Artificial Intelligence Experience

After two and a half years of working on getting his master’s degree in artificial intelligence, Jack Gage is experienced in different artificial intelligence development skills. The sections below list his academic experience in computer vision and reinforcement learning, including documents that demonstrate his abilities. Also included are annotated bibliographies written by Jack detailing ethics in artificial intelligence.

Computer Vision

  • The two documents below demonstrate how an image can be digitally processed and preprocessed through the use of image modifying techniques and the image processing library OpenCV. The work performed in the first document features the altering of the Lena image, performing actions such as resizing, cropping, color manipulation, and binarization. The second document focuses on an image of a human blood vessel and making the image clearer through image augmentations, histogram stretching, CLAHE, RGB normalization, and brightness and contrast enhancement.

  • This document demonstrates image processing through using the geometry within the images. Through boundary extraction, closing, region filling, and connected components, morphological processing can help create an image that is easier to understand.

  • This document demonstrates Haar cascade classifying using blood vessel analysis and license plate reading. The blood vessels are dilated, closed, and have connected components identified. The license plate reading process uses erosion, edge detection, contour detection, and masking to determine the license plate number.

  • This document demonstrates the use of YOLO (You Only Look Once) where filter boxes are used to identify specific objects in an image and a video.

Reinforcement Learning

  • The document demonstrates reinforcement learning through the use of deep Q-network (DQN) agents by displaying how the agent an be used for both the Mountain Cart and Lunar Lander environments. The agent receives rewards and penalties for its actions as a way to teach the agent to make the most desirable decisions.

  • This document demonstrates establishing a breakout environment and training a proximal policy optimization agent to how to behave within the environment.

Ethics Papers