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Dropbox ended up being fastest 56% of the time. Even more importantly, it was slowest only 4% of the time.

Dropbox and Amazon appear to be the most reliable solutions with only occasional delays. Google isn't far behind, and I can't imagine that Microsoft won't work hard to improve Skydrive - the company's subscription model depends on it.

For the study 353 volunteers were asked to submit adjectives they associated with coolness. Surprisingly, the word "friendly" topped the list, followed by "personal competence."

This ranking positioned socially skilled, popular, smart, and talented people as being the ultimate in cool; individualist hipsters featured lower on the list. Bar-Ilan concluded: "Coolness has lost so much of its historical origins and meaning." That is: rebels are not hot. Or cool.

Another attribute figured prominently in this recent study: physical attractiveness. The prominence of "good looks" in the study echoes the results of work I carried out for my most recent book, Brandwashed. During my $3 million study into the way word-of-mouth works, I asked a family of five to secretly promote brands to a cadre of their friends, family members and colleagues. During this experiment, I learned that the key to the family’s success was neither their extensive network, nor their gift of the gab; it was their good looks.

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.

shift toward what is now known as contextual computing

Amazon’s and Netflix’s recommendation engines, while not magnificently intuitive, feed you book and video recommendations based on your behavior and ratings. Facebook’s and Twitter’s valuations are premised on the notion that they can leverage knowledge of your acquaintances and interests to push out relevant content and market to you in more effective ways.

four data graphs essential to the rise of contextual computing: social, interest, behavior, and personal.

advertising technology called remarketing has proven alienating to online consumers. Remarketing, which lets advertisers follow someone around the Internet with a display ad, based on a previous search engine query, specific site visit, or other online action by the user, has increased in popularity in recent years.

The rapid spread of SnapChat--the picture sharing app that auto-deletes photos after ten seconds--shows that young people increasingly understand the need to keep some things secret, or at least to control the visibility and content of their communications. The migration of Millennials away from Facebook to the more anonymous Tumblr may be another sign. And the outcry raised by young Tumblr users in the wake of news that Yahoo! was purchasing the platform--driven by fears of more corporate control and increased advertising--only underscores the point.

Millennials are in the vanguard of mainstream online behavior: they were first on Facebook (after college students invited to the join in its earliest days), followed by their parents. A Millennial move towards greater online secrecy could represent the beginning of a larger shift that warrants additional research.

the specialist era is waning. The future may belong to the generalist.

there appears to be reasonable and robust data suggesting that generalists are better at navigating uncertainty.

Professor Phillip Tetlock conducted a 20+ year study of 284 professional forecasters. He asked them to predict the probability of various occurrences both within and outside of their areas of expertise. Analysis of the 80,000+ forecasts found that experts are less accurate predictors than non-experts in their area of expertise.