It is increasingly harder to speak of the future of technology without mentioning the Internet of Things (IoT) and blockchain. Likewise, the same could be said about machine learning. A research report by Gartner, which says that worldwide IT spending will reach $3.7 trillion in 2018, mentions that Artificial Intelligence (AI) is a key driver for the market’s growth.
A Forbes report also says that the global IoT market will grow from $157 billion to $457 billion between 2016 and 2020. What makes machine learning important in IoT is a critical step in a typical IoT pipeline that involves inspection of data and decision-making.
Uses with Blockchain
Machine learning can be best described as software that changes and adapts based on user behavior and data gathered from continued use. Therefore, new rules and provisions do not have to be added manually. A convergence between AI and blockchain could include blockchain benefitting from AI’s ability to accelerate analysis of an enormous amount of data. The potential of blockchain technology to simplify the process of data analysis is already apparent in not just the financial sector but the public sector as well.
Another field where blockchain might stand to benefit would be security. Already there are apparent advantages that blockchain can bring to IoT security, but machine learning’s implementation could bring more benefits. Creating blockchain systems advanced enough to bolster IoT security is so difficult, institutions are considering using AI. One example is Google’s DeepMind, a firm known for machine learning platforms, which hopes to improve their healthcare initiatives with healthcare and AI.
Still, there are a few roadblocks standing in the way of implementation that prevent machine learning and blockchain from transitioning from the theoretical to reality. Aaron Powell, the chief digital officer at Britain’s National Health Service (NHS) Blood and Transplant division, has overseen a few trials at his organization. Regarding a trial involving predictive analytics with organ donation, he believes that “predictive capability will have a significant impact, as well the ability to take advanced imaging. In organ donation, the ability to take imagines and send those to the transplanting team in a more intelligent way could be huge, particularly when it comes to knowing what types of situations health teams need to prepare for during an implant.”
He says that while blockchain could be useful in the logistics-based process of organ and blood donation, he remains skeptical. “We’ve looked at blockchain and we don’t see an obvious use case right now.” While blood donation services in other parts of the world are more fragmented and might be in need of a distributed ledger to securely trace various elements, it is less of a problem for Powell’s case, as he and his staff have more control over their end-to-end processes.
Uses with IoT
Namit Tanasseri of Tech Exchange breaks down a typical IoT solution pipeline into five stages: event production, event ingestion, transformation and analytics, persistence and storage, and presentation and action. The “transformation and analytics” phase is where the role of machine learning and AI is important. The ability of the system to make cognitive decisions based on historical data will greatly enhance the solution.
Tanasseri lists a few common scenarios where machine learning could work in-tandem with IoT, including anomaly monitoring, predictive maintenance, and vehicle telemetry.
Anomaly monitoring, where machine learning can be used to detect anomalies in time series data, in data feeds sent by IoT devices that are uniformly spaced in time. Anomalies, such as spikes and sips, and positive and negative trends, can be detected using a machine learning algorithm that monitors the live stream of device feeds.
As for predictive maintenance, machine learning algorithms can foresee possibilities of device failures, the remaining life of equipment, and causes of failure. Finally, with vehicle telemetry, machine learning can ingest millions of possible events from vehicles to improve safety, reliability, and driving experience.
One implemented example is Microsoft’s Azure Machine AI program on Windows 10, allowing developers to implement pre-built machine learning models on their apps to obtain real-time analysis of data and reduce costs.
Blockchain and IoT are both new frontiers for the tech industries, and markets that are projected to expand at an exponential rate. Likewise, machine learning is a technology that will shape the future. It’s ability to process a vast amount of data would prove indispensable in blockchain platforms. Likewise, the transformation and analytics phase of IoT devices can stand to benefit from machine learning algorithms, which can process the data and make predicative decisions based on that data. Whatever the future has in store for technology, IoT, blockchain, and machine learning will certainly be a part of it.