Which of the indicator properties listed in Table 1 are displayed by existing AI systems, including frontier generative language or multimodal models, language agents, and deep reinforcement learning agents?

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Many of the indicator properties can be implemented in AI systems using current techniques.claim0.832
  Feasibility demonstrated in Section 3.1.
• There are no obvious technical barriers to building AI systems which satisfy the indicator properties.claim0.808
  Feasibility claim about near-term conscious AI.
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.803
  Graded claim about the rubric.
• Indicator selection must avoid the minimal implementation problem while remaining open to varied forms of consciousness, including exotic AI forms.claim0.794
  Second guideline for deriving indicators; balance between demanding and permissive formulations
• Building AI systems with more indicator properties will increase the likelihood of consciousness.hypothesis0.780
  Guiding hypothesis of the rubric.
• Indicators used to assess AI systems for consciousness should focus on theories' central explanatory posits, abstracting away from implementation details that may differ across systems.claim0.771
  First of four guidelines for deriving indicators; prevents over-restriction to human-specific features
• What is the functional distinction between simple language models and multicellular organisms, and can generative AI harness that property to achieve long-range order?question0.769
  Core motivating question; drives investigation of topological differences between biological and artificial systems
• Park et al. 2023: Generative agents — interactive simulacra of human behaviorconcept0.769
  Paper on LLM-based simulacra of human behaviour; cited as ref 3