人工智能,深度学习,ALife的动机:死亡率和生存风险
1University of Sussex, Evolutionary and Adaptive Systems Group. inmanh@gmail.com.
Artificial life
|February 12, 2024
概括
人工智能 (AI) 已经通过两种主要方法进化:GOFAIstic和网络学,后者推动了现代深度学习. 了解这一区别是解决AI问题的关键.
科学领域:
- 人工智能 (AI) 是一种人工智能.
- 人工生命 (ALife) 的人工生命
- 网络学就是网络学.
背景情况:
- 人工智能的历史有两个主要技术方法的特点:GOFAIstic (计算灵感) 和网络 (ALife灵感).
- 受人工生命影响的网络学方法在近期深度学习和当代人工智能的进展中发挥了重要作用.
- 人工智能问题是如何构成的,存在着一个显著的,经常被忽视的分歧,其中占主导地位的GOFAIST视角.
研究的目的:
- 为了调查过去一个世纪的人工智能的历史轨迹.
- 检查人工生命 (ALife) 对AI发展的影响.
- 分析不同人工智能框架方法对社会风险的影响,特别是与人工智能相关的存在风险.
主要方法:
- 人工智能方法的历史调查.
- 对GOFAI的比较分析AI中的动态和网络方法.
- 探索AI问题框架及其对感知风险的影响.
主要成果:
- 网络/ALife启发的方法推动了深度学习和现代人工智能的重大进展.
- 目前的人工智能开发主要使用GOFAIstic框架,创建没有固有的代理或动机的工具.
- 人工智能带来的生存风险,如"机器人接管",主要与人类用户有关,而不是人工智能机构.
结论:
- 网络化范式对人工智能的近期成功至关重要,它与社会风险一起提供了巨大的好处.
- 目前普遍存在的GOFAIStic将人工智能系统视为仅仅是工具的框架,可以减轻人工智能代理产生的生存风险.
- 未来的人工智能开发和风险评估应该承认人工智能能力和人类用户意图之间的区别.
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