Time

The below is an evolving thought process on how to deal with information aging out in machine learning models.

Information aging out for learning (human and machine) requires thinking about the problem space from two different angles: 1.) Embeddings 2.) Weights / Models

Of which there are three scenarios: 1.) Explicitly dated information 2.) Implicity dated information 3.) Undated information

Of sources across a different set of vectors: a.) Mostly Trusted b.) Untrusted

from multiple sources including, but not limited to:

1. Books (our oldest form of information) - Permanent form of information
2. Articles (news, blogs, journals) - Semipermanent form of information
3. Social Media (the most ephemeral form of information)

In addition, we need to consider weather the model or human is aiming for general or deep knowledge of the topic at hand.

Deep knowledge may have less stickiness over time, while general knowledge may be more resilient to time decay.

So where to go from here?

1. Step 1 - Call a problem that I don't think others are looking at yet
2. Step 2 - Do nothign for six months
3. Step 3 - Wait for some other person to solve it
4. Step 4 - Profit!!!