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What physics offers for artificial intelligence? Lessons from the brain's inner time and its dynamics.

Georg Northoff1, Yasir Catal1, Samira Abbasi2

  • 1University of Ottawa Institute of Mental Health Research , Ottawa, Ontario, Canada.

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Physics offers computing insights into time and dynamics. The brain uses intrinsic neural dynamics and inner time to actively process information and participate in the world, unlike current AI.

Keywords:
artificial intelligence (AI)being in the worldbraindynamicscale-free dynamicsspontaneous activityvariability

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Area of Science:

  • Neuroscience and Computational Theory
  • Physics of Time and Dynamics
  • Artificial Intelligence

Background:

  • Physics provides a framework for understanding time and dynamics, crucial for activity patterns.
  • The brain exhibits intrinsic neural dynamics and spontaneous activity, enabling dynamic input processing.
  • Current computing paradigms, including AI, lack inherent temporal dynamics and 'inner time'.

Purpose of the Study:

  • To explore how computing can leverage physics' concept of time and dynamics.
  • To investigate the brain's use of intrinsic neural dynamics for processing and participating in physical time.
  • To highlight the limitations of current computing and AI in engaging with real-world temporal dynamics.

Main Methods:

  • Analysis of physics principles related to time and dynamics.
  • Review of empirical evidence on brain's scale-free activity and variability as 'inner time'.
  • Comparison of biological neural processing with classical and non-von Neumann computing architectures.

Main Results:

  • The brain utilizes scale-free activity and variability ('inner time') to actively track and encode input dynamics.
  • Neural activity, through entrainment, aligns with external rhythms, enabling participation in physical time.
  • Current computing devices lack spontaneous activity and 'inner time', preventing active engagement with dynamic environments.

Conclusions:

  • The brain's 'inner time' and dynamic repertoire are essential for active information processing and real-world participation.
  • AI and current computing are 'locked out of time and world' due to their passive processing.
  • Bridging this gap requires incorporating principles of dynamics and intrinsic temporal activity into future computing architectures.