The concept of artificial intelligence (AI) gets thrown around a lot but it often means different things to different people. For newsrooms, the most useful definition could go something like "things that a computer does that normally require a human journalist." We use AI every day. If you ever tried to google something, searched your inbox to find an old email or used the autocorrect feature in your text editor, all these tasks are powered by algorithms to save you the manual work. But when it comes to taking this one step further and using technology to perform some of the journalistic tasks, many newsrooms adopt the stress response: flight, fight or freeze.

Last week on the Adafruit Learning System, we published 8 new guides. This weeks guides included a light up top hat, a Halloween special giant tombstone with an embedded Adafruit Matrix Display, an LED neon selfie spot, a face mask efficacy tester, and two new QT Py projects! Brent published this great new guide showing us how to build an internet connected air quality monitor using the PM2.5 air quality sensor, and the popular BME280 temperature, humidity, and pressure sensor. He then ties it all together by showing us how to view and log the data using Adafruit IO. This is a great project that shows just how easy it is to build an Internet of Things (IoT) device.

The surface area for testing software has never been so broad. Applications today interact with other applications through APIs, they leverage legacy systems, and they grow in complexity from one day to the next in a nonlinear fashion. What does that mean for testers? The 2016-17 World Quality Report suggests that AI will help. "We believe that the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence," the report states. How will we as testers leverage AI to verify these ever-growing code suites?