从生成式AI走向可信AI：大语言模型能从Cyc学到什么
Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyc
AI总结：
本文指出生成式AI因推理不足而不可信，提出16项未来AI期望特性，并以Cyc系统为例说明如何在高阶逻辑推理中兼顾表达力与实时性，最终主张可信通用AI需将LLM与形式化符号方法混合。
摘要：
生成式AI是当前最流行的AI方法，由大语言模型（LLMs）组成，这些模型被训练来生成看似合理但不一定正确的输出。尽管它们的能力常常令人惊叹，但它们在推理方面存在不足，导致LLMs并不完全可信。此外，它们的结果往往既不可预测又不可解释。我们列出了未来AI的16项期望特性，并讨论了一种替代性的AI方法，该方法理论上可以解决当前方法相关的许多局限性：用精心策划的显式知识和经验规则片段来教育AI，使推理引擎能够自动推导出所有这些知识的逻辑蕴含。即使以这种方式产生的长论证也可以是可信且可解释的，因为完整的逐步推理链条始终可用，并且每一步所使用的知识的来源都可以被记录和审计。然而这里有一个难题：如果逻辑语言的表达力足够强，能够完全表示我们用英语所能表达的任何含义，那么推理引擎的运行速度就会太慢。这就是为什么符号AI系统通常退而求其次，采用某种快速但表达力弱得多的逻辑，例如知识图谱。我们描述了AI系统Cyc如何发展出克服这一权衡的方法，并能够实时进行高阶逻辑推理。我们建议，任何可信的通用AI都需要将LLM方法和更形式化的方法进行混合，并勾勒出一条实现这一梦想的路径。
英文摘要：
Generative AI, the most popular current approach to AI, consists of large language models (LLMs) that are trained to produce outputs that are plausible, but not necessarily correct. Although their abilities are often uncanny, they are lacking in aspects of reasoning, leading LLMs to be less than completely trustworthy. Furthermore, their results tend to be both unpredictable and uninterpretable. We lay out 16 desiderata for future AI, and discuss an alternative approach to AI which could theoretically address many of the limitations associated with current approaches: AI educated with curated pieces of explicit knowledge and rules of thumb, enabling an inference engine to automatically deduce the logical entailments of all that knowledge. Even long arguments produced this way can be both trustworthy and interpretable, since the full step-by-step line of reasoning is always available, and for each step the provenance of the knowledge used can be documented and audited. There is however a catch: if the logical language is expressive enough to fully represent the meaning of anything we can say in English, then the inference engine runs much too slowly. That's why symbolic AI systems typically settle for some fast but much less expressive logic, such as knowledge graphs. We describe how one AI system, Cyc, has developed ways to overcome that tradeoff and is able to reason in higher order logic in real time. We suggest that any trustworthy general AI will need to hybridize the approaches, the LLM approach and more formal approach, and lay out a path to realizing that dream.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
