How to become a God, in 3 simple steps (absent faith/belief):

[ProgrammingGodJordan]

Conclusion: Mathilda is God-bound, however less-likely so, in comparison to researchers that utilize Deep Neural Networks.

POST SCRIPTUM:
Neural Networks are dynamic, error minimization systems, though it is observable that you express of a separate class of dynamic construct.

I mean these type of dynamical systems, which neural networks can also implement.

https://en.wikipedia.org/wiki/Dynamical_system

I have a few questions though.

• After I publish my paper on dynamical systems, I also plan on performing the same experiments with neural networks. Will this promote me back to a being a god rather than a demi-god?
  
• These won't be the same neural networks used in deep learning systems, they'll be biologically plausible neural networks more similar to what we see happening in the brain. Will this make me more of less god like?
  
• If using more biologically plausible neural networks make me more god-like, then does this mean that computational neuroscientists are even more god-like than AI programmers?
  
• Does this mean that researchers in symbolic GOFAI and computational linguists are now our bitches? (please say yes)
  
• Just to clarify, you did mean God-bound didn't you and not egg-bound, as in constipated from eating too many eggs?
  
• I thought demi-gods were made when a God appears in the form of a swan and seduces your mother. Does this still work? And does it stack with writing papers on dynamical systems?
  

('A')

Simply: 

Mankind ( AI programmers/researchers... ) are BECOMING Gods.

_definition_: AI researchers but include computational neuroscientists. (AI Researcher: Any entity that researches, such that non trivial intelligence is hypothesized/encoded) 
See quote via ('B').

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('B')

I had long expressed of said plausible systems, particularly, in an earlier response betwixt thine, via http://atheistforums.org/thread-46062-page-22.html.

The paths via machine learning faces:


'...A' Hardware replication. [From  C. elegans worm neuronal replication, to Henry Markram's mammalian brain constructs]

'...B' Theory-bound compactions of neuronal cognition [Deep Neural Networks-aligned models, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory, Deep Reinforcement Learning that utilizes Deep Neural Nets...]

Therein:

[0] - '...A' is but entirely non-human surpassing. [approximates worms, partial mammalian brain as observed in '...A']

and

[1] - '...B' has already produced models that exceed human performance, in cognitive, non trivial tasks.[Although the human brain oscillates abound higher efficiencies entirely, brain based models excel in individual tasks/task groups as observed in '...B'.]

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('C')

Thusly, deep learning has but generated human performance exceeding models, whilst non-deep learning [strictly biological-plausible aligned] models have not.
However, non-deep learning models enhance as time diverges.

You are thereafter God-bound, on the horizon of _definition_ via ('A'), and the outcome/result sequences of the paths/methodologies stipulated; whence non-deep learning [strictly biological-plausible aligned] models relent that of the task proficiency computation par deep learning models.