Artificial Intelligence of Things Emerges: Should You Care?
Technology convergence in the digital workplace is nothing new. Companies have been pairing technologies for many reasons, though primarily to boost productivity and, more recently, to enable new remote and hybrid workplaces. One of the newest combos (and acronyms) is AIoT (or the Artificial Intelligence of Things).
The name pretty much gives it away. AIoT is a combination of artificial intelligence (AI) and the Internet of Things (IoT).
Sound intriguing? AIoT promises to offer a host of advantages for those companies that choose to use it. The question is, is it right for you?
The AIoT Advantage
There are several benefits to pulling AI and IoT together, including better risk management, better operational efficiency, easier scalability and downtime prevention, among others. In a recent paper from Farmington Hills, Mich.-based Bosch, for instance, the engineering company explained that its global strategy with AIoT is to offer users a better way of managing data.
By connecting existing IoT systems to AI and machine learning, the company can more quickly and effectively respond to customer issues, drawing more accurate conclusions from huge quantities of data and reacting to that data in seconds. “We learn from the data and can thus improve our products and services on an ongoing basis,” the paper reads.
And that's ultimately the goal of AIoT: to create more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. Connected products provide data that can be used to improve products and applications. That's the IoT advantage.
Adding AI to the mix means data is collected, processed, stored and structured even more efficiently, which, in turn, continuously feeds algorithms and machine learning to help organizations gain new insights into what is happening inside and outside processes — in real time.
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Setting Up for Success
To take advantage of AIoT, companies must first ensure that the data they have collected is accurate. AI simulates human intelligence in machines by accessing and analyzing historical data, so without sufficient past data or with inaccurate historical data, the technology would not make accurate predictions about the future.
Michael Levy, strategy consultant at Denver-based Harbor Research, said this also applies to all aspects of AI-NLP (Natural Language Processing) algorithms, which train on text or speech data sets to associate words and sounds with their meanings, while computer vision does the same with video data.
In the case of IoT, the same is true. It isn't enough to have devices connected to the internet. For IoT to be useful, these devices need to also collect data and transmit that data through the connections that request it.
In this respect, AI seems a great fit to bring out and boost the benefits of IoT. AI's reliance on data allows it to make informed decisions, and algorithms can use the IoT data to make more accurate decisions related to real-world devices and machines.
Levy cited the example of a connected CCTV camera that can collect data (video images) of its environment. With an AI algorithm trained with video data in which images of intruders are labeled intruders and images of dogs are labeled pets, the CCTV camera can then classify images as threats or non-threats as it collects data about its environment. If it detects an intruder, it can then notify the homeowner accordingly, potentially saving lives.
In the workplace, a similar example could be of a bank's ATMs outfitted with NLP algorithms trained on data from thousands of teller transactions. With microphones and connectivity, these ATMs could detect the words withdrawal or deposit when a user speaks them, rendering it able to recognize the meaning of these words and fulfill functions accordingly, like opening up the right page for the appropriate transaction.
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AIoT Use Cases
It should be clear by now that without AI, IoT is merely a group of connected devices pumping data to users. AI offers the ability to understand and utilize that data to make the IoT strategy a lot more effective and useful for both companies and users.
Samuel Hamway, research analyst at Nucleus Research, said that AI and IoT reach confluence in two use cases:
- At the edge, devices can use computer vision and speech recognition to conduct automated processes, including quality assurance, motion detection, voice command control, guided movement and bar/QR code reading.
- After data is centralized across the endpoint ecosystem, AI can also enable system-wide and sub-process optimization through predictive maintenance scheduling, resource allocation and Real Capital Analytics-informed alerts.
“Industrial use cases are a vertical where AIoT has delivered substantial value, with organizations eliminating the need for manual equipment inspection,” Hamway said.
According to Boris Jabes, CEO and co-founder of San Francisco-based Census, there are six other ways in which the AI and IoT combination works:
AIoT can be used to automatically detect and diagnose problems with IoT devices or to predict future failures before they occur. This can help improve the efficiency of IoT operations by allowing technicians to quickly and easily identify and fix any problems.
2. Optimizing resources
AIoT can be used to optimize the use of resources such as bandwidth, power and storage. This can help reduce waste and make sure that devices are getting the resources they need. AIoT can also be used to optimize storage by automatically deleting or archiving data that is no longer needed.
3. Improving security
AIoT can also be used to improve the security of IoT devices and networks by identifying and responding to threats in real time. This can help prevent data breaches and keep systems safe and secure. Additionally, AIoT can be used to monitor network traffic and identify malicious or unauthorized activity.
4. Increasing efficiency
AIoT can be used to automate tasks and processes that are currently manual or inefficient. This can result in significant cost savings and increased productivity. For example, AIoT can be used to automatically collect and analyze data, or to manage devices and networks. By automating these tasks, businesses can save time and money while improving their overall efficiency.
5. Improving human-machine interactions
AIoT can be used to improve human-machine interactions by providing context-aware information and recommendations to users. For example, AIoT can recommend products or services based on a user's preferences or past behavior. Additionally, AIoT can automate tasks that would otherwise require manual intervention. This can save users time and allow them to focus on tasks that are more important.
6. Data management and analytics
AIoT can be used to improve data management and analytics by providing more accurate and timely insights into the data collected by IoT devices. AIoT is still in its early stages of development, but it has already begun to impact the way organizations operate.