5 min read The Relationship Between AI, Machine Learning, IoT In 2017 we saw the [...]
In 2017 we saw the rise and fall of many overhyped trends. But despite being two to five years away from mainstream adoption according to Gartner’s 2017 Hype Cycle for Emerging Technologies, few technologies received more hype than AI Machine learning and IoT and ; and for good reason.
Akin to how humans adapt to their environments over time, companies must do the same to keep a competitive advantage 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.
This article from CNBC goes into detail about how all companies – whether they specialize in food, finance, wearables, or other – are all tech companies. You can use an app to order food and book a table at a restaurant, invest your money, track your biometrics, and much more.
RELATED: interested in monetizing your data? click below to read more!
Large corporations and small start-ups are leveraging three hot technologies, either through acquisitions or in-house development, to bolster their business strategies and gain an advantage over their competitors. AI, Machine Learning, and the Internet of Things; buzzwords that you’ve certainly heard by now, but what do they mean and how do they work together?
Umbrella term for machines which have the ability to perform functions, mimicking how humans would carry out the same tasks. Machine Learning falls under “Generalized AI”; a reality where computers could perform any task that a human can, and typically more efficiently and effectively.
An application (or subset) of AI that allows computers to train and learn for themselves with data that they’re fed or given access to.
(ML applications: fraud detection used by credit card companies by finding anomalies in data sets autonomously, facial recognition software, etc.)
An ecosystem of connected devices – ranging from smartwatches to smart refrigerators and HVAC systems – that can transfer data over a network without human-to-computer interaction.
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 which 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.
Smart devices collect sensor data, but the data has little worth if it isn’t being leveraged for data analysis (I wrote about that here). 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.
Google used AI to manage energy usage in one of its data centers in 2016. The result: a 40% reduction in the amount of energy used for cooling the data center, and overall power consumption cut by 15%.
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.
Find out how Mnubo is tapping into the benefits of product data by transforming raw sensor data into actionable business insights. Read more here!
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