Artificial Intelligence, International Competition, and the Balance of Power (May 2018)

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.LBJ School of Public Affair

Artificial Intelligence and National Security

As technology advances at an exponential rate, it is becoming increasingly important to consider the ramifications of that technology in the geopolitical environment, and especially as it pertains to American national security. One of the most important categories of technological innovation that will likely disrupt the global balance of geopolitical power, especially along the US-China axis, is the advent and growing sophistication of artificial intelligence. In order to address the new and evolving national security challenges that will accompany this disruption, this paper seeks to define and explain the disparity in artificial intelligence capabilities between the United States and China. First, it will describe the contemporary situation regarding the AI capabilities of both China and the United States, as well the implications of those capabilities as they relate to American national security interests. Additionally, this paper will identify the major contributing factors that are driving and/or mitigating artificial intelligence development in each country. Moreover, this paper will explain the discrepancies found to exist between the two countries in terms of the discrepancies found between their contributing and mitigating factors. Lastly, this paper will discuss the possible implications of these findings for the national security of the United States

2035 AND U.S. NAVY INTELLIGENCE: COMMUNITY MANNING FOR SUCCESS IN THE INDO-PACIFIC

This thesis seeks to understand the best method for employing the Naval intelligence community in 2035 and beyond. Naval intelligence manning has remained largely unchanged since the end of the Cold War. As the United States adapts to a new geopolitical paradigm involving peer military forces and the rapid technological advances, the Naval intelligence community must adapt to ensure U.S. success in all phases of conflict. This thesis sets the stage for a future geopolitical scenario defined by multipolarity, confrontation with China, and the rise of artificial intelligence and remote technologies. This thesis examines the problem of strategic warning to enable deterrence, effective team building to optimize information flow, and the effectiveness of tactical intelligence in the modern and future naval battlefield. Ultimately, this thesis argues the Naval intelligence community should expand its support to tactical warfighting units to ensure sustained U.S. naval dominance.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

The Role of AI in Cervical Cancer Screening

In the last few years internet-based technologies played an important role in reinventing various medical procedures and facilitating quick access to medical services and care, particularly in the remote areas of China. The use of artificial intelligence and cloud computing in clinical laboratory setting for slide analysis contributed to standardized cytology and pathology diagnosis but more importantly slide analysis with artificial intelligence has a huge potential to compensate for a country wide lack of pathologists and systematic quality control. While well-established automated slide scanning is already in use, we added intelligent algorithms located in a secure cloud for the better slide readings, and mobile phone microscopes to capture those regions of Hubei province where laboratory infrastructure is supported by high-speed internet and 5G networks. These technological advances allowed us to bring an important pathology expertise across the large areas of China

The Faculty Notebook, September 2016

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost

Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

ZOOpt: Toolbox for Derivative-Free Optimization

Recent advances of derivative-free optimization allow efficient approximating the global optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (https://github.com/eyounx/ZOOpt) toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and a light-weighted distributed version with the help of the Julia language for Python described functions. ZOOpt toolbox particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox is being maintained toward ready-to-use tool in real-world machine learning tasks