So far in this series on Artificial Intelligence < Artificial Intelligence: Monster or Mentor? and AI for Summarization – Enabling Human-Consumable Information >, we looked at several key ways in which AI advances can improve human productivity in organizations. Last time’s article dove into Distillation – automating the path to value. In this article, we’ll look at the next common approach: Categorization.
Categorization is applying AI approaches to automate the labeling and organization of large data volumes, so that data can be routed, processed, and interpreted in the right way. Imagine an enormous coin sorter that takes dump truck loads of coins (mixture of currencies across the globe) and produces nicely sorted buckets of quarters, nickels, etc. for each currency. This is a poster-child example of categorization – the categories are well understood (we know up front all the possible “buckets” that coins can land in), and the sorter sorts accurately into categories. In many real-life applications, and especially when we are categorizing large volumes of data, we aren’t this lucky. We (a) might not know what the “buckets” should be, and we (b) often make mistakes in categorization.
View the original article here.