Industrial IoT

Are Machine Learning and IoT Dependent or Independent?

5 min readLast Updated: 2/6/2019 The Relationship Between Machine Learning, IoT, and AI In the [...]

5 min read

Last Updated: 2/6/2019

The Relationship Between Machine Learning, IoT, and AI

In the past few years, we saw the rise and fall of many overhyped trends. But only few technologies received more hype than IoT, machine learning, and AI.

And for good reason.

Just like humans need to adapt to their environments over time, companies must do the same to remain competitive within their respective industries. This “method of survival” often involves the implementation of new processes and technologies. However, it is often falsely assumed that the implementation of new cutting-edge technologies is reserved for traditional tech companies.

These days, all companies are tech companies – regardless of their industry -. For instance, you can use an app to order food and book a table at a restaurant, invest your money, track your biometrics, and much more. With the incoming 5G revolution, this will become even more true.

Large corporations and small startups are leveraging three hot technologies, either through acquisitions or in-house development, to bolster their business strategies and gain a strategic and long-term advantage over their competitors:

  • Machine Learning
  • Artificial Intelligence
  • Internet of Things

You’ve heard those buzzwords before, but what do they really mean and how do they work together?

 

What is Machine Learning (ML)?

Simply put, it is an application (or subset) of artificial intelligence that allows computers to train and learn for themselves with data that they’re fed or given access to.

Potential ML applications include:


Mnubo’s Asset Health Dashboard, an example of Machine Learning application

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI), loosely defined, is the ability for a computer to perform cognitive functions that are typically associated with human minds (perceiving, reasoning, learning, etc). In more technical ‘down-to-earth’ terms, AI means programming computers to learn from examples. Hence the dependency on having a lot of examples, i.e. Big Data.

AI is standing out as a transformational technology because of its practical applications across all industries:

  • Smart Buildings: energy consumption and occupancy predictions allow the buildings to be more energy efficient
  • Smart Agriculture (AgTech): increasing a given crop yield using sensor data and machine learning
  • Health: detecting cancer signs more accurately out of X-rays
  • Entertainment: Amazon and Netflix both use predictive technologies to suggest items to purchase and movies or shows to watch, respectively.)

Agtech mnubo

How does an IoT analytics company use AI and machine learning?

While some companies are working to incorporate these technologies around their already-existing business plans, other companies have been built on the foundation of AI / Machine learning, and IoT tech. Mnubo is an example of one such company. Mnubo is best described as an IoT data analytics company that leverages AI and ML to transform connected product data into advanced insights.

Connected products are outfitted with a plethora of sensors that collect data. Mnubo ingests the product data into its SmartObjects platform and then runs analytics on the data. As a result, product manufacturers receive insights (meaningful information) from their product data that they can use to improve their business, their product, the customer experience, etc.

Depending on the nature of data that a connected object is collecting, Mnubo can either perform descriptive, or soft, analytics on a product’s data, or advanced analytics; Artificial Intelligence and ML come into play for the latter.

Below are examples of features in Mnubo’s end-to-end solution that use AI and Machine learning:

Time-to-target (predictive)

  • Predict when a set threshold will be reached (i.e.: predict when a battery will reach a pre-set level of depletion)
  • Understand factors impacting battery depletion
  • Mnubo data-science team trains an ML model to be able to predict such factors by analyzing a device’s historical data

Event Explorer

  • Use K clustering to find trends, patterns, anomalies in event data (i.e.: identify users that took longer than average to change their HVAC filters after receiving an alert)
  • Drill down to identify root cause and take action to improve customer on-boarding

Mnubo SmartObjects Platform

Unleashing the Power of Machine Learning

Smart devices collect sensor data, but the data has little worth if it isn’t being leveraged for data analysis. Soft analytics are useful, but there is limited value in knowing who is using your products and where they’re being used. Advanced analytics come into the equation if the goal is to monetize your data.

Running advanced analytics requires data science expertise to extract insights from product data. This is where Artificial Intelligence and ML technologies become relevant, as they’re used by data scientists to train models that can sift through aggregates of data autonomously, finding anomalies, trends, faults, and more.

Aside from the analytics angle, by leveraging an AI layer, smart devices can train other smart devices based on their “experience” in the field and the sensor data they’ve collected; peer-to-peer training, if you will.

For example, HVAC ‘A’ can teach HVAC ‘B’ how to maximize energy efficiency. But how? HVAC ‘A’ will self-learn the most efficient way of cooling down an office building by analyzing its sensor data (ie: internal/external temperature, fan speed, etc.), and then teach its “peers” how to do the same.

HVACR data

AI and Machine Learning will facilitate advanced data analysis, especially for smart devices; Mnubo is at the forefront of this undertaking. However, we still have a narrow understanding of how these technologies, together, will revolutionize industries as a whole.


Want to learn more about how we use machine learning and IoT data? Download our free case study.