现有AI在简单推理任务上常出错，因依赖统计学习而非精确推理。
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
- 从统计学习转向精确学习，要求所有输入都正确推理。
- 当前最先进模型在简单逻辑题上仍频繁失败。
- 适合追求可靠推理的通用AI研究者阅读。
可靠的演绎推理——从已有事实和规则中推导新知识——是通用智能不可或缺的特征。尽管人工智能在数学与科学领域取得显著进展，尤其自变压器架构引入以来，但研究表明，即使最先进的前沿系统在看似简单的演绎推理任务上也持续出现错误。因此，这些系统无法实现具备可靠演绎推理能力的人工通用智能。我们指出，其不严谨行为源于驱动其发展的统计学习范式。为克服此问题，我们认为要使基于学习的AI系统具备可信赖的演绎推理能力，研究者必须根本性转变：不再以推理问题分布上的统计性能为目标，而应采纳更具雄心的精确学习范式，即对所有输入都保证正确性。我们主张，精确学习既必要又可行，应成为算法设计的指导原则。
原文摘要 · Abstract (English)
Sound deductive reasoning -- the ability to derive new knowledge from existing facts and rules -- is an indisputably desirable aspect of general intelligence. Despite the major advances of AI systems in areas such as math and science, especially since the introduction of transformer architectures, it is well-documented that even the most advanced frontier systems regularly and consistently falter on easily-solvable deductive reasoning tasks. Hence, these systems are unfit to fulfill the dream of achieving artificial general intelligence capable of sound deductive reasoning. We argue that their unsound behavior is a consequence of the statistical learning approach powering their development. To overcome this, we contend that to achieve reliable deductive reasoning in learning-based AI systems, researchers must fundamentally shift from optimizing for statistical performance against distributions on reasoning problems and algorithmic tasks to embracing the more ambitious exact learning paradigm, which demands correctness on all inputs. We argue that exact learning is both essential and possible, and that this ambitious objective should guide algorithm design.
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