Implement machine learning features

[oduyemi]

A machine learning feature to analyze map/address data patterns.

[cbeddow]

Let's add a summary of proposed methods here

I will suggest: can we make the output of conversion have a "alternative_outputs" property that contains anything an LLM proposes

[oduyemi]

• Data Validation and Correction Suggestions: To use an LLM to learn common patterns in the Overture and OSM data. The model can flag inconsistencies, missing fields, or anomalies during the conversion process. The model could suggest potential fixes or autofill missing data based on learned patterns. For instance, if an address or coordinate is missing or malformed, the LLM can suggest probable matches or corrections.
• Semantic Tagging: To use an LLM to analyze the context and content of Overture place data to improve the mapping of tags to OSM formats. So, the model can make smarter tagging decisions based on nuanced differences in descriptions. Based on existing conversion data, the LLM can propose additional features or improvements to mappings that better align with OSM standards.
• Address Parsing and Formatting: To implement an LLM model that takes raw, unstructured address data and formats it according to OSM standards. It may include suggesting corrections for misspelled or incomplete addresses.
• User Assistance: To integrate the LLM to provide real-time suggestions or explanations as users input data. And maybe, add some automated FAQs and support by using the model to generate context-aware answers to user questions and assist in troubleshooting common issues.
• To provide OSM conversion tips based on past data conversions, and recommend best practices for tagging or mapping certain types of data to OSM.
• To make the output of conversion have a "alternative_outputs" property that contains anything an LLM proposes

[oduyemi]

I made a switch from Tensorflow to brain.js because of the challenges I had running it.
Now, I'm working on the data validation/correction suggestion task