Why #nlp is all about machine learning stuff ?!
The real wonder behind #google is the large, wide, and almost universal scale it operates. In terms of number of humans (and number of machines) it is a wonderful achievement.
Did you look at #openai GPT-2 results? Not the numbers, but the actual generated "natural language" sentences. They look ugly. I do think that it can be a good instrument for black hats to generate fakes after some human post-processing and curation.
There is room for improvement.
When I do natural language processing stuff, whether it is #wikidata or #conceptnet or #python spacy. I always feel undermined by the size of my screen monitor. Otherwise said, It will help #science to have (much?) bigger screen resolution.
I did not try 4K screens but 2K is not enough.
Also, tilling window manager does not help :/
Released my (very (tiny (small))) database server for conceptnet at https://groups.google.com/forum/#!topic/conceptnet-users/Jn1IARVbp-E
I have been working almost all day on the functional database, I was talking previously named #datae. Unlike what is written in the README. I think, that my future self will not commit to build a #CKAN competitor, instead I will enjoy the full shenanigan of #nlp and try to build something that requires less frontend work.