Even the best electrical engineers and IoT practitioners might not be able to figure out the AI component of IoT artificial intelligence without some guidance. IoT practitioners and data scientists who want to build IoT-based AI don't have to work it out on their own. In fact, they must often partner with other experts or else they risk missing-critical factors to ensure their project succeeds. In Artificial Intelligence for IoT Cookbook, author and senior staff enterprise architect Michael Roshak discusses techniques with detailed instructions to build AI for IoT deployments and resolve common problems. After establishing the basic set up for IoT and AI, Roshak digs into advanced skills such as computer vision and natural language processing.

Microearthquakes -- low-intensity earthquakes that register 2.0 or less magnitude on the moment magnitude scale -- rarely cause property damage. And as a result of background noise, small events, and false positives, they're not always picked up by seismic monitoring systems. A possible solution is described in a new paper from the Department of Geophysics at Stanford University, where scientists have developed an AI system -- dubbed Cnn-Rnn Earthquake Detector, or CRED -- that can isolate and identify a range of seismic signals from historical and continuous data. It builds on the work of Harvard and Google, which in August created an AI model capable of predicting the location of aftershocks up to one year after a major earthquake. The researchers' system consists of neural network layers -- interconnected processing nodes that loosely mimic the function of neurons in the brain -- of two types: convolutional neural networks and recurrent neural networks.

Oklahoma never used to be known for its earthquakes. Before 2009, the state had roughly two quakes of magnitude three and above each year. In 2015, this tally rocketed to more than 900, though it's calmed since, falling to 304 last year.