If you’ve made it this far! Well done! Here’s where you’ll be able to put your knowledge to the test. Below is a report from the Canaria Technologies data science team about the first heat exhaustion incident captured in the case study from the last article.
It’s always much easier to understand something with an example, so now we’re going to use a case study from Canaria Technologies for heat exhaustion prediction. In this case study, a client conducted a heat exhaustion pilot for 6 weeks: this case study covers the first part of a two-part pilot. It explains the process of installing and testing a predictive biometric system using the Canaria Puck on a workforce who has expressed recurring heat exhaustion problems in a site in Northern Australia.
A predictive biometric system combines data about how the human body functions with AI to predict future body functions. It is an emerging field of science that has not yet hit the mainstream. Like the term AI itself, the term predictive biometric system covers applications of varying levels of complexity across many different fields of study.
Deep learning, in turn, is a sub-category of ML. Specifically, it is a class of machine learning algorithm. It is one of the most complex forms of computational information processing as all deep learning systems mimic the way neurons in the human brain help to process complex information. Almost all deep learning applications are based on artificial neural networks. There are many different types of neural networks. Two of the most common that are brought up in conversation are Convolutional Neural Networks (CNNs) and Adversarial Neural Networks (ANNs).
Last time we covered the definition of AI. You’ll probably have heard the terms AI and ML being used interchangeably in the mainstream press or at events, but they are actually quite different topics.
In this series we provide a comprehensive, plain-English introduction to AI. All too often we find people at technology conferences either nodding along trying to pretend they understand what AI is; or giving such incomprehensible pseudo-engineering presentations that even fellow experts in AI can’t follow along.