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Friendly Superintelligence

Friendly Superintelligence. My assumptions. Need to make friendliness work in general, not just for particular AI designs we do not know which will succeed Hard takeoff unlikely AI will develop over time in interaction with society

aurelia-allison
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Friendly Superintelligence

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  1. Friendly Superintelligence

  2. My assumptions • Need to make friendliness work in general, not just for particular AI designs • we do not know which will succeed • Hard takeoff unlikely • AI will develop over time in interaction with society • The context systems are developed in must be taken into account, we cannot use simple a priori arguments

  3. Friendly AI is in the end a practical problem • AI will be created for economic reasons, and will be involved in economic transactions with humans from the start. • Whether AI, IA or something else will be developed will be determined only to a minor extent by deliberate global choices and more by what technologies provide payoffs during their development

  4. Friendliness as a game • Friendly AI as a game: we want an infinite game for humans • It is not a game for a single player, but from the start consisting of many different players with slightly different goals.

  5. Do we aim for no risk or acceptable risk? • As risks become smaller the cost of removing them increases with no limit • The hard take-off assumption assumes that there is going to be one gamble with a single large risk, while the soft take-off implies many interactions with medium risks.

  6. Suggested approaches to friendly AI • Internal constraints (Asimov’s laws) • Built in values or goals (“Love humans”) • Learned values (Brin, Lungfish) • External (law, economics)

  7. Problems with the approaches • Asimov laws allow accidental unfriendly behaviour • the full consequences of a complex formal system are unknowable, and being in contact with the messy real world makes things worse.

  8. Internal constraints and values are design solutions, but there are many designers and some might be malevolent, misguided or make mistakes. • Designs compete with each other - a risky architecture may show greater economic potential

  9. If values are learned, then they can be mis-learned. • External approaches can seldom be proven to work due to their complexity.

  10. Law of comparative advantages • Trade is mutually profitable even when one part is more productive than the other in every commodity that is being exchanged • specialisation enables the more productive agent to produce more of the commodity most profitable to it. • AI and humans can profit from specialisation, even when their capabilities are vastly different.

  11. External Approaches • Seek to reward friendliness and punish unfriendliness • Relevant for the soft takeoff scenarios • AIs that have “grown up” within a human culture are more likely to encompass its ethics and values, and have tight economical connections • Defection is profitable only as long as there are no interactions that can make it unprofitable

  12. A Combination Approach • Guidelines for AI development • will be useful for selling AI in any case • Good rearing? • Make sure we set up a legal and economical framework where friendly AIs prosper and unfriendly are inhibited • This will not be a guarantee of friendliness, any more than current systems of upbringing, education and law guarantee it.

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