continuing linguistics learning from now:

LING 1330/2330 Computational Linguistics (pitt.edu)

yayy goal now is to fastly study to all courseware in this course to have much better initial understanding of the linguistics methodologies/algorithms. 

then following task would be continueing the study of TB domain knowledge to get to know that PTB2 syntax. then try analyze that PTB 2 extractor neural network. 

hmm. 

then I need to devise the paragraphs dependency graphs in terms of linkages between paragraphs. 

and a covering RDF definition set to translate such interpreted language to. 

hmm lots of tasks but 3 days holidays. i guess i wish i iterate alot and reach a parse tree /treebank parser to translate it to such topological ontological language /knowledge graph (interpretation). 

hmm today would most possibly pass with linguistics domain knowledge study.  then later topology studies revisiting/remembering topology basic information (not phd but bsc topology knowledge)

tomorrow would be passed with defining the RDF with an RDF tool and then starting writing the translator with ANTLR, which would convert parse trees outputted by utilized neural network to the topological RDF based representations. this 3 days task would be try to finalize this topological initial very simple toplogical language. then write the antlr translator. knowing that this topological language could be also changed. it would start with very basical topological language. but it would be like written that the antlr would be written to modules that separate topological language definitions would be easily written new antlr modules of. then after this translator is ready. next task would be writing a topological inference maker with again using antlr to create such query language again a basic version initially. 

hmm at the same time i would start translating wikidata to such topological definition wise ontology knowledge graphs as db. 

then after all these studies and iterations of these inference engine/ query syntaxes/topology RDF definitions, the 0.1 ai with static intelligence would be ready then next tasks would be in 0.2 would be adding more maths RDF topological RDF structures like e.g. differential equations or differential geometry or abstract algebra or analytical topology or measure theoryor so.  then 0.3 would be like more adding more inference capabilities. etc etc.  

 so i think most interesting tasks would be at 0.2 version's studies where more maths topologies query inference languages would be defined.

current task sets are not very challanging but rather unoriginal uncreative to be finished tasks (I mean 0.1 versions' tasks are just software engineering and not alot more) but 0.2's studies would be like adding more inference capabilities based on mroe advanced topology languages. so that would become nthe more ingenius studies. but 0.1 is rather all unoriginal/uncreative usual softwatre engineering based study to define some encyclopedic (static) intelligence type. 

so 0.1 version studies are rather full hard work of software engineering but nothign of some very creative task. 

but surely 0.1 version should be readied asap to be able to start 0.2 version which would be holding the creativity/ingenuity aspects in terms of tasks of the software project. 

so 0.1 version is full boring hard work:)  just usual software engineering. to create some software architectures or query languages etc etc. just usual boring software work. 

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