In 2017 we saw the rise and fall of many overhyped trends. But despite being [...]
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 Artificial Intelligence; 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.
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. Artificial Intelligence, 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.
(AI applications: Amazon and Netflix both use predictive technologies to suggest items to purchase and movies or shows to watch, respectively.)
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, ML, 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; AI and ML come into play for the latter. Below are examples of features in mnubo’s end-to-end solution that use AI and ML.
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.
Deloitte Global predicts that by end-2016 more than 80 of the world’s 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products, a 25 percent increase on the prior year. By 2020, we expect the number will rise to about 95 of the top 100.
Running advanced analytics requires data science expertise to extract insights from product data. This is where AI 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%.
Artificial Intelligence 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.
Interested in more examples of AI-driven use cases? Learn how mnubo is monetizing product data with artificial intelligence!