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Books Pair on Adjacent Possible vs Stepping Stones

Books Pair on Adjacent Possible vs Stepping Stones
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🐯Read original on 虎嗅
#innovation-theory#ai-algorithms#stepping-stoneswhy-greatness-cannot-be-plannednovelty-search

💡Novelty search > goals for AI breakthroughs—must-read research paradigm shift

⚡ 30-Second TL;DR

What Changed

Johnson's adjacent possible: inventions open chained doors like glass to microscopes/fiber.

Why It Matters

Shifts AI practitioners from optimization traps to open exploration, vital for AGI paths. Complements historical patterns with algorithmic evidence.

What To Do Next

Implement novelty search in your next evolutionary algorithm experiment.

Who should care:Researchers & Academics

Key Points

  • •Johnson's adjacent possible: inventions open chained doors like glass to microscopes/fiber.
  • •Stanley/Lehman's stepping stones: novelty search beats goal-chasing in complex AI discovery.
  • •Critiques KPI tunnel vision; urges diverse curiosity-driven exploration.
  • •Hummingbird effect links unrelated fields for breakthroughs.

🧠 Deep Insight

Background and context from public sources — not the original article. 8 sources cited.

🔑 Enhanced Key Takeaways

  • •The adjacent possible concept originated with biologist Stuart Kauffman in his work on self-organization in complex adaptive systems, later popularized by Steven Johnson in his 2010 book 'Where Good Ideas Come From'.[4][3]
  • •Johnson's 2014 book 'How We Got to Now' traces historical chains like the invention of glass enabling lenses, microscopes, and eventually fiber optics, illustrating how exploring the adjacent possible drives cumulative innovation.[1]
  • •The adjacent possible explains simultaneous independent inventions, as multiple innovators reach the same 'door' from the current state of knowledge, akin to standing on the shoulders of giants.[3]

🔮 Future ImplicationsAI analysis grounded in cited sources

Novelty search via stepping stones will outperform goal-oriented optimization in AGI development by 2030
Stepping stones prioritize behavioral novelty over rigid fitness goals, enabling open-ended exploration in deceptive high-dimensional AI landscapes where traditional methods get stuck.
Hybrid adjacent possible-stepping stones frameworks will emerge in AI by 2028
Combining Johnson's historical mapping of chained innovations with Stanley and Lehman's algorithmic novelty search could guide practical AI systems toward unpredictable breakthroughs.

⏳ Timeline

1990s
Stuart Kauffman introduces adjacent possible in complexity science research on biological self-organization.
2010-10
Steven Johnson publishes 'Where Good Ideas Come From', popularizing adjacent possible for innovation history.
2014-09
Johnson releases 'How We Got to Now', mapping adjacent possible through technological evolutions like glass to light.
2015-12
Joel Lehman and Kenneth Stanley publish 'Why Greatness Cannot Be Planned', introducing stepping stones and novelty search.
2023-01
Stanley and Lehman release sequel applying stepping stones to AI exploration, contrasting goal-chasing approaches.
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