A Game Plan for the AI Boom

TL;DR

AlphaGo, developed by Google DeepMind, significantly impacted the field of AI after defeating top human Go players, including Lee Sedol.
The technical frameworks used for AlphaGo, such as pairing proposing and judging algorithms with reinforcement learning, are foundational to today's advanced AI, including reasoning models.
The concept of scaling AI not just through data and computing power, but also by dedicating more time to a task, emerged from the AlphaGo experience.
A key challenge for current AI is the lack of simple, clear evaluation metrics, unlike the win/loss outcomes in board games.
The author draws parallels between AlphaGo's impact on Go and chess, suggesting that advanced AI may complement human roles, fostering creativity and new strategies rather than wholesale replacement.
The article questions the business proposition of generative AI automating work versus the human value of learning and striving.

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