OK. I'm serious.

Would you write this?

1) A physical simulator that is a compromise between performance and accuracy. A set of standard physical objects and an articulated creature with sensors and actuators capable of interaction with said physical simulation. Sensors and actuators are read/controlled via 2 float arrays.
2) A virtual machine with short, concise bytecode with the property that any bytestring is a correct program. A fast JIT (maybe LLVM) implementation of this VM. This VM should be able to host any arbitrary program that controls creature from 1) via actuators using data from sensors.
3) A modern genetic algorithm capable of running on multicores and clusters (probably natural evolution strategies or CoSyne).
4) A set of benchmarks set in 1) simulator that are designed to require intelligent behavior on behalf of creature controller software to solve them. Think about puzzles, navigation, labyrinths, simple games, pattern recognition. A set of benchmarks uses 1) simulator with creature controlled by arbitrary 2) VM software and outputs a fitness number, higher is better.

Then I'd like you to run 3) genetic algorithms on a sufficiently large cluster (perhaps sized in 100k+ cores and based on AWS, GCP or Azure) to breed 2) arbitrary VM software against 4) benchmark until it shows signs of general intelligence. Then email me, I'll inform you on our next steps.

You called yourself a god? Do your work then (^:

Also you could do all the above but also study deepmind's, openai's and FAIR's papers and bias your evolution to known good AI algorithms.