In today's hyper-connected world, where health apps are proliferating at an unprecedented rate, a pressing question emerges: Can users manipulate these systems to achieve desired outcomes? This isn't merely about bending the rules—it’s about preserving the integrity and effectiveness of tools designed to enhance our well-being amidst societal pressures and personal challenges.

Imagine that you are in a plane, thinking the flight is going smoothly but in reality both pilots in the cockpit have fallen asleep. Does it sound like a Hollywood scenario? Sadly enough it happens in real life with the most recent example being when an Ethiopian Air flight lost contact with ground control and overflew its runway landing destination.

As a startup founder or employee, it’s easy to get lost in the excitement of creating something new and revolutionary. But amidst all the buzz and busyness, mental health is often overlooked. The entrepreneurial path is not an easy one, and the burden of creating a successful business can sometimes take a toll on one’s well-being. In this post, we’ll explore the dangers of mental health problems in startups and suggest ways to mitigate them.

Anomaly detection is a field within machine learning that has been thoroughly explored, with methods from k-nearest neighbor to isolation forests to clustering. But what if we want to limit our anomaly detection to statistical methods only? Furthermore, what if our data doesn’t follow parametric distributions?

From the dawn of time, humans have tried to understand each other by looking at their faces and listening to their voices. It has led to the development of the practice of Physiognomy, which means assessing a person’s character or personality from their outer appearance—especially the face. While the concept is great, the biggest peril is when someone tries to use this assessment to pass judgment on a person.