Adoption of the Artificial Intelligence of Things has never been more relevant for both industry and governments. A recent study by IDC and SAS found that a significant predictor of organizational ability to deliver value from the IoT is the use of AI. This is likely to become even more of a differentiator in the future.
I have suggested before that AI and IoT is a natural pairing, but research evidence shows that the connection goes deeper. From customer, patient and citizen experience to the factory of the future, companies that have linked AI to the IoT are more competitive and efficient than those using the IoT alone.
Connected everything – and enter COVID-19
Estimates published by the consultancy firm McKinsey suggest that the number of connected "things" is growing by more than 100 items every second – and these things produce immeasurable amounts of data. Until quite recently, most of this data would have gone to waste because it just could not be collected, collated, merged, stored or analysed in time to provide any real value.
Now, however, we can use new approaches to working with this type of data source, including machine and deep learning AI techniques to help augment and automate human decision making. This means we can improve the value of this data. Using AI to examine the vast quantities of IoT data is very much the logical next step in the evolution of data management and intelligent decision making.
IDC and SAS host a live webinar on June 8 to explore how AI and IoT technologies together can help organizations looking for new ways to create safer environments.
Many businesses and governments are responding now to the crisis by reprioritising projects using the IoT and AI to modernise ways of working with data, particularly to make better decisions.
There are now many initiatives taking place all around the world. For example, some organizations are merging computer vision techniques with environmental sensor data to enhance the quality of products on production lines. And others are optimising supply chains with cargo and pallet sensors in cold chain logistics.
Crowd safety management
One very good example is crowd safety management in city public transport systems. SAS has been working with a small group of specialist companies to offer an AIoT solution that works with existing infrastructure sensors, cameras and other data-generating devices in stations. This solution helps manage overcrowding, monitors passenger safety and optimises the experience for customers using public transport.
Public transport systems use SAS streaming analytics to capture data from different IoT devices and synchronise it with video feeds and audio from cameras with varying frame rates and image quality. This builds a composite picture of the current state of each platform. The composite data stream passes through decision flows, rules and inferenced analytics models, such as neural networks, to quickly identify any safety concerns or required action. It can even predict the likelihood of particular events taking place and proactively suggest action to mitigate them. What’s more, it includes built-in strict adherence to GDPR and personal data protection.
We are now using this same capability in a very different way to fight COVID-19. Instead of helping to monitor and manage crowd flows, we are focusing on managing the need for social distancing.
This kind of capability and flexibility just would not be possible without the AIoT. Much has changed as a result of the pandemic, and more is likely to come.
More on the topic at a live webinar
IDC and SAS host a live webinar on June 8 to explore how AI and IoT technologies together can help organizations looking for new ways to create safer environments. I hope to see you there.
Meanwhile I encourage you to watch this video: Track Social Distancing Using Computer Vision by AI Suisse, Switzerland's largest group of professionals with focus on artificial intelligence.