This semester, I wrote two interesting papers. Here's a quick summary of both.
"Thinking FastAR" for Human-Machine Symbiosis.: When faced with a decision, it is often difficult for people to choose the best option for their long term well-being. Augmented reality can enable users to see more than what is physically present. Here, I propose and demonstrate an augmented reality system that helps users make healthier choices. Using object recognition as a heuristic for decision recognition, the system guides the user toward objects that align with a user’s personalized health goals. The current implementation of this system involves predefined objects with hard coded health goals. For future work, recent advances in object recognition appear promising for a more universal version of the system, which will provide more flexibility and customization for users.
"Escaping the Local Minimum", for Integrative Theories of Mind and Cognition: Artificial Intelligence performs gradient descent. The AI field discovers a path of success, and then travels that path until progress stops (when a local minimum is reached). Then, the field resets and chooses a new path, thus repeating the process. If this trend continues, AI should soon reach a local minimum, causing the next AI winter. However, recent methods provide an opportunity to escape the local minimum. To continue recent success, it is necessary to compare the current progress to all prior progress in AI.