This text explores the capabilities and limitations of current machine translation and image captioning systems. These systems,
largely based on encoder-decoder neural networks, achieve impressive
results in translating languages and generating image descriptions, but their performance is inconsistent, often failing to capture nuances and context. The evaluation methods used to assess these systems—like BLEU scores and human ratings—are flawed and can be misleading. Ultimately,
while these technologies are improving and useful, they lack genuine
understanding and remain fundamentally unreliable without human
oversight.
