Google's AlphaGo & it's fairy tale !!!

AlphaGo is a AI computer program developed by Alphabet Inc.'s Google DeepMind in London to play the ancient Chinese board game Go, a territory game. Players take turns to place black or white stones on a full-sized 19x19 board, trying to capture the opponent’s stones or surround empty space to gain points.

It’s been estimated there are 10 to the power of 700 possible ways a Go game can be played - more than the number of atoms in the universe. The number compares to about 10 to the power of 60 possible ways in chess, which shows the complexity.

Recently AlphaGo defeated the legendary Go player Lee Sedol, winner of 18 world titles and widely considered to be the greatest player of the past decade in the Go game.

How this became possible for AlphaGo to achieve this feet?

1. AlphaGo was built using "advanced tree search with deep neural networks" a widely used concept to build the AI system now-a-days.
2. AlphaGo is a self taught system which implemented a process known as reinforcement learning means "Letting the system play against itself to let the system improve itself".
3. AlphaGo has watched 100,000 Go games downloaded from the Web & self-trained to mimic moves of human Go players. 
4. AlphaGo played 1.2 million games with randomly selected previous version of itself.
5. It started playing game against amateurs & professional Go players which helped AlphaGo to learn all the best moves.
6. AlphaGo was trained on 30 million moves from games played by human experts, until it could predict the human move 57 percent of the time 
7. Since AlphaGo requires huge processing power Google Cloud platform was utilized for compute power.
8. AlphaGo ran on 48 CPUs and 8 GPUs and the distributed version of AlphaGo ran on 1202 CPUs and 176 GPUs.

Why this win is important in the AI arena ?

AlphaGo uses general machine learning techniques to figure out for itself how to win at Go. The same can be applied to important real-world problems. Because the methods used are general-purpose,which means one day they could be extended to address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis.

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