Back
AI Behind AlphaGo: Machine Learning and Neural Network - USC Viterbi School of Engineering
webillumin.usc.edu·illumin.usc.edu/ai-behind-alphago-machine-learning-and-ne...
A student-level explainer from USC useful for understanding the technical foundations of AlphaGo, relevant as background context for discussions of AI capability milestones and the trajectory of reinforcement learning research.
Metadata
Importance: 30/100blog posteducational
Summary
This USC Viterbi article explains the core AI techniques behind DeepMind's AlphaGo, including deep reinforcement learning, policy networks, value networks, and Monte Carlo Tree Search. It provides an accessible overview of how these methods combined to defeat world-class Go players. The piece serves as an educational introduction to the capabilities that marked a milestone in AI development.
Key Points
- •AlphaGo combines deep neural networks with Monte Carlo Tree Search to evaluate board positions and select moves in the game of Go.
- •Policy networks predict probable next moves by training on human expert games, then refining via self-play reinforcement learning.
- •Value networks estimate the probability of winning from a given board state, reducing the need for exhaustive search.
- •AlphaGo's success demonstrated that deep learning could master tasks previously thought to require human intuition and strategic reasoning.
- •The article highlights the broader significance of AlphaGo as evidence of rapid AI capability advancement beyond prior expectations.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Deep Learning Revolution Era | Historical | 44.0 |
Cached Content Preview
HTTP 200Fetched Feb 22, 202615 KB
AI Behind AlphaGo: Machine Learning and Neural Network - USC Viterbi School of Engineering
Skip to content
Sunday, February 22, 2026
Latest:
Editors’ Note – Volume XXV Issue III
AI and Games: Partners in Innovation
Striking Physics: The Science Behind Bowling
Chewing Gum: The Newest Study Tool
Through RAG Comes Revolution: Efficient & Accurate AI
Computer Science Entertainment Health & Medicine Issue I Sports & Recreation Volume XIX
About the Author: Yiqing Xu
Yiqing Xu is a senior studying Computer Science with an interest in a variety of programming languages and a solid math background.
Abstract
The board game Go has been viewed as one of the most challenging tasks for artificial intelligence because it is “complex, pattern-based and hard to program”. The computer program AlphaGo’s victory over Lee Sedol became a huge moment in the history of artificial intelligence and computer engineering. We can observe AlphaGo’s enormous capacity, but people know little about how it “thinks”. AlphaGo’s rules are learned and not designed, implementing machine learning as well as several neural networks to create a learning component and become better at Go. Seen in its partnership with the UK’s National Health Service, AlphaGo has promising applications in other realms as well.
Background
From March 9 to March 15 in 2016, a Go game competition took place between the world’s second-highest ranking professional player, Lee Sedol, and AlphaGo, a computer program created by Google’s DeepMind Company. AlphaGo’s 4-1 victory over Lee Sedol became a significant moment in the history of artificial intelligence. This was the first time that a computer had beaten a human professional at Go. Most major South Korean television networks carried the game. In China, 60 million people watched it; the American Go Association and DeepMind’s English-language livestream of it on YouTube had 100,000 viewers. A few hundred members of the press watched the game alongside expert commentators [1]. What makes this game so important? To understand this, we have to understand the roots of Go first.
Go Game
Go, known as weiqi in China and igo in Japan, is an abstract board game for two players that dates back 3,000 years. It is a board game of abstract strategy played across a 19*19 grid. Go starts with an empty board. At each turn, a player places a black or white stone on the board [2]. The general objective of the game is to use the stones to surround more territory than the opponent. Although the rule is very simple, it creates a challenge of depth and nuance. Thus, the board game, Go, has been viewed as one of the most challenging tasks for ar
... (truncated, 15 KB total)Resource ID:
9dbe6e22c4589545 | Stable ID: YTM0OWQyZj