AlphaGo - An Artificial Intelligence Superpower

Yann LeCun quoted, “Our intelligence is what makes us human, and AI is an extension of that quality.”

AlphaGo is the first computer program that defeated became popular in around 2016n after it was able to defeat human players in games. AlphaGo has become that power over the years that doesn’t need human intervention at all because it learns all superhuman skills by playing itself every time. Doing the artificial intelligence course in Delhi will add lot of value.

It is basically a self-taught gaming software. AlphaGo is a reality mirror that shows us how close we are to big changes in the way we live life.

According to the deep mind, AlphaGo approach is, “To capture the intuitive aspect of the game, we needed a new approach.

We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections. 

One neural network, the “policy network”, selects the next move to play. The other neural network, the “value network”, predicts the winner of the game. We introduced AlphaGo to numerous amateur games to help it develop an understanding of the reasonable human play.

Then we had it play against different versions of itself thousands of times, each time learning from its mistakes. Over time, AlphaGo improved and became increasingly stronger and better at learning and decision-making.

This process is known as reinforcement learning. AlphaGo went on to defeat Go world champions in different global arenas and arguably became the greatest Go player of all time.”

Go is a Chinese board game like chess to be played with two players, played with two colour figures- black and white, changing position with of respective colours in each turn. Chess is perhaps a more famous board game with white and black pieces but Go has more possible moves.

“AlphaGo is based off a Monte Carlo algorithm tree search based looking at a list of possible moves from its machine-learned repertoire. Algorithms and learning differ among the various versions of AlphaGo. AlphaGo Master, the version that beat the world champion Go player Ke Jie, uses supervised learning.

AlphaGo Zero, the unsupervised learning version of AlphaGo, learns by playing against itself. First, the AI plays randomly, then with increasing sophistication. Its increased sophistication is such that it consistently beats the Master version that dominates human players.”

The results of go-game demonstrate an approach called ‘reinforcement learning’. A simple example of reinforcement learning is giving your pet a command and treating them when they listen to it, which reinforces positive behaviour and good feedback.

In the same way, by using reinforcement learning, machines are programmed. These programmed machines can produce data they learn from and use this database.

"A reinforcement learning model is an approach that is free from training datasets, such success can open new avenues in personalized medicine. Instead of determining the treatment (e.g. drug dosage or radiotherapy sessions) based on data from 'other' patients, reinforcement learning models could learn from the same patient's data and determine a treatment that fits this particular patient's condition.

This can impact patients with chronic diseases such as diabetes and renal failure where a continuous regulation of specific blood markers (sugar and haemoglobin, respectively) is required." Said Mohamed Helmy, Head of Bioinformatics at BenchSci.

However, reinforcing learning may not be largely applicable beyond applications where there are immediate reward signals. In video games and robots reinforcement learning today is done. Madrid Software Trainings is the best artificial intelligence training institute in Delhi.

AlphaGo Zero is an important advance. "It probably means that given a fast enough feedback cycle, it can learn anything and beat a human at it in about a month." And if that expedites bio-medical research and drug discovery, everybody wins.