Modern machines, including quantum computers and machine learning systems, achieve reliable function through uncertain events, making unpredictability a feature rather than a defect.

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• "It is certainly very hard, and perhaps impossible, for mere humans to anticipate and rule out in advance all the disastrous ways the machine could choose to achieve a specified objective."quote0.797
  Russell's statement opening Section 2 articulating the core motivation for the Contemplative AI approach
• 21st-century machines are increasingly unpredictable and noisy.claim0.785
  Quantum computers, ML systems, and evolved machines exploit uncertainty rather than requiring determinism.
• We hypothesize that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that successful implementation can be established via analysis or testing.hypothesis0.784
  The central hypothesis of the paper
• The segmentation clock exhibits functional robustness consistent with James' definition of intelligence.claim0.779
  Claims that the re-emergence and adaptation of the segmentation clock qualifies as intelligent behavior.
• Physicality is not a requirement for a machine; a machine learning algorithm that generates other machine learning algorithms or designs robots qualifies as a machine.claim0.776
  Proposed definitional update removing physicality as a necessary condition for machines
• 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.claim0.775
  Asserts that the time is ripe for formal models.
• Future models with substantially increased capabilities will exhibit alignment faking that is more consistent, robust, and harder to detecthypothesis0.774
  Extrapolation from scale-emergence finding to future risk
• Modern machines are increasingly resistant to reductionist analysis and explanation.claim0.771
  Deep neural networks, swarms, and complex autonomous systems require holistic ethological and cognitive approaches.