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Over the eight months since its release, ChatGPT and its underlying model,GPT3.5, have garnered massive attention, due to their potent mix of capabilityand accessibility. While a niche-industry of papers have emerged examining thescope of capabilities these models possess, the information fed to andextracted from these networks has been either natural language text orstylized, code-like language. Drawing inspiration from the prowess we expect atruly human-level intelligent agent to have across multiple signal modalities,in this work we examine GPT3.5's aptitude for visual tasks, where the inputsfeature content provided as ASCII-art without overt distillation into a lingualsummary. We conduct experiments analyzing the model's performance on imagerecognition tasks after various transforms typical in visual settings, trialsinvestigating knowledge of image parts, and tasks covering image generation.

Testing the Depth of ChatGPT's Comprehension via Cross-Modal Tasks Based  on ASCII-Art: GPT3.5's Abilities in Regard to Recognizing and Generating  ASCII-Art Are Not Totally Lacking