PhD in natural language processing, with years spent building NLP applications end to end. Moe works on text analysis: lexical and syntactic structure, and what separates machine-generated prose from human prose statistically. He has been experimenting with computational linguistics since the early days of NLTK, spaCy and WordNet, and still writes most of his tooling in Python.
Japanese academic writing runs on a single consistent register, and most AI-generated drafts break it within a paragraph. This post walks through the specific grammatical tells, how Japanese universities and detectors actually check for them, and what a Japanese AI humanizer needs to preserve along the way.
Most Hindi academic writing in India is actually produced in English, but Hindi-medium scholarship still faces its own AI-detection risks. This guide breaks down the calques and stiff openers that give away ChatGPT-drafted Hindi, what Indian and Nepali universities currently say about generative AI, and how a humanizer needs to preserve citations, terms, and register.
Thirty minutes, five phases, one rule: fix the parts worth the most marks while the attention is still fresh, and save commas for last.
Perplexity gets the attention, but the arithmetic underneath it is token probability: what the model's distribution over the next word actually contains, why detection math runs in log space instead of raw probability, and how a string of per-token scores becomes one number.
Presentation is what students polish the night before a deadline. A published marking rubric says it is usually the narrowest of the categories that decide a grade. Here is where the weight actually sits, and why.
GPTZero made perplexity and burstiness famous, then quietly retired both in autumn 2023 for a deep-learning classifier. This post traces what changed, what GPTZero and Turnitin actually document today, and why the two statistics still explain why machine text is detectable even though neither vendor computes them directly anymore.