Does IoT Need Machine Learning To Succeed?

Why intelligence is integral to the success of IoT

Despite encountering a few bumps in the road, the great worldwide Internet of Things deployment keeps on forging ahead. As IoT hardware becomes even cheaper to make and IoT software becomes even more advanced, consumers, businesses, and other entities will see major changes. However, the intelligence that powers the IoT is in the midst of a revolution, and understanding what the IoT will look like requires understanding a classic computing concept undergoing a renaissance: Machine learning.

Wrangling Information

While machine learning will play a key role in several elements of the IoT, its most important job will be to simply keep it together. New information constantly flows on the IoT, and ensuring the right data is captured and sent to the right hardware and software requires advanced artificial intelligence. Although basic configuration can go a long way to making use of this information, the self-improving nature of machine learning is perhaps to only way to move toward true efficiency.

A Two-Way Street

The IoT needs machine learning, but machine learning needs the type of information only the IoT can provide. Machine learning can provide powerful and revolutionary insight, but the volume of data required to find signals in the noise is massive. The insight drawn from machine learning is inherently difficult to predict, and feeding the sheer volume of information the IoT generates will serve as indispensible fuel. Machine learning is well-established in theory, but its practical results should shine as the IoT continues to grow.

Efficiency and Performance

IoT devices typically consume very little power, and many can function with little maintenance on solar power alone. However, the massive number of devices being deployed, and the sheer volume of data they create, means overall energy usage can skyrocket. Machine learning enables IoT installations to scan through this data and find ways to improve efficiency, letting networks grow naturally without energy demands causing problems. Performance matters as well; responsiveness is key for creating systems appropriate for real-world use. Again, machine learning will be at the forefront of lowering latency and providing real-time systems appropriate information.

Saving Time

IT professionals are, and will remain, critical for managing computer systems. However, increasing complexity means that companies either need to hire more IT staff or find ways to offload some of this increasing demand to IT equipment. Machine learning has the potential to make decisions that previously required human input. Systems, for example, can tweak their performance automatically so IT staff doesn’t have to dedicate time to fine-tuning performance. This frees up staff to perform tasks that still require human input, lowering operating costs and letting companies build out their IoT networks more quickly.

Facing the Unexpected

Part of managing businesses is dealing with uncertainty. Nobody can predict the future, but maintaining flexibility is great for ensuring businesses can handle unexpected demands in a graceful manner. Machine learning often places a focus on providing more general-purpose capabilities than other artificial intelligence technology found in businesses, and flexible design will let IoT networks handle whatever the future brings in a smooth manner. Companies that place more of an emphasis on machine learning instead of sensors and other hardware might be better equipped as new technology comes online.

Machine learning is an old concept, and it’s provided tremendous value for decades. However, it requires a significant volume of data to truly shine, and these applications have historically been rare. As IoT networks start churning out data at never-before-seen rates, machine learning will play an increasingly critical role in IT.