How AIoT will take productivity levels to new heights

Przemysław Łopaciuk

Director Technology, Media & Telecoms

10 min read

Human productivity comes as the result of an intricate collaboration between our brain and the nervous system. Now, artificial intelligence (AI), the digital mind, and the Internet of Things (IoT), the sensory apparatus of computing systems, are teaming up to push efficiency and productivity to unprecedented levels. 

This is called the Artificial Intelligence of Things (AIoT), and takes place when AI is embedded into components such as programs, chipsets and edge computing, which are all connected by IoT networks.

 

With AIoT, it becomes much easier to create systems that have near limitless processing power. The technology is already having an impact despite being in its infancy, and holds huge promise for driving productivity as use cases expand and develop in varying industries. Here’s how AIoT is boosting capabilities of teams across sectors.

AIoT speeds everything up

It’s no secret that optimizing the time it takes to gain insights or complete projects is a key component of efficiency. AIoT plays a huge role here: It drastically reduces response time latency and increases bandwidth as it processes and mines data efficiently without having to send it to the cloud.

 

While IoT’s cloud-centric management system limits the amount of data that can be collected, processed and analyzed due to the amount of processing power needed, AIoT allows for IoT management at the edge.

 

AIoT reduces the traffic that flows back to the cloud and cuts response times as management decisions are available on-premise, close to the devices. This allows for real-time response times, which are critical for driving productivity in industries such as manufacturing and automotive. 

 

For example, NVIDIA’s Jetson platform combines hardware accelerators and software platforms to run inference models on the edge and provide optimized edge pipeline workflow. We have also seen Tesla leveraging AIoT to achieve ultra-low latency: The carmaker’s autopilot system incorporates GPS, cameras, sonars, and forward-looking radars to use the data gathered by the self-enclosed system, and then uses a neural network model to determine how the car should react - virtually instantaneously.

AI at the edge drives proactivity, not reactivity

IoT without AI integration meant teams could quickly react to issues that were detected by the devices across the network. Now with additional processing power generated by AIoT, organizations can go one step further to being truly proactive, rather than reactive. This happens because AI on the edge uses predictive maintenance to preempt potential failures and events.

 

By recognizing the patterns in large datasets that it’s able to process, AIoT provides accurate feedback and insights to empower teams’ decision-making. Rather than just using data to uncover what happened in the past, AI on the edge is able to predict future scenarios and ways to make processes more efficient - driving teams toward greater operational efficiency.

 

Siemens leads the way when it comes to using predictive maintenance and now it’s leveraging AIoT to further these efforts. By using edge computing to complete process-oriented data analysis, the company is able to anticipate machine failure up to 36 hours ahead of time and thus reduce the downtime of its production facilities. This has resulted in cost savings of up to €12,000 per machine.

 

Clearly, AIoT is already proving to be a seriously valuable technology for companies’ bottom lines. In fact, 92% of senior leaders that are involved in IoT project decisions say that AIoT value has exceeded their expectations.

The intelligent edge allows for increased autonomy

Thanks to an increase in processing power of AI algorithms being run at the edge, intelligent decisions can be made in shorter time spans and with a fraction of the power needed than when sending data to the cloud. This is allowing for the rapid development of autonomous robots, devices, and systems.

 

For example, Boston Dynamics’ “Pick” robot is a vision-processing solution that uses deep learning to enable building and depalletizing of mixed-SKU pallets.

 

Drone traffic monitoring also leverages AIoT to identify where adjustments to traffic can be made to reduce congestion. The ET City Brain can detect accidents, illegal parking, and can change traffic lights to help ambulances get to patients who need assistance faster, and has led to a decrease in traffic by 15% in Hangzhou, China.

 

Ultimately, the more we empower devices with autonomy, the further we drive productivity as human time is freed up to complete more value-adding activities.

 

From automotive to robotics, AIoT is already having a huge impact across sectors. By integrating AI and IoT, teams are driving operational efficiency, saving on resources, and enhancing their own capabilities as the humans building the technologies. While we can’t predict the future of AIoT, what’s for certain is its ability to empower humans and drive productivity at a rate unforeseen - until now.

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