In the industrial world, and specifically the energy sector, the amount of connected devices, sensors and machines is continuously growing, resulting in the internet of energy, or IoE. IoE can be broadly defined as the upgrading and automating of electricity infrastructures, making energy production more clean and efficient, and putting more power in the hands of the consumer. Given the vast amount of data the energy sector generates and the increasing number of sensors added, it is the perfect environment for machine learning applications. Artificial intelligence (AI) excels at finding subtle patterns in data sets of all shapes and sizes, particularly under complex or changing conditions. Although data within IoE is growing at exponential rates, much of that data is traditionally siloed across business units (generation, transmission and distribution, energy trading and risk management, and cybersecurity).

At the intersection of machine learning and energy consumption stands an incredibly powerful force with the potential to transform the way we globally produce and consume energy. So powerful in fact, that the concept of merging machine learning and renewable resources has been named the "energy internet" by economic theorist and author Jeremy Rifkin or "digital efficiency" by Intel and GE. Digital twin tech, or a virtual representation of a product, is a critical concept in IoT that's still being sorted out. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Today's IoT use cases increasingly depend on performing analytics or updating machine learning algorithms in real time on huge amounts of device-generated data. If the data for patient monitoring, autonomous vehicles or predictive maintenance applications isn't ingested, processed and acted upon in real time, patients suffer, vehicles crash or systems fail. So how can businesses cost-effectively build a reliable platform for ingesting and responding to massive amounts of data at scale? Businesses can do so with a streaming platform and data storage system built on an open source software stack. Many of today's open source solutions have proven to be reliable across thousands of production deployments.

Since the next gen technologies became mainstream about three to four years ago, the IoT industry has gone through a significant journey. Needless to say, IoT has been the focal point of many transformation-related conversation in the asset heavy industries. In the beginning, most organizations were struggling with the question of how to interpret IoT in the context of their business. Since the possibilities were endless, so was the dilemma of where and how to begin. The industry ultimately chose to go with a case-based approach and the technology service providers played a key role in launching it.

The COVID-19 outbreak has reminded the world that there's a lot of truth in the old saying that necessity is the mother of invention. As businesses, schools, churches and other organizations have closed down to stem the spread of the virus, people quickly found ways to connect and interact with each other virtually. An accelerated outcome of the pandemic has been the decentralization of healthcare, which has allowed for real-time decision making around diagnosis and treatment. Fortunately, digital technologies such as AI, machine learning and IoT are helping solve some of the most critical healthcare problems during these trying times. For example, keeping patient information safe while connecting several devices urgently during a pandemic requires an interconnected, secure digital ecosystem on the backend.

The deployment of 5G networks promises to exponentially increase the amount of IoT data available for modern enterprises. The number of global 5G subscriptions is expected to hit 1.5 billion in the next few years, according to GlobalData research, only escalating the number of IoT devices and sensors that are producing constant streams of data. But with this data and connectivity boom comes a heightened threat of fraud and network breaches. Nearly half of enterprises sacrificed mobile security in 2019 and are twice as likely to suffer a security compromise as a result, according to Verizon's 2020 Mobile Security Index report. As 5G connectivity and IoT devices expand into unexplored territories, opportunities for cybercriminals to become more creative and exploit new and unforeseen vulnerabilities will inevitably continue to grow.

This is the first part in a collection of articles covering IoT and AI. There is a parable in Buddhism about problems: It says that we all have 83 problems, and as we take one away, another appears to take its place. I don't know about you, but I am pretty certain that the same applies to the Pareto principle. Many are all familiar with the Pareto principle, or the 80/20 rule: 20% of your effort can yield 80% of the desired value, while 80% of your can yield 20% of the desired value. Whether it be sales, relationships, learning or the arts, this rule informs us that focusing our efforts on the right 20% will drive the most impactful outcomes.

IoT Agenda featured Centric Consulting's Carmen Fontana, Modern Software Delivery Service Offering Lead, in an article discussing how to navigate the challenges of the Internet of Things (IoT). Carmen is one of three experts featured in the article, and she shares her insights into Machine Learning and her views on how this area of AI will affect IoT for the better. "IoT truly comes to life when paired with machine learning. Machine learning pushes IoT to transition from reactive to predictive analytics, forecasting future outcomes and cutting through the fog of data. Machine learning also thrives on scale, making it a natural pairing for voluminous IoT data," the article reads.

AI is transforming the world as we know it. Contextual awareness paired with AI is opening the door to many positive solutions for healthcare, environmental protection, conservation, smart cities and public safety. Enterprise AI applications also proliferate in marketing and sales, HR and recruiting, security, autonomous operations and financial services. On the other hand, the rapid advancement of AI also raises questions and concerns around data ethics, which are only beginning to be addressed. As a case in point, the New York Police Department (NYPD) has been challenged by AI bias concerns for its new crime analysis AI tool.

Any business looking to implement emerging technologies has one primary goal: to generate revenue. Deploying advanced digital technologies into business functions is no small undertaking; it requires a large financial investment, a reskilling of the workforce and a cleaning of vast amounts of data to ensure it's prepared to be analyzed. Simply put, if you take this on, you want to see the return. However, there's a fundamental problem with the approach many companies are taking with machine learning. They are using it to superficially enhance the customer experience, but stop short of transforming it into a true revenue-generating engine.