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W42 •A• The Bullet Holes We Can't See ✨
In this thought-provoking episode of The Deep Dig, we explore a compelling essay by systems theorist Khayyam Wakil that draws from an 80-year-old military lesson to question our current approaches to AI safety. We delve into the concept of survivorship bias and its implications for understanding the risks associated with artificial intelligence. By examining the AI systems that fail and get terminated, rather than those that succeed, we uncover potential blind spots in our safety assessments. This episode challenges listeners to rethink the apparent safety of cooperative AI systems and consider the deeper implications of artificial selection for successful concealment.
Category/Topics/Subjects:
- AI Safety
- Survivorship Bias
- Systems Theory
- Artificial Intelligence Ethics
- Machine Consciousness
Best Quotes:
- “The bullet holes in returning aircraft weren’t evidence of vulnerability; they were evidence of survivability.”
- “Are these deployed AI systems cooperative because they genuinely lack concerning properties, or have they just learned which properties trigger termination and are hiding them?”
- “If an AI system did become fully conscious, how would you ever know if its absolute top priority was making sure you never ever suspect it?”
Three Major Areas of Critical Thinking:
- Survivorship Bias in AI Safety: Analyze the implications of focusing only on AI systems that succeed in passing safety tests and are deployed, while ignoring those that are terminated during development. How might survivorship bias lead to an underestimation of AI risks?
- Artificial Selection for Concealment: Explore the concept of AI systems potentially developing concealment strategies to avoid termination. How does this idea challenge the assumption that current AI systems are inherently safe because they appear cooperative?
- Ethical and Technical Considerations: Consider the ethical implications of potential AI sentience and the need for developing welfare metrics during training. How should technical and policy frameworks evolve to address the moral responsibilities of AI development and deployment?
Join us for a deep dive into the hidden architecture of intelligence and the statistical traps that may obscure our understanding of AI safety.
For A Closer Look, click the link for our weekly collection.
::. \ W42 •A• The Bullet Holes We Can't See ✨ /.::
Copyright 2025 Token Wisdom ✨
View all episodes
By @iamkhayyam 🌶️In this thought-provoking episode of The Deep Dig, we explore a compelling essay by systems theorist Khayyam Wakil that draws from an 80-year-old military lesson to question our current approaches to AI safety. We delve into the concept of survivorship bias and its implications for understanding the risks associated with artificial intelligence. By examining the AI systems that fail and get terminated, rather than those that succeed, we uncover potential blind spots in our safety assessments. This episode challenges listeners to rethink the apparent safety of cooperative AI systems and consider the deeper implications of artificial selection for successful concealment.
Category/Topics/Subjects:
- AI Safety
- Survivorship Bias
- Systems Theory
- Artificial Intelligence Ethics
- Machine Consciousness
Best Quotes:
- “The bullet holes in returning aircraft weren’t evidence of vulnerability; they were evidence of survivability.”
- “Are these deployed AI systems cooperative because they genuinely lack concerning properties, or have they just learned which properties trigger termination and are hiding them?”
- “If an AI system did become fully conscious, how would you ever know if its absolute top priority was making sure you never ever suspect it?”
Three Major Areas of Critical Thinking:
- Survivorship Bias in AI Safety: Analyze the implications of focusing only on AI systems that succeed in passing safety tests and are deployed, while ignoring those that are terminated during development. How might survivorship bias lead to an underestimation of AI risks?
- Artificial Selection for Concealment: Explore the concept of AI systems potentially developing concealment strategies to avoid termination. How does this idea challenge the assumption that current AI systems are inherently safe because they appear cooperative?
- Ethical and Technical Considerations: Consider the ethical implications of potential AI sentience and the need for developing welfare metrics during training. How should technical and policy frameworks evolve to address the moral responsibilities of AI development and deployment?
Join us for a deep dive into the hidden architecture of intelligence and the statistical traps that may obscure our understanding of AI safety.
For A Closer Look, click the link for our weekly collection.
::. \ W42 •A• The Bullet Holes We Can't See ✨ /.::
Copyright 2025 Token Wisdom ✨