Aside

Fast Broadband in the USA: Population Density Matters

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The New York Times Bits blog has a post about how a Japanese company can upgrade their broadband service to 160 megabit/second broadband for $20 a home. Saul Hensell contrasts this with Verizon, who is spending $817 per home. Sadly, Mr. Hensell’s attempt to explain this difference falls short of believability. Instead of “less competition”, “no demand”, and “fear of losing TV revenue” a better explanation is “population density”. Mr. Hensell explains that the super-fast modem in Japan costs $60, up from $30; which leaves us wondering where the $20-upgrade fee comes from. One explanation is that it’s the average price per home – in Tokyo, where J:COM is based. But the population density in Tokyo is 14,410 people per sq mile; compared to 2,181 in New York City, and 409 in New York State. The infrastructure costs per home will therefore be considerably less in Japan.

Hume and Critical Realism

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It is astonishing how much freedom I feel after a rejection of Hume-an Constant Conjunctions; the twin ideas that the limitations in perception in turn limit the knowledge one can acquire to that of observing “constant conjunctions”, and that the weaknesses of induction prevent one from locating real “laws”. Hence the absence of “laws” in science, in favor of “laws which haven’t been disproved yet”. Critical Realism allows a rejection of Hume-an Constant Conjunctions on an ontological basis. Such a rejection re-defines science, from the task of locating constant conjunctions to the task of locating reality’s ontology and subsequently creating theories to explain how they operate. While the result is that I approach science in largely the same way I did beforehand, I know have a considerably firmer philosophical base from which to operate – and one which serves to explain how science both does and should operate. Hume, and his legacy of Positivism, does no such thing.

Tools for Research

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It seems that there is a large opportunity to apply modern technologies to academic articles. Take a data sources such as arXiv.org, and apply text analytics and quantitative algorithms to relate and rank articles. In academia, word choice tends to be more precise than ordinary speech, so entity extraction should have a higher success rate. Furthermore, format is mandated, which both makes entity extraction easier and allows interesting statistics to emerge. For instance, in-text citations allows one to find out how many times an article is referenced (superior to simple the bibliography), what text the article is cited to support, and so on. One could use that to create a document map of the article; who is cited where? Given the demarcation into sections in journal articles, simply knowing who’s most often cited in introductions for a particular field is useful. Furthermore, the time-based nature of academic progress provides a number of interesting analyses for research. In short, the avenues for interesting research are almost endless – you have vast quantities of data, in a more than unstructured format, with the potential upside of both making research dramatically easier and potentially building a machine knowledgebase. Pity there’s not enough money in it.