The Internet of Things (IoT) is very closely related to the artificial intelligence (AI) technologies: in order to function properly, AI requires algorithms and most of these algorithms are based on machine learning and deep learning techniques. Further, AI is required for robotics, and both are the forms of automation. The IoT is all about connectivity and automation that, in its turn, requires algorithms. So, as we can see, IoT and AI are greatly interconnected and empower each other!
Let's take a look at 6 ways AI would empower IoT.
IoT connects high-end devices like PCs and micro-controllers to low-end devices like sensors and actuators. This will create an ecosystem of devices of all kinds that wouldn't rely on advanced self-awareness. Instead, they would rely on predictability. As a pool of smart gadgets is getting larger literally from month to month, any excessive computation will become a waste of energy. As such, AI will help avoid self-reflective thinking that increases device's unpredictability.
IoT enables significant scalability of devices, each having a great potential to streamline parameters and measurements. In order to summarize data before transmitting it to other devices, AI is an indispensable technology! As a result, you'll reduce your data flood to an easily manageable level.
In addition, AI can be used to act as a manager of groups of devices and help coordinate interoperability. As such, it'll become possible to connect a plethora of devices to the network and scale it up or down as the market changes.
Data sharing capability is a critical feature of a smart device. One sensor can generate a lot of data, but the value of data multiplies if it's generated by dozens or hundreds of sensors. When devices are connected and communicate with each other, they generate a richer data stream and that's when AI comes in very handy!
Yet, security design should be embedded in the AI algorithms prior to enabling data sharing for the purpose of enhanced IoT security.
Collaborative learning is another type of data sharing that is best achieved with AI. This approach is inherent to self-driving (aka driverless) technologies that are booming nowadays (e.g., Tesla). A collaborative learning is created by networking several smart cars together so that one car that has learned how to drive would teach all others. Unlike Tesla, Google uses an isolated learner approach for its driverless tech. Yet, this approach is also based on AI.
Based on the AI algorithms, machine learning is used in a limited extend to respond to unexpected situations. When a device receives an unexpected query or identifies non-typical scenarios, it should be informed whether to react anonymously or involving a human being. IoT uses deep learning to make the best guess about the query meaning in order to respond to it in the real time without any human done research. By the way, Google's RankBrain uses the same approach.
AI enables intelligent machine learning and decision making which are critical for success of any IoT project.
Mass Customization and Uniqueness
AI-based machine learning can help each device adapt to the conditions within its own environment. Let's take a look at the Nest smart thermostat. Once deployed to the home environment, it starts memorizing user habits and preferences and modeling / reflecting on typical user behavior patterns. That being said, no matter how many smart devices a manufacturer will push to the mass market, there won't be two of the same kind as each will be unique in the actual usage (because we all have different habits and lifestyles!).
This uniqueness enables mass customization of the IoT devices.
Internet of Emotions
When machines interact with humans, they learn from not only irregular behavior patterns, but from verbal inputs and facial expressions as well. As it's very difficult to program such inputs by hand, AI will be added to the sensory layer to teach devices how to recognize user faces and emotions.
This will add empathy to the Internet of Things!