Machine Learning

Machine Learning vs non-Machine Learning algorithm

Recently, I was interacting with some non-technical folks and noticed that people use the term ML even when an algorithm doesn’t actually use any Machine Learning. So here is the simplest definition (that I know of) which explains the difference between Machine Learning and non-Machine Learning algorithms: Machine learning algorithms improve when you provide them with more data. Non-machine learning algorithms don’t. Digit recognizer A famous and somewhat standard ML beginner project is the handwritten digit recognition project. As it turns out, this simple task is a really good way to understand the difference between ML and non-ML algorithms. There are a few reasons. Simple Input The input to a Digit recognition algorithm is very simple. It is a scanned…

Machine Learning

How much can Machine Learning ACTUALLY help with answering free-form questions?

At the moment, not much. I am contributing to a Kaggle competition which is trying to use ML techniques to answer freeform questions from medical literature.¬†At first, the technical folks were running helter-skelter trying to come up with useful visualizations (but not coming up with much). Until one day an epidemiologist showed up, and started talking about visualizations in the form of evidence gap maps, and people realized that getting useful data from this dataset wasn’t going to be such an easy task with the existing ML tools we have at our disposal. That seemed to have prompted a thread on what would actually make competition entries useful. Someone on the forum wrote a great comment under that same thread…