Theoretical concept of Machine Learning and Deep Learning in Artificial intelligence

Artificial intelligence has been the most intriguing topic of 2018 according to McKinsey.AI became a catch-all term that refers to any computer program that automatically does something. Many people make referrals to AI without actually knowing what it really means. When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols .There is public debate on whether it is an evil or savior for humanity. Thus this is yet another attempt to compile & explain the introductory AI/ML concepts to go beyond this buzz for non-practitioners and curious people. Artificial intelligence as an academic discipline was founded in 50s. Actually the ―AI‖ term was coined by John McCarthy, an American computer scientist, back in 1956 at The Dartmouth Conference. According to John McCarthy, AI is ―The science and engineering of making intelligent machines, especially intelligent computer programs‖. Though it was not until recently it became part of daily life thanks to advances in big data availability and affordable high computing power. AI works at its best by combining large amounts of data sets with fast, iterative processing and intelligent algorithms. This allows the AI software to learn automatically from patterns or features in that vast data sets. It is typical now we see AI news and examples on the mainstream news. Arguably the popularity milestone with public awareness was AlphaGo artificial intelligence program that ended humanity‘s 2,500 years of supremacy in May 2017 at the ancient board game GO using a machine learning algorithm called ―reinforcement learning‖. Then these kinds of AI news become part of our daily digests with self-driving cars, Alexa/Siri like digital assistants frenzy, real time face recognition at airports, human genome projects, Amazon/Netflix algorithms, AI composers/artists, hand writing recognition, Email marketing algorithms and the list can go on and on. While Deep neural network, the most advanced form of AI, is at the top of the Gartner ‘s 2018 hype cycle that is a sign of inflated expectations, self-driving cars have already made millions of miles with relatively satisfactory safety records. Artificial intelligence technologies will continue disrupting in 2019 and will become even more widely available due to affordable cloud computing and big data explosion. We do not recall any other tech domain right now that attracts so many smart people & vast resources from both the open source/maker community and the largest enterprises at the same time.