How Calm is using machine learning to keep us all mellow
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(Image sourced via Calm)
With a mission is to make the world happier and healthier by helping people prioritise mental health and claiming to be the world s most popular sleep, meditation and relaxation app Calm exults in being what the Centre for Humane Technology dubs the world s happiest app .
Designed to help users manage stress, sleep better and live a happier, healthier life, the app is available in more than 190 countries. But for all the apparent talk of calm science, there s a lot of deep IT and data-driven marketing behind the way the privately held, nine-year-old company gets such high satisfaction rates for its meditation app.
The pandemic economy exposed our legacy planning approaches. But what to do about it? Planful has a continuous planning vision, but is it realistic? Planful's CEO shares what they've learned about planning maturity.
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For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here s a practical guide to how Bayes Nets can solve enterprise problems.
In part one of this series, we covered some basic probability theory principles - and compared Machine Learning approaches to Bayesian Belief Nets (Can Bayesian Networks provide answers when Machine Learning comes up short?). In this article, we ll dig a little deeper into Bayesian Belief Networks and how they can be applied to complex decisions.
Understanding Bayesian Inference
In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, very few have any experience implementing Judea Pearl s Bayesian Belief Networks:
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Rolls-Royce is using cloud-based technologies to help its customers in the aviation industry avoid unplanned grounded planes, with real-time data from hundreds of engine sensors.
(Image by Alexei Chizhov from Pixabay )
Rolls-Royce powers 35 different types of commercial aircraft and has over 13,000 engines in service around the world. It s a household name in the aviation industry and in 2020 the company brought in over £20 billion in revenues. It s a company that also prides itself on its technological innovation, where it is making huge strides in the fields of data analytics and AI.
This week we got the chance to speak to Rolls-Royce s Chief Information Officer, Stuart Hughes, about one of the company s recent ventures in this field, where it is collecting real-time engine data from its customers to model performance in the cloud, with the aim of reducing unnecessary maintenance and unplanned time on the ground for planes.