Why does IoT Need Artificial Intelligence to Succeed?
- Apr 12, 2019
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IoT will produce a treasure trove of big data – data that can help cities predict accidents and crimes, give doctors real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, create truly smart homes with connected appliances and provide critical communication between self-driving cars. The possibilities that IoT brings to the table are endless.
As the rapid expansion of devices and sensors connected to the Internet of Things continues, the volume of data being created by them will increase to a huge level, and the big problem will be finding ways to analyze the deluge of performance data and information that all these devices create.
Here’s the thing: The only way to keep up with this IoT-generated data and gain the hidden insight it holds is with machine learning.
What is AI and What is Machine Learning?
Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience, and decision theory. Many fields fall under the umbrella of AI, such as computer vision, robotics, machine learning, and natural language processing.
Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.
Why Machine Learning Matters?
Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic.
The rate of acceleration is already astounding. After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years.
Machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like.
In an IoT situation, machine learning can help companies take the billions of data points they have and boil them down to what’s really meaningful. The general premise is the same as in the retail applications – review and analyze the data you’ve collected to find patterns or similarities that can be learned from, so that better decisions can be made.
For example, wearable devices that track your health are already a burgeoning industry – but soon these will evolve to become devices that are both inter-connected and connected to the internet, tracking your health and providing real-time updates to a health service.
The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example. To be able to call out potential problems, the data has to be analyzed in terms of what’s normal and what’s not. Similarities, correlations and abnormalities need to be quickly identified based on the real-time streams of data. Could this be done by an individual working at the health service – reviewing data from thousands of patients in real-time and correctly deciding when to send an emergency flag out? Not likely – writing code, or rules, to scour thru the data to find known patterns is enormously time consuming, fraught with error and limited to only identifying previously known patterns.
In order to analyze the data immediately as it’s collected to accurately identify previously known and never-before seen new patterns, machines that are capable of generating and aggregating this big data must also be used to learn normal behaviors for each patient and track, uncover and flag anything outside the norm that could indicate a critical health issue.
The realization of IoT depends on being able to gain the insights hidden in the vast and growing seas of data available. Since current approaches don’t scale to IoT volumes, the future realization of IoT’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in almost every facet of our daily lives.
Machine learning is at the core of our journey towards artificial general intelligence, and in the meantime, it will change every industry and have a massive impact on our day-to-day lives.