Machine Learning in Practice

The best tools are born out of user necessity. AI Auditing was no different.

As the desire to scale machine learning programs grows, so does the frustration of the executives who attempt it.

Putting machine learning to practical business use is hard. Here’s hoping that these tips help make your journey a little easier.

Deep learning has been the sexiest term in machine learning. For business leaders accustomed to taking a “deep dive” on an issue, or…

Given the attention machine learning has received and the sophisticated problems it is solving, it may seem like magic. But it is not…

Good managers are empathetic to their employees, and good machine learning managers understand the perspective of data scientists…

Machine learning projects ultimately come down to delivering and implementing computer code. It’s no surprise, then, that the management…

Machine learning is a technical process, but it starts and ends with people. The first step to structuring your machine learning project…

While I believe the future for humans is generally sunny, some workers face rainy days ahead.

Grab a friend and try this quick experiment. Think of a simple song that almost everyone knows, like “Mary Had a Little Lamb.” Then using…

Historically, technology has destroyed some jobs (how many of your friends are candlestick makers?) while creating new ones (how many of…

ethics: the discipline dealing with what is good and bad and with moral duty and obligation

There are roles within each enterprise that consist largely of boring repetitive mental tasks better suited to software. In a sense, these…

You don’t just need project ideas. You need the means to carry them out. So let’s get to the final round: process questions.

If you’re using machine learning to do a task that’s currently performed by people, you’re going to face a version of the “Jobs” question…

This is the second in our three-part series laying out questions you should ask before you start a machine learning project.

We live in a time of transition to a new, AI-driven era.

Ask These Questions First

There’s a Chinese proverb that applies to machine learning in business: “The best time to plant a tree was 20 years ago. The second-best…

Think Task Automation, Not Job Automation

Reduce costs. Increase efficiency. Achieve breakthroughs.

The “New Electricity” Needs A Lot of New Wiring

Seven key lessons for executives we’ve learned at Infinia ML over the past two years as we’ve implemented a few dozen ML projects.

The author (left) with other young alumni of St. Lawrence University. Photo credit to St. Lawrence University’s Career Center.