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My feed today showed this paper published by Buehrer and colleagues (2017) with respect to a mathematical framework for superintelligent machines. 

Leaving the sophisticated title aside, their paper is about a class calculus expressive enough for self-learning optimization (it is capable of describing and improving its learning process). As specified in the paper:

_"It can design and debug programs that satisfy given input/output constraints, base d on its ontology of previously learned programs.  It can improve its own model of the world by checking the actual results of the actions of its robotic activators."_ [[source](https://arxiv.org/ftp/arxiv/papers/1804/1804.03301.pdf)]

Some potential applications and examples to which this abstract concept can be used for are (as they suggest):

- check the black-box of a crashed car to determine the reason of the crash:

_"...if  it  was  probably  caused  by  electric  failure,  a  stuck  electronic  gate,  dark  ice,  or  some  other condition  that  it  must  add  to  its  ontology  in  order  to  meet  its  sub-goal  of  preventing  such  crashes  in  the future."_ [[source](https://arxiv.org/ftp/arxiv/papers/1804/1804.03301.pdf)]

Now, to nudge you and to provide some sort of interest for reading the paper and maybe understanding some of its implications is that the paper is a description of something called 'The Master Algorithm' - by machine learning researcher Pedro Dominguez. Now you're interested?
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<center>[[Super-Intelligent Machines - A Mathematical Approach](https://arxiv.org/ftp/arxiv/papers/1804/1804.03301.pdf)]</center>
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[Cristi Vlad](http://cristivlad.com) Self-Experimenter and Author