Time and again, those keeping a pulse on the Internet of Things (IoT) space frequently hear about the “rise of the machines.” Humanity is not only discovering fascinating ways to integrate machines into our daily lives, but also finding new uses for machines as well. How? Machines are now “internet-connected” just like the smartphones we carry around in our pockets. And this isn’t just on the commercial side with the likes of smart thermostats or connected vehicles – even tractors and oil and gas machinery are industrial examples of where new “things” are now on the digital network.
In fact, there are more M2M or “machine-to-machine” communication devices on this planet than humans. As GSMA Intelligence reported in 2014, there are 7.2bn M2M devices versus 7.19bn humans. Stuart Taylor from Cisco also wrote a prediction that “The Internet of Things (IoT) is a world where up to 50 billion things (or devices) will be connected to the Internet by 2020; or, the equivalent of 6 devices for every person on the planet.”
Realizing the major role M2M devices continue to have in our connected world, specifically as it relates to the advent of machine learning, it’s only natural to highlight the impact of machines and M2M in the past, present and future.
The Machines are Coming: How M2M Spawned the Internet of Things
In the digital world, M2M wireless solutions will work for us quietly, in the background solving all our day-to-day needs. John Kennedy with Silicon Republic reports that, “M2M is at the heart of the industrial internet of things (IIoT), powering smart factories that can be run remotely from a tablet computer, and smart buildings that monitor their environment and feed data back to the cloud.”
Is Machine Learning Over Hyped?
In the now 24-hour news cycle, often the top news lingers around lighter topics. So how much hype should be given to machine learning (ML)? The Huffington Post respondent Scott Aaronson, theoretical computer scientist at MIT, seems to think that “There’s no doubt in my mind that people 30 years from now will agree with us about the central importance of ML, but which aspects of ML will they rage at us for ignoring, or laugh at us for obsessing about when we shouldn’t have?
Machine Learning: Demystifying Linear Regression and Feature Selection
Machine learning needs to integrate domain knowledge in order to improve the quality of data collected from analysts. Josh Lewis with Computerworld thinks that, “Business people need to demand more from machine learning so they can connect data scientists’ work to relevant action.”
Machine Learning Examples Crop up for Data Center Management
Data centers appear to be the perfect place for enterprises to implement machine learning to its fullest. Christopher Yetman, COO at Vantage Data said, “There are also sensors that generate data about air pressure, humidity, temperature and supply voltage and typically feed into a programmable logic controller.”
M2M Technology Driving Agriculture’s Industrialization
On a global front, M2M is driving agriculture’s industrialization in South Africa. IT News Africa informs us that, “Given the ability to automate many monitoring and control functions through intelligent devices, agriculture is a prime target for leveraging M2M capabilities.”
We hope you have enjoyed this week’s roundup, and as M2M connections continue to pile-up, we urge you to consider the plethora of commercial and industrial use cases that can benefit from these innovations.