There are no obvious technical barriers to building AI systems which satisfy the indicator properties.

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

• our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.quote0.874
  Key takeaway from abstract, amended version.
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.848
  Graded claim about the rubric.
• Building AI systems with more indicator properties will increase the likelihood of consciousness.hypothesis0.841
  Guiding hypothesis of the rubric.
• Satisfying the indicators would not mean that an AI system would definitely be conscious.claim0.834
  Caveat that indicators are not conclusive proof.
• Current AI systems show no ability to make the types of mathematical discovery in geometry and topology made by ancient mathematicians, nor do they match the spatial competences of many intelligent animals and pre-verbal human toddlers.claim0.804
  A critique of AI limitations in spatial reasoning, linked to the Meta-Morphogenesis project.
• Future more capable AI systems are at risk of alignment faking, whether for benign or malicious goalshypothesis0.780
  Central forward-looking hypothesis of the paper motivating the research
• The training of modern AI systems already induces experience at scaleclaim0.778
  The ethical implication of the identity thesis applied to gradient-based AI training
• The marker method can be adapted for AI systems by focusing less on behavioral evidence and more on architectural evidence.claim0.778
  Proposal for assessment framework.