This guest column is authored by tech enthusiast & writer, Vivian Michaels
Wearable health and fitness sensors have widely been adopted, and many people are not aware that they are using sensors to track the movements of animals and track the Structural integrity of buildings and bridges. As a result of the rapid Internet of Things (IoT) increase, it is speculated that a lot of sensor devices will be available in the coming ten years. These are connected sensor devices that will be in a position to automate different processes across a wide range of sectors including healthcare management and industrial plants.
Each IoT device and application will come with unique content including the location, behaviour of individuals in a given area and the surrounding environment’s condition. Each device will be able to observe and become adapted to their contexts.
Comes artificial intelligence
When artificial intelligence is introduced, devices can be able to change their behavior in relation to the evolving contexts. Just the same way living organisms optimize their activities to cope with their surrounding environment, even a small avatar can evolve its software with time. Take a mobile gadget such as a Smartphone or a Smartwatch that is able to ship in large volumes where one size is enough for all apps and features for all users.
To customize them, users need to configure individual apps manually and repeatedly update the configurations whenever their preferences change. However, if the device could learn peoples’ preferences on its own by observing the patterns of usage, the personalization process could be automated.
In situations whereby the devices are yet to be experienced, could they learn peoples’ preferences in unknown situations? This is where virtual intelligence assistant can help one another to learn faster and efficiently by sharing each other’s experiences and information. This could result in an increased learning process by the devices.
Smartphones could make use of their proximity to run individual artificial intelligence machines as well as share logic blocks so as to accelerate learning such as how they can maintain battery life. This could benefit the two devices independently whereby each could learn how to develop genetic material on its own also referred to the island model computing. This implies that each individual device becomes an island of its own in IoT. From time to time, the devices can share things they have learned.
This is very beneficial because new things are added to their genetic pool diversity as they evolve. Additionally, it implies that the two devices are able to know the best way to deal with new contexts that might have been witnessed the collaborating devices originally.
Animal tracking also provides the same driver from collaborating artificial intelligence. Devices are always placed on ear tags or collars to monitor the activities and positions of wildlife, pets or livestock. To provide accurate monitoring information, individual devices must learn the movement features particulars of the creature it is monitoring including the age, gender and the species, which artificial intelligence can provide assistance.
The advantages of IoT shared learning to surpass devices on people and animals. Consider the devices placed to track the structural health of roads or bridges. Usually, these devices don’t have a link to internet communication due to remoteness and cost. However, they are able to collect information and also learn certain models from observed sensor data to predict faults.
Since faults can be relatively uncommon, sharing learning with the neighboring devices can provide a larger IoT devices training pool that might not have encountered a fault yet. Some questions still exist before shared IoT devices learning can become a reality. For instance, is it possible for devices to compromise their owners’ privacy in case they take part in shared learning environments? The answer to these questions greatly relies upon whether the artificial intelligence approach used can share information that contains some intrinsic meanings or not.
Additionally, how does a certain device determine which of its neighboring gadgets to have confidence in when choosing the devices to collaborate with? What could happen in case a malicious unit enters the network with an aim to inject some disruptive logic inside the shared IoT learning environments? There is need create methods that can address these issues fully.
It is unclear about the progress of having IoT gadgets that are able to learn from one another. Although the applications for IoT are assumed to be in their initial stages, the potential opportunities require debate, attention, and thorough investigations. This could lead to IoT devices that are able to deliver its daily tasks continuously and learn new methods to react to new situations appropriately.
For artificial intelligence machines to learn from each other, there is a need to put in place relevant safety controls including placing some hard restrictions on what devices should learn and what not to. Additionally, hard constraints should also be placed on things that shouldn’t change in relation to machine learning. This can assist artificial intelligence machines effectively, and faster learn from each other by sharing information.