzz word "Machine Learning". In this article, I am trying to give a high-level overview of machine learning, its applications and will introduce a platform called ML.Net provided by Microsoft to implement machine learning in .Net applications.

According to McKinsey, AI will create an estimated $13 trillion of GDP growth between now and 2030. As a comparison, the GDP of the entire United States of America was around 19 trillion in 2017. Leading AI scientists, like Andrew Ng, describe AI as the fourth industrial revolution or „the new electricity". AI is undoubtedly a centerpiece of digital transformation and its application throughout the industry will dramatically change our world and how we do business. The problem is that many people want to participate in this AI-revolution but they are overwhelmed by its technological sophistication. They don't know what AI is capable of, let alone how they could use it for their company.

On June 7, 2014, a Turing-Test competition, organized by the University of Reading to mark the 60th anniversary of Alan Turing's death, was won by a Russian chatterbot pretending to be a Russian teenage boy named Eugene Goostman, which was able to convince one-third of the judges that it was human. The media was abuzz, claiming a machine has finally been able to pass the Turing Test. The test was proposed by Turing in his 1950 paper, "Computing Machinery and Intelligence," in which he considered the question, "Can machines think?" In order to avoid the philosophical conundrum of having to define "think," Turing proposed an "Imitation Game," in which a machine, communicating with a human interrogator via a "teleprinter," attempts to convince the interrogator that it (the machine) is human. Turing predicted that by the year 2000 it would be possible to fool an average interrogator with probability of at least 30%.

Deep learning has been fantastically successful in recent years, and is responsible for better-than-human performance in image classification, face recognition and playing Go. Not everyone thinks that deep learning is the bee's knees -- because the conclusions it reaches can't be explained easily (they're not'interpretable'), and it tends to require a LOT of data and compute power. Combinations of deep and other learning methods may be far more powerful than one alone. How does machine learning relate to Artificial Intelligence (and Artificial General Intelligence)? AI refers to systems that can act intelligently, even in a very narrow scope.