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Area of Science:

  • Neuroscience
  • Genetics
  • Artificial Intelligence
  • Biophysics

Background:

  • Biological systems, including animal brains and genetics, have evolved with objectives mirroring artificial intelligence goals, such as maximizing likelihood or minimizing cross-entropy.
  • Predator-prey dynamics drove brain evolution, necessitating complex computations for survival, akin to graphics processing unit (GPU) operations.
  • Living systems utilize molecules, information, and energy (ATP) to maintain low entropy, a concept illuminated by Maxwell's demon model.

Purpose of the Study:

  • To highlight the underappreciated role of energy in biological systems and medicine.
  • To propose that energy and entropy are frontiers in biology and medicine.
  • To identify energy-related diseases and introduce a novel therapeutic approach.

Main Methods:

  • Comparative analysis of evolutionary pressures on animal brains and goals in artificial intelligence.
  • Application of Maxwell's demon model to understand entropy in living systems.
  • Identification of potential energy-related diseases based on genetic and molecular data.

Main Results:

  • A functional similarity exists between biological systems (genes, brain) and artificial intelligence (deep learning) in optimizing objectives.
  • Over 10% of human genes are involved in energy metabolism, yet energy's role in disease is understudied.
  • Neurodegenerative diseases, muscle disorders, cardiomyopathy, and diabetes are proposed as potential energy-shortage diseases.

Conclusions:

  • Future medical and biological research frontiers will likely focus on energy and entropy.
  • Developing treatments like ATP enhancers is crucial for energy-related diseases.
  • Understanding cognitive processes through evolutionary survival strategies will advance scientific discovery.