You probably know that IoT and AI are very different technologies. The Internet of Things refers to the interconnecting of devices from tiny sensors to complicated engines, enabling them to capture data and communicate with each other, or with a centralised platform. Artificial intelligence refers to the simulation of human intelligence in machines – technology which can learn from previous inputs and results, and ultimately get smarter over time.
So far, so straightforward. And yet the underlying mechanism of both technologies is remarkably similar. Both involve turning data – often vast volumes of it – into tangible, actionable insights. Both involve tracking trends through time, and making predictions as to how those trends might continue in the future. And often they are fundamentally entangled with each other, with AI technology being brought to bear on data collected by IoT ecosystems. Ultimately, artificial intelligence has a vital role to play in the IoT, by making useful sense of the vast amounts of data generated by a typical IoT ecosystem.
Let’s take a closer look at some of the ways in which this can work in practice:
Connected devices in the healthcare industry – whether wearable or implanted devices – collect a huge amount of valuable data pertaining to individual health and activity levels, and how these interact with other data such as the likelihood of their being diagnosed with a particular condition, or how they respond to a specific treatment. Analyse this information with the help of AI, and you can start to spot trends in treatment effectiveness, or the risks and benefits of different conditions and behaviours. AI and the IoT are therefore helping to develop more accurate and proactive approaches to predicting, diagnosing and treating illnesses.
Yes, they haven’t quite hit the mainstream yet. But the development of driverless cars is involving a sophisticated mixture of both IoT and AI technology. Connected sensors within the car enable it to operate autonomously, by establishing its location, environment and proximity to others. AI technology is enabling those systems to get ever smarter at recognising the vehicle’s surroundings and, crucially, to make near-instantaneous decisions as to what – or – who – to avoid.
Farms, ranches and other agricultural settings are ripe for IoT deployments – they cover sometimes vast areas, and collecting relatively simple data from across those environments, such as humidity or soil nutrient levels, can be hugely valuable. However, yet again, making tangible sense of that vast amount of data requires a machine learning approach. In time, such a system can make proactive decisions as to which crops should be grown when and where, and when irrigation and pest control should take place in order to optimise yields.
In smart city contexts, AI can help IoT ecosystems to learn from patterns and develop more efficient approaches to waste management, security systems, parking and traffic management and more. The possibilities for intelligent and proactive approaches to a wide range of mundane urban planning and management tasks are truly enormous.
In short, the IoT generates big data – but AI makes big data make sense.