伦理的人工智能的人类成本
James K Ruffle1, Chris Foulon2, Parashkev Nachev2
1Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK. j.ruffle@ucl.ac.uk.
Brain structure & function
|June 23, 2023
概括
基础模型需要广泛的人类监督内容过和输出评估. 开发这些人工智能模型需要在定量伦理学方面取得进展,以解决固有的主观性并确保负责任的部署.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 伦理学 伦理学 伦理学
背景情况:
- 像ChatGPT这样的基础模型依赖于庞大的互联网数据集.
- 这些数据集包含有关逻辑,事实,道德和合法性的各种内容.
- 缩放数据是一个技术上的挑战,但语义过需要人类的投入.
研究的目的:
- 突出在基础模型开发中人类监督的必要性.
- 解决人工智能培训中降低成本和质量保证之间的冲突.
- 探索为复杂语义,特别是道德推理创造客观函数的挑战.
主要方法:
- 在基础模型中分析数据缩放和语义过.
- 检查人工智能培训和成果评估中人类监督的作用.
- 讨论当前人工智能在处理道德等复杂主观概念方面的局限性.
主要成果:
- 基础模型需要显著的人类干预来实现可靠的语义过.
- 人工智能模型的主观性受到人类创造者的信仰的影响.
- 尽量减少培训成本与确保高质量的AI输出相冲突.
- 将复杂的语义,特别是道德概念,简化为机器可读的客观函数是有问题的.
结论:
- 基础模型开发需要大量的人类监督,影响客观性.
- 在定量伦理学方面的紧急创新对于推进基础模型至关重要.
- 需要进一步的研究来开发将道德考虑纳入人工智能的方法.
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