"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased data is difficult to spot as the programming field is homogeneous and this issue reflects underlying societal biases. Facial recognition programs do not identify minorities at the same rate as their Caucasian counterparts leading to false positives in identifications and an increase of run-ins with the law. AI does not have the ability to role-reverse judge as a human does and therefore its use should be limited until a more equitable program is developed and thoroughly tested.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Americans today face an age of information overload. With the evolution of Media 3.0, the internet, and the rise of Media 3.5—i.e., social media—relatively new communication technologies present pressing challenges for the First Amendment in American society. Twentieth century law defined freedom of expression, but in an information-limited world. By contrast, the twenty-first century is seeing the emergence of a world that is overloaded with information, largely shaped by an “unintentional press”—social media. Americans today rely on just a small concentration of private technology powerhouses exercising both economic and social influence over American society. This raises questions about censorship, access, and misinformation. While the First Amendment protects speech from government censorship only, First Amendment ideology is largely ingrained across American culture, including on social media. Technological advances arguably have made entry into the marketplace of ideas—a fundamental First Amendment doctrine—more accessible, but also more problematic for the average American, increasing his/her potential exposure to misinformation. <br/><br/>This thesis uses political and judicial frameworks to evaluate modern misinformation trends, social media platforms and current misinformation efforts, against the background of two misinformation accelerants in 2020, the COVID-19 pandemic and U.S. presidential election. Throughout history, times of hardship and intense fear have contributed to the shaping of First Amendment jurisprudence. Thus, this thesis looks at how fear can intensify the spread of misinformation and influence free speech values. Extensive research was conducted to provide the historical context behind relevant modern literature. This thesis then concludes with three solutions to misinformation that are supported by critical American free speech theory.

This research covers the landscape of influencer marketing and combines it with the knowledge of 11 content creators and one social media specialist, ultimately producing an actionable handbook. Participants were asked questions that were intended to discover key strategies, level of difficulty, and overall insight into the content creator world. Best practices and key findings are identified in the research paper, and outlined into four parts in the handbook. The handbook serves as a compilation framework derived from my primary and secondary sources designed to provide anyone interested in becoming a content creator or social media influencer on steps they may take given what their predecessors have done to successfully launch their careers in the space.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.