Can humans and artificial intelligences share concepts and communicate?
'Making AI Intelligible' shows that philosophical work on the metaphysics of
meaning can help answer these questions. Herman Cappelen and Josh Dever use the
externalist tradition in philosophy to create models of how AIs and humans can
understand each other. In doing so, they illustrate ways in which that
philosophical tradition can be improved.
The questions addressed in the book are not only theoretically interesting,
but the answers have pressing practical implications. Many important decisions
about human life are now influenced by AI. In giving that power to AI, we
presuppose that AIs can track features of the world that we care about (for
example, creditworthiness, recidivism, cancer, and combatants). If AIs can
share our concepts, that will go some way towards justifying this reliance on
AI. This ground-breaking study offers insight into how to take some first steps
towards achieving Interpretable AI.

人类和人工智能是否能共享概念并进行交流？《使人工智能变得可理解》展示了意义形而上学的哲学研究如何回答这些问题，Cappelen 和 Dever 利用哲学中的外在主义传统构建了人工智能和人类相互理解的模型，从而改进了哲学传统，给出了有关理论和实践的重要答案，为实现可解释的人工智能迈出了重要的第一步。

使人工智能可理解：哲学基础

Making AI Intelligible: Philosophical Foundations

Artificial General Intelligence falls short when communicating role specific
nuances to other systems. This is more pronounced when building autonomous LLM
agents capable and designed to communicate with each other for real world
problem solving. Humans can communicate context and domain specific nuances
along with knowledge, and that has led to refinement of skills. In this work we
propose and evaluate a novel method that leads to knowledge distillation among
LLM agents leading to realtime human role play preserving unique contexts
without relying on any stored data or pretraining. We also evaluate how our
system performs better in simulated real world tasks compared to state of the
art.

提出和评估一种新颖的方法，在不依赖任何存储数据或预训练的情况下，在 LLM 代理之间进行知识蒸馏，以实时保留人类角色扮演中的独特语境，并评估我们的系统在模拟的真实世界任务中表现更好。