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基础科学的未来由生成性闭环人工智能带领
Hector Zenil1,2,3,4,5,6, Jesper Tegnér7,8, Felipe S Abrahão3,4,9,10
1Research Departments of Biomedical Computing and Digital Twins, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Frontiers in artificial intelligence
|February 27, 2026
概括
人工智能 (AI) 现在可以自主执行整个科学周期. 然而,科学家必须仔细平衡人工智能.
科学领域:
- 人工智能在科学发现中的作用
- 计算科学 计算科学
- 科学哲学的科学哲学
背景情况:
- 人工智能正在接近自主执行完整科学循环的能力,从假设生成到验证.
- 当前的人工智能系统仍然需要人类干预来处理数据,超参数调整,可解释性和定义令人满意的解释.
- 人工智能模型正在探索超越人类直觉的复杂假设空间,需要新的科学验证方法.
研究的目的:
- 探索人工智能在科学研究中越来越多的自主权的影响.
- 提出将人工智能纳入科学过程的框架,同时保持人类的相关性和控制.
- 解决与科学中不关键的AI部署相关的AI和认识体系崩的风险.
主要方法:
- 在科学周期中对AI能力的概念分析.
- 对生成模型的混合因果和神经符号方法的讨论.
- 检查科学中人工智能的治理策略.
主要成果:
- 人工智能可以以机器速度关闭科学循环,但人类监督仍然至关重要.
- 在没有理解的情况下过度依赖人工智能会带来模型和认识体系崩的风险.
- 人工智能方法与科学领域的原则匹配是必不可少的.
结论:
- 倡导人工智能科学中的分级自治,平衡机器速度与人类优先事项.
- 强调在人工智能驱动的科学中需要可验证的机制和适当的领域理解.
- 警告反复训练和不批判地重复使用AI模型,以防止科学停滞.
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