Tunnel vision in computer vision: can ChatGPT see?

ChatGPT, OpenAI's latest generative pretrained transformer model, has quickly captured public interest by demonstrating its capability to generate human-like text and handle a wide range of tasks, including composing poetry, writing essays, and generating code. Built on an updated version of GPT-3, ChatGPT's conversational fine-tuning allows it to create coherent and contextually relevant responses, making it particularly useful for various applications in computer vision (CV). While ChatGPT excels at generating commonplace code and integrating method calls from popular CV libraries, it struggles with niche tasks, complex mathematical manipulations, and expansive code blocks, often leading to errors or suboptimal solutions. Despite these limitations, ChatGPT can empower CV engineers by efficiently handling boilerplate code, thus allowing them to focus on more critical tasks like dataset curation and model evaluation. However, users must exercise caution when interpreting its responses, especially for specialized tasks, as the model's responses can vary due to its inherent randomness.