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a prudent client—the kind you really want—will choose a web partner on the strength of the entire package. Doing otherwise, selecting on a matrix of numbers, dates, and line items, is an exercise in foolishness, akin to buying a car without knowing the make and model. Relationships, even in business, are founded on and strengthened by mutual compatibility. Proposing to work together on a project is remarkably similar to proposing marriage (despite the obvious and important differences): it ought to be a decision based on both emotion and reason, supported by a high degree of trust. In writing a proposal, you are making the case for the appropriateness of your new life together. These early, hesitant steps toward knowing one another better are crucial.

At some point later in the document, include the contact information for a few of your best clients. References reassure the reader that you are who you claim to be. You might also wish to provide a brief overview of some previous projects similar to the project on which you are bidding. Business is as much (if not more) about people as it is about dollars and cents, deliverables and timelines. If your company is difficult to relate to because your proposal is generic, it shouldn’t surprise you when you are inevitably forgotten.

Meticulously evaluate every client before issuing a proposal. Once you do, don’t panic if they walk because you stuck to your philosophical guns. You just saved yourself bucket-loads of stress and misery.

Recent research suggests that emotions hold the secret to viral web content. Articles, posts, or videos that evoke positive emotions have greater viral potential than something that evokes negative feelings, but both do a better job recruiting clicks than neutral content. The finer details tell a similar story: triggering high-arousal emotions, such as anger or humor, is a surer path to click gold than triggering low-arousal ones, such as contentment or sadness.

positive emotions best negative ones, any emotion bests none at all.

content evoking high-arousal emotions (in this case, awe, anger, and anxiety, emotions that tend to whip us into action) went viral more often than articles evoking a low-arousal emotion (sadness, an emotion that often leaves us subdued).

The National Newspaper Association reported print advertising has dropped 60% over the past seven years. And magazine print advertising has not fared much better, dropping 38% in the same period of time.

Remarketing is a form of behavioral targeting that allows advertisers to serve messages to people who have previously visited a particular website. A snippet of code is placed on a webpage or set of pages and when a person visits the page, they are cookied. A cookie acts like a tracking tag and enables the ad to “follow” individuals around the web.

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.