Recommended Viewing : Microsoft IoT, Azure IoT, Time Series Insights, Remote Monitoring

All of our devices are sending mammoth amounts of telemetry (= data) and a lot of that data is useful. With the emergence the “Internet of Things” (as both a concept and a reality) over the past few years Microsoft has jumped at this new opportunity. Numerous efforts they are engaged in are around Azure IoT Core, certified device catalog, providing cloud/intelligence to the edge, remote monitoring, Azure Iot Hub Device Provisioning, time series insights and quite a bit more.  This is the tip of the iceberg – there are a number of other related features around, for example, machine learning.

One of the more interesting introductory talks to what Microsoft has introduced is from Sam George, Director of Microsoft Azure IoT.  It is a long talk but covers a lot of territory :
One very interesting aspect of the work being done is Time Series Insights which allows you to stream put your time series data into this tool and understand what is going on with your monitored device.  This is a particularly interesting application. There is no schema or telling the tool what the format of your data is, it will figure it out.  This talk provides an overview of Azure IoT Time Series Insights.



Highly Recommended Viewing: A Rapid Overview of Machine Learning, Deep Water meta-Framework and 16 GPU EC2 Instances

The first talk hits the mark on providing a rapid-fire quick history and update on machine learning and also provides an introduction to Amazon’s 16 GPU Amazon EC2 instance to train or act as the hardware foundation for the new world of machine language frameworks.

From the Deep Water website – “Using complex multi-layer artificial neural networks, Deep Learning helps us derive insights from large unstructured data such as images, videos, sound and text or structured data from transactional databases such as financial data or time series. It enables innovations in domains as varied as medicine, social media, customer service, targeted marketing, automotive safety, security or fraud detection.”

In the next video, Amazon offers up a 8 and 16 GPU EC2 instance.  A P3 instance can offer 1 PetaFLOP in a single instance.

A side note: We can differ on the optimism expressed but we are looking at the building of a new machine learning technology which will have a deeply profound ability to change society in radical ways. This is no longer speculation. An equivalent of a “Cambrian explosion” is on its way and will hit human societies in relatively short order.  For example, what happens when you don’t need drivers, cashiers, analysts, and a wide range of other jobs because machine-learning agents do the work? When technology will not generate new jobs for humans but rather for AI?  What effect will this have on human societies in general to see such wide-spread job losses. Will governments act on behalf of their citizens or on behalf of other interests.  It is an interesting question worth pondering now (as opposed to when we are faced with the problem).  Two authors Robert McChesney (professor at the University of Illinois at Urbana–Champaign) and John Nichols (journalist) offered a view of this future in 2016 in the book “People Get Ready – The fight against a jobless economy and a citizenless democracy “.

Recommended Reading: 7 Applications of Machine Learning in Pharma and Medicine

If you are wondering what is happening in the area of machine learning and medicine – the short answer is : quite a bit. Across seven areas we are seeing advances in disease identification/diagnoses, personalized treatment, drug discover/manufacturing, clinical trial research, radiology/radiotherapy, smart electronic health records and epidemic prediction.

Application: How machine learning will transform the way we look at medical images

One of the hottest topics today is around machine learning – you could be excused if you didn’t know that it actually has been around for a long time in a different context and survived the “AI winter”.  One area of machine learning focuses on ‘smart’ image recognition and its use in medical imaging.  The following article provides a perspective on how medical imaging is being changed by machine learning.