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: Keys To Success With Computer Vision-Guided Robotics For Flexible Automation

If you are interested in computer vision-guided robotics there is a nice talk on the topic. David Dechow, Staff Engineer for Intelligent Robotics/Machine Vision, FANUC America Corporation provides guidance on where vision-guided robotics makes sense in different settings.  It discusses the need for smart design of robot work cells and how to incorporate vision.

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.

Datomic : Moving Beyond the Current Generation of Relational and NoSQL Databases

It’s really seldom that you get a chance to see a different thought pattern emerge in database design that literally covers the key advantages of both relational and NoSQL databases and offers a new set of features. Datomic is an aggressive attempt to create a next-generation database that moves us beyond the usual and is built around running in current and future cloud architectures. Let’s start with the some of the features in Datomic. It provides support for ACID transactions, Joins, a sound data model and a logical query language called Datalog. Most of all it breaks with the rigidity of document-based models and relational database models. It also breaks with a model of mutability and a lack of audit data trails. To understand it is worth taking a tour of both it and it’s query language. One surprising aspect that it provides this by using engines like Couchbase, Cassandra,Riak, etc as nothing more than storage engines or services.  Let’s start with the tour  of Datomic :

By deconstructing the current generation of databases and implementing a modern database focused on breaking with designs from decades ago or some of the issues with the newer NoSQL databases (lack of Joins, lack of ACID, etc).   To understand the power of the database you have to explore the power of the datalog query language:

Large, medium and small companies using Datomic.  So you may be interested in the “why”.  In this presentation – developers of the Brazilian NuBank expose four hidden super-powers of Datomic.  In this case, the Clojure programming language is used.

and here is another use-case from Room Key ( joint project of Hilton, Choice, Hyatt, IHT, Marriot, Windom) :

 

More reading :

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examples

 

 

Recommended Learning Exercise : Simulating A Parking Garage in Clojure

Here is a nice example of creating software that simulates a parking garage. Two different examples. If you are learning Clojure this might be of interest to you.  In the first example, Clojure Refs are used. The problem is to simulate operations on a garage used for parking vehicles – vehicles come into the parking garage, park and then later leave.  They are identified by their license plate number.  This is a nice example for those looking for Clojure examples.

parkgarage1

In the second example, the same parking garage problem from the previous post is solved using  clojure.spec . See more on the clojure.spec rationale and the  guide.  Here is the second example which uses Clojure.spec.

garagespec2

 

 

Recommended Reading : An Archaeology-Inspired Database

Yoav Rubin shows how a change in a common perspective affects the design and archdb2implementation of a well-studied type of software: a database.  In this excellent examination of how a shift in perspective changes everything, Yoav provides us with a rich examination of what we thought we knew but it turns out that perhaps there are better ways to approach databases. “Database systems are designed to store and query data. This is something that all information workers do; however, the systems themselves were designed by computer scientists. As a result, modern database systems are highly influenced by computer scientists’ definition of what data is, and what can be done with it.

For example, most modern databases implement updates by overwriting old data in-place instead of appending the new data and keeping the old. This mechanism, nicknamed “place-oriented programming” by Rich Hickey, saves storage space but makes it impossible to retrieve the entire history of a particular record. This design decision reflects the computer scientist’s perspective that “history” is less important than the price of its storage.”

As Yoav puts it succintly -If you were to instead ask an archaeologist where the old data can be found, the answer would be “hopefully, it’s just buried underneath”.

Leveraging the richness of the Clojure language and 360 lines of code – we have an “archeology-inspired” database. To read the article, select:

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