If we want a future where AI serves a plurality of interests, then we should pay attention to the factors that drive its success. While others have studied the importance of data, hardware, and models in directing the trajectory of AI, I argue that open source software is a neglected factor shaping AI as a discipline. I start with the observation that almost all AI research and applications are built on machine learning open source software (MLOSS). This thesis presents four contributions. First, it quantifies the outsized impact of MLOSS by using Github contributions data. By contrasting the costs of MLOSS and its economic benefits, I find that the average dollar of MLOSS investment corresponds to at least $100 of global economic value created, corresponding to $30B of economic value created this year. Second, I leverage interviews with AI researchers and developers to develop a causal model of the effect of open sourcing on economic value. I argue that open sourcing creates value through three primary mechanisms: standardization of MLOSS tools, increased experimentation in AI research, and creation of commuities. Third, I analyze the various incentives behind MLOSS by examining three key factors: business strategy, sociotechnical factors, and ideological motivations. In the last section, I explore how MLOSS may help us understand the future of AI and make a number of probabilistic predictions. I intend this thesis to be useful for technologists and academics who want to analyze and critique AI, and policymakers who want to better understand and regulate AI systems.

No abstract

No abstract

Progress in artificial intelligence has led to growing concern about the capabilities of AI-powered surveillance systems. This data brief uses bibliometric analysis to chart recent trends in visual surveillance research — what share of overall computer vision research it comprises, which countries are leading the way, and how things have varied over time.

U.S. policymakers need to understand the landscape of artificial intelligence talent and investment as AI becomes increasingly important to national and economic security. This knowledge is critical as leaders develop new alliances and work to curb China’s growing influence. As an initial effort, an earlier CSET report, “AI Hubs in the United States,” examined the domestic AI ecosystem by mapping where U.S. AI talent is produced, where it is concentrated, and where AI private equity funding goes. Given the global nature of the AI ecosystem and the importance of international talent flows, this paper looks for the centers of AI talent and investment in regions and countries that are key U.S. partners: Europe and the CANZUK countries (Canada, Australia, New Zealand, and the United Kingdom).