给AI是否具备意识提供一套概率评估框架，初步判断大模型无明确意识证据。
Initial results of the Digital Consciousness Model
- 融合多种意识理论，构建可量化评估AI意识的统一框架。
- 2024年大模型缺乏确凿意识证据，但结论尚不具有决定性。
- 比简单AI系统更弱的意识证据，说明当前模型仍远未达意识门槛。
人工智能系统已变得极为复杂，能对话、写文章，并在语境理解上表现出令人意外的能力，这引发了一个关键问题：我们是否正在创造具有意识的系统？数字意识模型（DCM）是首次尝试以系统化、概率化方式评估AI系统中意识的证据。该模型提供了一个共享框架，用于比较不同人工智能与生物有机体的意识证据，并追踪随着人工智能发展而变化的证据。不同于采纳单一意识理论，它整合了多种主流理论与视角，承认专家对意识本质及其必要条件存在根本分歧。本报告描述了数字意识模型的结构及其初步结果。总体而言，我们发现2024年大语言模型缺乏意识证据，但该证据并不具有决定性。相比之下，对于2024年大语言模型的意识否定证据，远弱于对更简单人工智能系统的否定证据。
原文摘要 · Abstract (English)
Artificially intelligent systems have become remarkably sophisticated. They hold conversations, write essays, and seem to understand context in ways that surprise even their creators. This raises a crucial question: Are we creating systems that are conscious? The Digital Consciousness Model (DCM) is a first attempt to assess the evidence for consciousness in AI systems in a systematic, probabilistic way. It provides a shared framework for comparing different AIs and biological organisms, and for tracking how the evidence changes over time as AI develops. Instead of adopting a single theory of consciousness, it incorporates a range of leading theories and perspectives - acknowledging that experts disagree fundamentally about what consciousness is and what conditions are necessary for it. This report describes the structure and initial results of the Digital Consciousness Model. Overall, we find that the evidence is against 2024 LLMs being conscious, but the evidence against 2024 LLMs being conscious is not decisive. The evidence against LLM consciousness is much weaker than the evidence against consciousness in simpler AI systems.
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