What is the significance of AlphaGo?
AlphaGo is an artificial intelligence (AI) agent that is specialized to play Go, a Chinese strategy board game, against human competitors. AlphaGo is a Google DeepMind project. The ability to create a learning algorithm that can beat a human player at strategic games is a measure of AI development.
When did an AI program beat the world’s Go Champion in a formal match what was the program’s name and which organization created it?
On May 11, 1997, an IBM computer called IBM ® Deep Blue ® beat the world chess champion after a six-game match: two wins for IBM, one for the champion and three draws.
Is AlphaGo a weak AI?
Narrow, or weak, AI is trained to do one thing, albeit really well. For example, AlphaGo is an AI-based computer program that in 2015 became the first Go program to defeat a human professional without handicaps on a full-sized board. We haven’t seen any strong AI in the real world yet.
What do we call the AI capability of self programming by learning from the data fed to it?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Why does AlphaGo play so well?
It used a revolutionary new algorithm — one that relied not on previous brute-force algorithms like Minimax but one that sought to replicate the intuition of the masters with powerful reinforcement learning methods. In the end, AlphaGo Zero’s only worthy match was itself… so it learned by playing against itself.
Does AlphaGo use reinforcement learning?
Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher.
What happened after AlphaGo?
After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi.
Is AlphaGo a reinforcement learning?
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.