Incorporating machine learning provides objective standards that help mitigate subjectivity in emergence identification.

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• The NIS and NIS+ frameworks provide effective solutions for causal emergence identification from data.claim0.769
  Central claim of the machine-learning section, summarizing the contribution.
• Conceptual advances in the links between machine learning and evolution now provide quantitative formalisms with which to begin to develop testable models of collective intelligence across scales.claim0.763
  Asserts that the time is ripe for formal models.
• Classification of Emergence: Nominal, Weak, Strongconcept0.763
  Bedau's tripartite classification: nominal (pattern), weak (computationally irreducible), strong (irreducible downward causation).
• Causal emergence identification tasks can be understood as causal representation learning tasks.claim0.761
  Authors propose a conceptual mapping between CE identification and CRL.
• We take our principal contributions in this report to be: 1. Showing that the assessment of consciousness in AI is scientifically tractable ... 2. Proposing a rubric for assessing consciousness in AI ... 3. Providing initial evidence that many of the indicator properties can be implemented in AI systems using current techniques ...quote0.760
  Summary of contributions.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model, a process known as self-evidencing.claim0.757
  Foundational claim of the paper, defining self-evidencing.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.753
  Opening sentence defining self-evidencing.
• Unsupervised learning builds a low-dimensional model of the input data.claim0.753
  Clarifies what unsupervised learning does.