Artificial General Intelligence - The Feathers Problem

Just a simple thought: The Feathers Problem

One of the challenges in the invention of powered flight was deciding what matters most.  If one wants to imitate birds there are many factors:

  • Wing curvature
  • Wing warping or twist
  • Flapping
  • Feathers
  • Jumping into flight from the ground
  • Energy source
and many more.

Some early pioneers of flight proposed feather covered wings.  After all, that is what birds have.  Flapping was tried also.
In the end the Wright Brothers figured out that the salient factors were lift, drag, thrust, and weight. The lift was generated by wing curvature, not the fact there were feathers. So the first working airplane (and first gliders that worked) did not have feathers. The other solutions they came up with were light weight engine, wing warping (flaps came later) for control, no flapping but instead propellers, linear sliding take off rather than a jump, solved the learning cure issue with learning to fly a craft without any craft working yet.

In AGI there are many factors in the brain that may or may not be important.

  • Spiking Neurons
  • Quantum effects
  • Redundancy of neurons
  • Spiking models such as refractory periods, charge-spike-and-reset etc.
  • Precise inter-spike timing
  • Weight adjustment and learning
  • Feedback
  • Goals
  • Rules
  • Expert Systems
and about a zillion more concepts.

Which are important to create a thinking machine?

I see many articles that focus on one or several ideas as "the thing" to solve AGI.
I bet once someone makes an AGI the actual solution will be as different from a biological brain as an airplane is from a bird, and as similar.

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