#artificialintelligencelearningcenterinkochi

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In 2005, Garry Kasparov popularized the idea of a FreeStyle Chess Tournament. It for the first time chess players could use any combination of man and machine to predict what their next move will be.

Hydra, a supercomputer more capable than Deep Blue (a supercomputer which had conquered Garry himself in a game of chess in 1996), came to the third position.

one of the other ways the machines tend to perform tasks by themselves is through Neural Networks. As technology improved in a way that it has the ability to understand how our minds work and it is progressed to understand the concept of what constitutes AI has changed. Neural networks are rough simulations of the human brain. They have thousands of neurons that have millions of synaptic connections amongst themselves.

And yet, two amateur chess players with weak computers, but a robust machine learning system, beat both the excellent chess masters and the Supercomputer Hydra to bag the first place.

How was this possible? Well, first, Hydra analyzes the positions of pieces on the chess board. Then, it calculated the possible movements of itself and the opponent team too. Using these set of probable outcomes, it made its upcoming move.

But Hydra was hardly a set of static, programmed rules. It does not have a memory. It could not use its prior experience to determine which move to make next. When we coach a machine to do that, we call it as Machine Learning. Although ML and AI are used quite interchangeably in common platforms, Machine Learning, just a current state-of-art technique which we have to implement AI.

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Artificial intelligence can simply be described as hardware and software systems combined to simulate human intelligence. To do so, they need to be able to ingest information and determine which information is best to use. AI makes up rules, that set operational processes that lead to conclusions. AI can correct itself in order to progress and ultimately define and pursue your business goals.