Minority Situation Activity and Paper (10 points). Spend 2–3 hours in a situation in which you are the minority (i.e., dissimilar from everyone else in the situation). This should not….
Also known as “query-by-example,” dynamic sound recognition recently found commercial success as a means to identify music through short audio snippets, captured through a microphone.
Also known as “query-by-example,” dynamic sound recognition recently found commercial success as a means to identify music through short audio snippets, captured through a microphone. First-generation algorithms recognized unique signatures in a particular sound, which they could then match with a most likely source or an equivalent sound stored in a large database of previously identified auditory signatures. Early mobile apps employing these algorithms were amusing and effectively enabled music listeners to identify a song’s title and the performing artist. One Los Angeles-based research and development company determined that the underlying technologies might have further, public-minded implications as well, and began exploring new uses for sound recognition algorithms. The most promising output of this research was a mobile app, dubbed Epimetheus. Epimetheus was particularly proficient at recognizing music, advertisements and human voices. Unlike previous apps using dynamic sound identification, Epimetheus was also adept at picking up subtle auditory signals and sorting through environmental noise in order to accurately identify natural phenomena, such as the changing tides. This functionality was meant to benefit scientific researchers who could employ Epimetheus as a tool to track ecological change in remote locations. It also proved popular among students and casual hobbyists who enjoyed the app’s educative and informative capabilities. In addition to identifying sounds with a high degree of accuracy, Epimetheus incorporated a machine learning algorithm that adapts to new inputs and provides users with useful information about the sounds being processed. For example, the app might identify personal information about those speaking, links to websites selling a product being advertised on television, encyclopedic entries about bird calls in the wild and other relevant resources. It wasn’t long before the titans of Silicon Valley recognized Epimetheus’ commercial and scientific potential and started bidding to acquire the underlying software. At that point, the research team behind Epimetheus began preparing demos that leveraged the strengths of its sound classification engine. For example, engineers developed an entertaining demo that was able to identify with high accuracy the voice actors/ actresses for cartoon characters. It even worked in cases where the cartoon characters were voiced by actors/actresses of the opposite sex (e.g. Bart Simpson is voiced by female voice actress Nancy Cartwright). One company, Cronus Corp., was especially impressed by these demos, and was eager to acquire Epimetheus and incorporate its sensing technology, databases and information provisions into its own products. However, before negotiations could proceed, Cronus Corp.’s lawyers asked the research team behind Epimetheus to prove that they had minimized the risk of unexpected harmful results. Programming an algorithm that is sensitive to societal norms and cultural flux is notoriously difficult, and Cronus Corp. did not want to unwittingly produce a bad outcome or acquire a public relations scandal.