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Optimal Safety Framework for AI and Robots

Explore the proposed approach for ensuring the safety of AI and robots by avoiding irreversibilities and focusing on explicit goals and rights. Recommendations include passive safety strategies and implementation methods using logical programming and machine learning.

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Optimal Safety Framework for AI and Robots

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  1. Safety of AI and robots Roland Pihlakas Institute of Technology in University of Tartu 26. April 2008

  2. You may do it like • Good states - goals • Bad states - don’t do-s

  3. In contrast, proposed approach is kind of orthogonal • Implicit: avoid any irreversibilities (does not need pre-specified bad states) • Explicit: goals (good states)

  4. A small addition... • Implicit: avoid any irreversibilities (no pre-specified bad states) • Explicit: rights (still no pre-specified states; instead - sorts of states) • Explicit: goals (good states)

  5. In short • 1. Rights • 2. Goals

  6. An analogy: the three laws of robotics • 1. Don’t do harm. => Avoid irreversibilities • 2. Do what you are ordered to do. • a. Goals • b. May include explicitly stated bad states • 3. Be optimal, be tidy, clean up reversible temporary changes. Recommendations.

  7. “passive” safety - avoid only own mistakes

  8. Goals value of indicator current measured preferred state planned action

  9. Reversibilities first action value of indicator current measured first measured state second action

  10. IR-reversibilities No-no action: don’t do this - don’t cause irreversible states value of indicator state to avoid first measured state cannot achieve - cannot reverse the first action

  11. Permissions Not reversible, but okay action value of indicator state,which we dont need toavoid first measured state cannot achieve - cannot reverse the first action

  12. Implementation • Logical programming; planning is using theorem provers • Statistical / machine learning; control system

  13. A safe AI should not increase entropy, except only temporarily.

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