> 1. Start by training a machine learning model to identify features of natural language. You can use supervised learning techniques with a corpus of labeled data consisting of texts written by both humans and AI systems.
> 2. Use natural language processing techniques to extract features from the text such as word frequencies, grammar, and syntax.
> 3. Implement a scoring system to assign a numerical score to each text based on the features identified by the model.
> 4. Create a threshold score which will be used to differentiate between texts written by humans and AI systems.
> 5. Test the model on a separate dataset to ensure accuracy.
> 6. Deploy the model in a production environment to detect AI-written texts.
This is from the prompt: "How would you build a tool to detect texts written by artificial intelligence systems?"
It seems like an okay plan, but doesn't offer any actual insight into differences between human text and AI text.
For example, a frequent feature I've observed (of course, I'm not alone) is that it tends to generate lists of things and tie them together with "Firstly, item1. Additionally, item2. More so, item3. In conclusion, item4".