مسئله کنترل هوش مصنوعی (Persian Wikipedia)

Analysis of information sources in references of the Wikipedia article "مسئله کنترل هوش مصنوعی" in Persian language version.

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  • اوراکل چیست ؟. «چت با هوش مصنوعی». دریافت‌شده در ۲۰۲۴-۰۴-۲۸.

archive.org

  • Russell, Stuart (October 8, 2019). Human Compatible: Artificial Intelligence and the Problem of Control. United States: Viking. ISBN 978-0-525-55861-3. OCLC 1083694322.
  • Bostrom, Nick (2014). "Chapter 10: Oracles, genies, sovereigns, tools (page 145)". Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press. ISBN 978-0-19-967811-2. An oracle is a question-answering system. It might accept questions in a natural language and present its answers as text. An oracle that accepts only yes/no questions could output its best guess with a single bit, or perhaps with a few extra bits to represent its degree of confidence. An oracle that accepts open-ended questions would need some metric with which to rank possible truthful answers in terms of their informativeness or appropriateness. In either case, building an oracle that has a fully domain-general ability to answer natural language questions is an AI-complete problem. If one could do that, one could probably also build an AI that has a decent ability to understand human intentions as well as human words.
  • Bostrom, Nick (2014). "Chapter 10: Oracles, genies, sovereigns, tools (page 147)". Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press. ISBN 978-0-19-967811-2. For example, consider the risk that an oracle will answer questions not in a maximally truthful way but in such a way as to subtly manipulate us into promoting its own hidden agenda. One way to slightly mitigate this threat could be to create multiple oracles, each with a slightly different code and a slightly different information base. A simple mechanism could then compare the answers given by the different oracles and only present them for human viewing if all the answers agree.

arxiv.org

  • Carlsmith, Joseph (2022-06-16). "Is Power-Seeking AI an Existential Risk?". arXiv:2206.13353 [cs.CY].
  • Bommasani, Rishi; Hudson, Drew A.; Adeli, Ehsan; Altman, Russ; Arora, Simran; von Arx, Sydney; Bernstein, Michael S.; Bohg, Jeannette; Bosselut, Antoine; Brunskill, Emma; Brynjolfsson, Erik (2022-07-12). "On the Opportunities and Risks of Foundation Models". Stanford CRFM. arXiv:2108.07258.
  • Ouyang, Long; Wu, Jeff; Jiang, Xu; Almeida, Diogo; Wainwright, Carroll L.; Mishkin, Pamela; Zhang, Chong; Agarwal, Sandhini; Slama, Katarina; Ray, Alex; Schulman, J.; Hilton, Jacob; Kelton, Fraser; Miller, Luke E.; Simens, Maddie; Askell, Amanda; Welinder, P.; Christiano, P.; Leike, J.; Lowe, Ryan J. (2022). "Training language models to follow instructions with human feedback". arXiv:2203.02155 [cs.CL].
  • Doshi-Velez, Finale; Kim, Been (2017-03-02). "Towards A Rigorous Science of Interpretable Machine Learning". arXiv:1702.08608 [stat.ML].
    • Wiblin, Robert (August 4, 2021). "Chris Olah on what the hell is going on inside neural networks" (Podcast). 80,000 hours. No. 107. Retrieved 2022-07-23.
    • Hadfield-Menell, Dylan; Dragan, Anca; Abbeel, Pieter; Russell, Stuart (12 November 2016). "Cooperative Inverse Reinforcement Learning". arXiv:1606.03137 [cs.AI].
    • Hibbard, Bill (2014): "Ethical Artificial Intelligence"
    • Irving, Geoffrey; Christiano, Paul; Amodei, Dario; OpenAI (October 22, 2018). "AI safety via debate". arXiv:1805.00899 [stat.ML].
    • Leike, Jan; Kreuger, David; Everitt, Tom; Martic, Miljan; Maini, Vishal; Legg, Shane (19 November 2018). "Scalable agent alignment via reward modeling: a research direction". arXiv:1811.07871.
    • Everitt, Tom; Hutter, Marcus (15 August 2019). "Reward Tampering Problems and Solutions in Reinforcement Learning". arXiv:1908.04734v2.
    • Christiano, Paul; Leike, Jan; Brown, Tom; Martic, Miljan; Legg, Shane; Amodei, Dario (13 July 2017). "Deep Reinforcement Learning from Human Preferences". arXiv:1706.03741.

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mlr.press

proceedings.mlr.press

  • Langosco, Lauro Langosco Di; Koch, Jack; Sharkey, Lee D.; Pfau, Jacob; Krueger, David (2022-06-28). "Goal Misgeneralization in Deep Reinforcement Learning". Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning. PMLR. pp. 12004–12019. Retrieved 2023-03-11.

nih.gov

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openai.com

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psu.edu

citeseerx.ist.psu.edu

  • Goertzel, Ben (2012). "Should Humanity Build a Global AI Nanny to Delay the Singularity Until It's Better Understood?". Journal of Consciousness Studies. 19: 96–111. CiteSeerX 10.1.1.352.3966.

quantamagazine.org

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