Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017. But in fact they are more real today than any time in the past. However in order for companies to realize the full potential of IoT, they need to combine IoT with rapidly-advancing Artificial Intelligence (AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention.
Back to the basics
What do these terms really mean and what is their relation? Let’s start by defining both terms first:
- IoT is defined as a system of interrelated Physical Objects, Sensors, Actuators, Virtual Objects, People, Services, Platforms, and Networks that have separate identifiers and an ability to transfer data independently. Practical examples of IoT application today include precision agriculture, remote patient monitoring, and driverless cars. Simply put, IoT is the network of “things” that collects and exchanges information from the environment.
IoT is sometimes referred to as the driver of the fourth Industrial Revolution by industry insiders and has triggered technological changes that span a wide range of fields. Gartner forecasted there would be 20.8 billion connected things in use worldwide by 2020, but more recent predictions put the 2020 figure at over 50 billion devices. Various other reports have predicted huge growth in a variety of industries, such as estimating healthcare IoT to be worth $117 billion by 2020 and forecasting 250 million connected vehicles on the road by the same year. IoT developments bring exciting opportunities to make our personal lives easier as well as improving efficiency, productivity and safety for many businesses.
- AI, on the other hand, is the engine or the “brain” that will enable analytics and decision making from the data collected by IoT. In other words, IoT collects the data and AI processes this data in order to make sense of it. You can see these systems working together at a personal level in devices like fitness trackers and Google Home, Amazon’s Alexa, and Apple’s Siri.
With more connected devices comes more data that has the potential to provide amazing insights for businesses but presents a new challenge for how to analyse it all. Collecting this data benefits no one unless there is a way to understand it all. This is where AI comes in. Making sense of huge amounts of data is a perfect application for pure AI.
By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. This leads to a variety of benefits for both consumers and companies such as proactive intervention, intelligent automation and highly personalised experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.
This, in turn, leads to even higher adoption rates. That’s exactly why; we need to improve the speed and accuracy of data analysis with AI in order to see IoT live up to its promise. Collecting data is one thing, but sorting, analyzing, and making sense of that data is a completely different thing. That’s why it’s essential to develop faster and more accurate AIs in order to keep up with the sheer volume of data being collected as IoT starts to penetrate almost all aspects of our lives.
Examples of IoT data:
- Data that helps cities predict accidents and crimes.
- Data that gives doctors real-time insight into information from pacemakers or biochips.
- Data that optimize productivity across industries through predictive maintenance on equipment and machinery.
- Data that creates truly smart homes with connected appliances.
- Data that provides critical communication between self-driving cars.
It’s simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge, just ask a data scientist.
But in order for us to harvest the full benefits of IoT data, we need to improve:
AI and IoT Data Analysis
There are six type of IoT Data Analysis where AI can help:
- Data Preparation: Defining pools of data and clean them which will take us to concepts like Dark Data, Data Lakes.
- Data Discovery: Finding useful data in the defined pools of data.
- Visualization of Streaming Data: On the fly dealing with streaming data by defining, discovering data, and visualizing it in smart ways to make it easy for the decision making process to take place without delay.
- Time Series Accuracy of Data: Keeping the level of confidence in data collected high with high accuracy and integrity of data.
- Predictive and Advance Analytics: Very important step where decisions can be made based on data collected, discovered and analyzed.
- Real Time Geospatial and Location (logistical Data): Marinating the flow of data smooth and under control.
AI in IoT applications:
- Visual big data, for example – will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.
- Cognitive systems will create new recipes that appeal to the user’s sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.
- Newer sensors will allow computers to “hear,” gathering sonic information about the user’s environment.
- Connected and Remote Operations– With smart and connected warehouse operations, workers no longer have to roam the warehouse picking goods off the shelves to fulfil an order. Instead, shelves whisk down the aisles, guided by small robotic platforms that deliver the right inventory to the right place, avoiding collisions along the way. Order fulfilment is faster, safer, and more efficient.
- Prevented/Predictive Maintenance: Saving companies millions before any break down or leaks by predicting and preventing locations and time of such events.
These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people lives.
Challenges facing AI in IoT
- Compatibility: IoT is a collection of many parts and systems they are fundamentally different in time and space.
- Complexity: IoT is a complicated system with many moving parts and non –stop stream of data making it a very complicated ecosystem.
- Privacy/Security/Safety (PSS): PSS is always an issue with every new technology or concept, how far IA can help without compromising PSS? One of the new solution for such problem is using Blockchain technology.
- Ethical and legal Issues: It’s a new world for many companies with no precedents, untested territory with new laws and cases emerging rapidly.
- Artificial Stupidity: back to the very simple concept of GIGO (Garbage In Garbage Out), AI still need “training” to understand human reactions/emotions the decision will make sense.
While IoT is quite impressive, it really doesn’t amount to much without a good AI system. Both technologies need to reach the same level of development in order to function as perfectly as we believe they should and would. Scientists are trying to find ways to make more intelligent data analysis software and devices in order to make safe and effective IoT a reality. It may take some time before this happens because AI development is lagging behind IoT, but the possibility is, nevertheless, there.
Integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems. So businesses must move rapidly to identify how they’ll drive value from combining AI and IoT—or face playing catch-up in years to come.
The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the catalyst of IoT.
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