📄 Related papers: AlphaGo Zero paper
📊 Reference graph

1️⃣ Reinforcement learning (RL) is not a new concept in the AI ​​industry.

• It is already widely known in the AI ​​industry that reinforcement learning (🎯) is a powerful learning method.

• AlphaGo is a representative example of successful application of this to Go.

2️⃣ Supervised Learning vs. Reinforcement Learning

• 📌 Supervised Learning (purple line)

  • Learning and imitating the game data of human experts

  • Improves to a certain level, but cannot surpass the highest human level.

• 📌 Reinforcement Learning (blue line)

  • Play Go yourself and find the best strategy

  • Reaching superhuman abilities over time

  • Ultimately, AlphaGo achieves stronger performance than Lee (blue dotted line)

3️⃣ AlphaGo's groundbreaking move: 'Move 37'

• AlphaGo discovers a unique move that humans rarely make (1/10,000 chance)

• At the time, experts judged it to be a mistake, but it turned out to be an innovative strategy .

• This is an example of how reinforcement learning can enable creative thinking that surpasses human capabilities.

4️⃣ Scalability of Reinforcement Learning 🚀

• RL is now being applied to large language models (LLMs), and has the potential to go beyond simple human imitation.

• Discovering new logical patterns, creative problem solving, and even the possibility of creating new languages.

• Beyond games with correct answers like Go, research is underway to enable AI to develop in ‘open problems’

📌 The case of AlphaGo is an innovative case that shows that AI can not simply imitate humans, but can overcome human limitations through independent learning! 🚀