Week 3: Netflix and Jeopardy!

The article on Netflix was particularly interesting because it put to practice the extreme level of sophistication and specificity Netflix incorporated into its genre-ization algorithm to create specialized profiles for each Netflix user. Alotting for a variety of combinations of periods, genres, and even actors, Netflix attempts to precisely categorize every film ever made, even if that category has just one film. A set of generated genres attributed to each viewer, Netflix not only has the capacity to describe what each viewer watches, but ultimately predict it. Case in-point is Netflix’s educated purchase of “House of Cards”, a show that lined up perfectly with Netflix’s average profiles. However, as the article points out, “the data can’t tell them how to make a TV show, but it can tell them what they should be making.” The automation of something as emotional and complex as movie-making/movie-watching seems to have been reduced and perhaps even mocked by Netflix’s algorithm. Yet, the opposite is true. “House of Cards”, and Netflix for that matter, are not successful because the human capacity to enjoy films can be trivialized to an algorithm but because the preferences related to enjoyment can be more accurately communicated via metadata. Thus, the creation of the media in response to these results remains an essential, anthropological product.

Watson: i.kinja-img.com/gawker-media/image/upload/s–Gbchunvr–/18mhcmpj5aul1png.png

As an avid fan of “Jeopardy!”, this article reminded me of IBM’s Watson. A supercomputer put up against “Jeopardy!’s” greatest champions, Watson easily won the contest with its huge data storage and processing capacities, as well as precise “buzzing” within a millisecond to first “question” the answer given. Its speed and accuracy improving with every clue, Watson also had a “learning” algorithm to remember combinations of answers that proved evident throughout the contest. Remarkably obvious, however, was Watson’s inability to “pattern” human thought and speech. Things like puns and jokes in clues went unregistered by Watson, and was unable to perceive answers to these trickier clues. Thankfully, Watson’s capacities were created with the medical field in mind, even though its stunning calculation abilities are hotly contested and marginalized by professionals in the medical community who worry about the economic and moral ramifications of automating medical practice (“The Robot Will See You Now”). Indeed, Watson is far from capable of understanding human gray areas like fear and morals to present proper diagnoses. However, perhaps medicine as nothing to fear – just as Netflix needs viewers and filmakers, surely medicine requires both human patients and doctors.

Netflix: “www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/”

Watson: “www.theatlantic.com/magazine/archive/2013/03/the-robot-will-see-you-now/309216/”