Modernist Success in a Postmodern Failure: Jackson Pollock and Abstract Expressionism, the Avant-Garde and the Ascension of Late Capitalism, Art after 1945

It is hard to imagine the magnitude of the events at the end of World War II. The thought produced in the face of a myriad of deaths is almost unfeasible sixty years after the fact, but the energy was integral to the changing social landscape. Because of the country's prominence in and fortitude after the war, the U.S. was left responsible for reshaping and rejuvenating the international landscape that was destroyed by the years of brutal fighting and vile contestation. The American establishment was granted a major opportunity to establish itself amongst the global leaders. Such a grand responsibility must account for the multiplicity of thought that arises in such a decisive moment. In order to align the Abstract Expressionist art movement with the intersection of the intense, multifaceted thought developed during the postwar period, the following will discuss the political, philosophical, economic, and art historical overlap that occurred in the mid to late 1940s in the hopes of illustrating the fertility yet lingering problems associated with the restructuring of the world with America at the helm. In this way, the duration of the Abstract Expressionist moment will be better understood for both its triumphs and downfalls.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.