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Researchers from the University of Rochester Medical Center surveyed nearly 1,000 mostly college-aged students on their perception of cool. The three-year study found that more people believe a person is cool when they are friendly, warm, smart and trendy. Today people are less apt to respond to the James Dean-style of aloof coolness that was once so dreamy in yesteryears.

The researchers acknowledged that the study population is limited in scope, since it consisted of "mostly educated, young, Canadian, ethnically white and Asian, [and] predominantly female" in British Columbia, Canada. This is important to note because the idea of "cool" does not necessarily translate between different types of people, experts said.

The leather jacketed, cigarette smoking, rebel to authority is still perceived as somewhat cool, researchers said, but the nice guy next door now reigns coolest, according to the study surveys. Those who are talented and smart and striving to succeed also rated high up on the cool scale. Even nerdiness, which was once the antithesis of "cool," has changed its reputation. Geeks are now "under that counterculture umbrella of edginess" Dar-Nimrod said.

50 percent of social network users share or repost news stories, images, or videos, while 46 percent discuss news or current events on their networks, and 11 percent have submitted their own content to news websites or blogs. Pew reiterated its findings from a report earlier this month that Internet users who arrive at the 26 news websites it analyzed by directly typing in those sites’ URLs or via bookmarks spend far more time on those sites, view more pages, and return more times per month that Internet users who arrive via Facebook.

78 percent of Facebook users see news while they are on the social network for other reasons. Only 34 percent of Facebook news consumers like news organizations or individual journalists, which Pew interprets to mean that most of the news they see on the social network is shared by their friends. Facebook news consumers reported seeing entertainment news the most, followed by “people and events in my community,” sports, national government/politics, crime, health/medicine, and local government/politics. News consumers on LinkedIn were high earners and college-educated, while those from Twitter were younger than those from Facebook, Google Plus, and LinkedIn.

One-half of Facebook users get news there even though they did not go there looking for it. And the Facebook users who get news at the highest rates are 18- to-29-year-olds.

Two years ago, the two greatest Jeopardy champions of all time got obliterated by a computer called Watson. It was a great victory for artificial intelligence--the system racked up more than three times the earnings of its next meat-brained competitor. For IBM’s Watson, the successor to Deep Blue, which famously defeated chess champion Gary Kasparov, becoming a Jeopardy champion was a modest proof of concept. The big challenge for Watson, and the goal for IBM, is to adapt the core question-answering technology to more significant domains, like health care. WatsonPaths, IBM’s medical-domain offshoot announced last month, is able to derive medical diagnoses from a description of symptoms. From this chain of evidence, it’s able to present an interactive visualization to doctors, who can interrogate the data, further question the evidence, and better understand the situation. It’s an essential feedback loop used by diagnosticians to help decide which information is extraneous and which is essential, thus making it possible to home in on a most-likely diagnosis. WatsonPaths scours millions of unstructured texts, like medical textbooks, dictionaries, and clinical guidelines, to develop a set of ranked hypotheses. The doctors’ feedback is added back into the brute-force information retrieval capabilities to help further train the system.

For Watson, ingesting all 2.5 million unstructured documents is the easy part. For this, it would extract references to real-world entities, like corporations and people, and start looking for relationships between them, essentially building up context around each entity. This could be connected out to open-entity databases like Freebase, to provide even more context. A journalist might orient the system’s “attention” by indicating which politicians or tax-dodging tycoons might be of most interest. Other texts, like relevant legal codes in the target jurisdiction or news reports mentioning the entities of interest, could also be ingested and parsed. Watson would then draw on its domain-adapted logic to generate evidence, like “IF corporation A is associated with offshore tax-free account B, AND the owner of corporation A is married to an executive of corporation C, THEN add a tiny bit of inference of tax evasion by corporation C.” There would be many of these types of rules, perhaps hundreds, and probably written by the journalists themselves to help the system identify meaningful and newsworthy relationships. Other rules might be garnered from common sense reasoning databases, like MIT’s ConceptNet. At the end of the day (or probably just a few seconds later), Watson would spit out 100 leads for reporters to follow. The first step would be to peer behind those leads to see the relevant evidence, rate its accuracy, and further train the algorithm. Sure, those follow-ups might still take months, but it wouldn’t be hard to beat the 15 months the ICIJ took in its investigation.