The Perception of American Adolescent Culture Through the Dubbing and Fansubbing of a Selection of US Teen Series from 1990 to 2013

The present dissertation aims at analyzing the construction of American adolescent culture through teen-targeted television series and the shift in perception that occurs as a consequence of the translation process. In light of the recent changes in television production and consumption modes, largely caused by new technologies, this project explores the evolution of Italian audiences, focusing on fansubbing (freely distributed amateur subtitles made by fans for fan consumption) and social viewing (the re-aggregation of television consumption based on social networks and dedicated platforms, rather than on physical presence). These phenomena are symptoms of a sort of ‘viewership 2.0’ and of a new type of active viewing, which calls for a revision of traditional AVT strategies. Using a framework that combines television studies, new media studies, and fandom studies with an approach to AVT based on Descriptive Translation Studies (Toury 1995), this dissertation analyzes the non-Anglophone audience’s growing need to participation in the global dialogue and appropriation process based on US scheduling and informed by the new paradigm of convergence culture, transmedia storytelling, and affective economics (Jenkins 2006 and 2007), as well as the constraints intrinsic to multimodal translation and the different types of linguistic and cultural adaptation performed through dubbing (which tends to be more domesticating; Venuti 1995) and fansubbing (typically more foreignizing). The study analyzes a selection of episodes from six of the most popular teen television series between 1990 and 2013, which has been divided into three ages based on the different modes of television consumption: top-down, pre-Internet consumption (Beverly Hills, 90210, 1990 – 2000), emergence of audience participation (Buffy the Vampire Slayer, 1997 – 2003; Dawson’s Creek, 1998 – 2003), age of convergence and Viewership 2.0 (Gossip Girl, 2007 – 2012; Glee, 2009 – present; The Big Bang Theory, 2007 - present).

On the Democratization of AI: Bridging the Gap with AutoML

Riding the wave of recent groundbreaking achievements, artificial intelligence (AI) is currently the buzzword on everybody’s lips and, allowing algorithms to learn from historical data, Machine Learning (ML) emerged as its pinnacle. The multitude of algorithms, each with unique strengths and weaknesses, highlights the absence of a universal solution and poses a challenging optimization problem. In response, automated machine learning (AutoML) navigates vast search spaces within minimal time constraints. By lowering entry barriers, AutoML emerged as promising the democratization of AI, yet facing some challenges. In data-centric AI, the discipline of systematically engineering data used to build an AI system, the challenge of configuring data pipelines is rather simple. We devise a methodology for building effective data pre-processing pipelines in supervised learning as well as a data-centric AutoML solution for unsupervised learning. In human-centric AI, many current AutoML tools were not built around the user but rather around algorithmic ideas, raising ethical and social bias concerns. We contribute by deploying AutoML tools aiming at complementing, instead of replacing, human intelligence. In particular, we provide solutions for single-objective and multi-objective optimization and showcase the challenges and potential of novel interfaces featuring large language models. Finally, there are application areas that rely on numerical simulators, often related to earth observations, they tend to be particularly high-impact and address important challenges such as climate change and crop life cycles. We commit to coupling these physical simulators with (Auto)ML solutions towards a physics-aware AI. Specifically, in precision farming, we design a smart irrigation platform that: allows real-time monitoring of soil moisture, predicts future moisture values, and estimates water demand to schedule the irrigation.