AlphaGo and AlphaZero.

AlphaGo and AlphaZero are both groundbreaking AI systems developed by DeepMind, but they have distinct architectures, purposes, and capabilities. Here’s a comparison of the two:

1. Purpose

• AlphaGo: Designed specifically to play the board game Go. It made history in 2016 by defeating world champion Lee Sedol in a series of matches, demonstrating AI’s ability to tackle complex strategy games.
• AlphaZero: A general-purpose reinforcement learning system that can master multiple games like Go, Chess, and Shogi from scratch without prior human knowledge or input beyond the rules of the game.

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2. Training Approach

• AlphaGo:
  • Used a combination of supervised learning (training on human professional games) and reinforcement learning (improving by self-play).
  • Heavily relied on human expert data as a starting point.
• AlphaZero:
  • Relied purely on reinforcement learning, learning by playing games against itself.
  • No input from human games, making it more generalized and autonomous.

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3. Architecture

• AlphaGo:
  • Featured a more complex architecture combining:
    • Policy networks (to suggest moves).
    • Value networks (to evaluate positions).
    • Monte Carlo Tree Search (MCTS) to plan and evaluate game states.
• AlphaZero:
  • Simplified the architecture into a single deep neural network that predicts both policy (best moves) and value (winning probability).
  • Still used Monte Carlo Tree Search but with a more efficient integration of the neural network.

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4. Performance

• AlphaGo:
  • Had variants (e.g., AlphaGo Fan, AlphaGo Master), each fine-tuned to improve performance.
  • Demonstrated superhuman ability in Go but was game-specific.
• AlphaZero:
  • Outperformed AlphaGo in Go after a short training period.
  • Became a state-of-the-art player in Chess and Shogi, defeating top AI programs like Stockfish and Elmo.
  • Demonstrated superior adaptability to new games.

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5. Generalization

• AlphaGo: Specialized for Go and unable to generalize to other games without extensive reengineering.
• AlphaZero: Generalized framework capable of mastering a wide range of games with little modification, showcasing the potential of reinforcement learning for broader applications.

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Legacy

• AlphaGo: Pioneered the AI revolution in games and inspired advances in deep learning and reinforcement learning.
• AlphaZero: Took AlphaGo’s success to the next level, showing that AI could be generalized across multiple domains, thus influencing research in AI for other real-world applications like protein folding (AlphaFold).

AlphaZero is considered the more advanced of the two due to its simplicity, generality, and greater performance across different domains.