In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.

Universal Basic Income is a proposed policy where the government would regularly pay all citizens in cash. The idea of a Universal Basic Income (UBI) has had a resurgence in recent years because of popular figures like Andrew Yang and Elon Musk, but its history and potential implications go deep into the structure of human society. This thesis delves into how a basic income would transform social concepts of work and disrupt the personal economic model. With the bargaining power and freedom granted by a basic income, workers would find themselves in a position of work freedom and choice that has never existed in human history. With new freedom to do as they wish, the place of work in people’s lives needs to be reimagined as a source of fulfillment instead of an unlikeable but necessary part of everyday life. Workers will be given the choice to leave unfair or unfulfilling work and decide for themselves how they want to contribute within society. From increasing mental and economic well-being for most Americans to serving as a response to unemployment trends in the automated future, to encouraging greater business innovation, there are myriad ways in which basic incomes have the potential to benefit society. Framed by Martin Luther King Jr. and Franklin Delano Roosevelt as the only policy capable of abolishing poverty forever, Universal Basic income will be an important feature of transformative innovative policy advocacy until it is adopted by a major world government at which point the effects in practice will become clear.