AI & Machine Learning

Artificial intelligence (AI) is attracting increasing attention from manufacturers. Here are some of the popular misconceptions about this technology.

This blog will review some of the best practices for generating high-quality, up-to-date documentation directly from your EKG’s metadata.

This post describes a well-known and classical algorithm for clustering known as K-means clustering. It also coveres an advanced variant called soft k-means clustering and a robust variant that uses median instead of mean.

Discussing applications of ML in theory is much different than actually applying ML models at scale in production. In this article, we walk through common challenges and corresponding solutions to making ML a force multiplier for your data organization.

We are barely starting to talk about the ethical foundations of ML, and as a result our society is going to have to pay the price for our slow action.

As more business decisions are influenced by AI, it opens numerous questions and requirements for greater oversight, transparency and ethics in how those decisions are made leading to rising demand for explainable AI.

AI Product Owners need to know about the potentials and pitfalls of AI-based applications.

In the age of digital transformation, every business is under pressure to apply predictive analytics and other emerging technologies and deploy solutions in the most productive and streamlined ways.

Think of Plug n’ Play Predictive Analysis as your secret weapon, one that every team member can utilize to your advantage.

AI journalists have shown tremendous possibility in clearing away much of the field’s hard labour: collecting data, transcribing recordings, writing fewer interesting articles, etc.

This post will be useful to readers who’d like to understand how simple RNNs work, how an enhanced version with a forgetting mechanism works, GRU in particular, and how the latter improves upon the former.