Artificial intelligence in industry.

Using business AI applications in industry analysed by Henry Martel, Field Application Engineer, Antaira Technologies 

If there is an overarching technology theme to 2023 it is Artificial Intelligence. Despite its recent hype, however, Artificial Intelligence or “AI” is not a new concept. Its roots date back more than 70 years to a young British mathematician named Alan Turing. In 1950 Turing suggested that since humans have the innate ability to combine available information with reason to solve problems, why can’t machines?

Fast forward to the 1990s when Artificial Intelligence went from science fiction to science fact, thanks to landmark advancements in computational processing power, rule-based algorithms, neuroscience, and data storage capacity.

Today, artificial intelligence is being fully realized in industries as diverse as entertainment, manufacturing, finance, retail, and agriculture. AI can be found in autonomous vehicles, the optimization of pricing based on consumer and customer behavior alone, interactive chatbots like ChatGPT, automated financial planning, healthcare management, cybersecurity, Amazon product recommendations, and, countless other uses.

At the foundational level, AI is about extracting value from historical and real-time data. Data is a commodity that we have virtually no limit in our capability to collect, whether it's from sensors, cameras, or the importing of internal and external datasets. AI analyzes patterns hidden within “big data” in mere seconds and displays human-like cognitive processing in the form of reasoning, deep learning models, planning, and creativity to gain more contextual knowledge. For instance, the LinearFold AI developed by Baidu in 2020 was able to predict the RNA sequence of the COVID-19 virus in only 27 seconds, making it possible for researchers at that time to develop a vaccine.

Artificial Intelligence Subsets.
As with the human mind, AI is always learning. Each time AI performs a round of data analysis and processing, it tests and measures its performance and then uses those results to develop additional expertise in helping gain insight. This ability, better known as the AI subset Machine Learning, is constantly discovering new patterns and generating awareness from collected data. Other subsets of AI are:

• Neural Networks that utilize nervous system science,
• Deep Learning, which is like Machine Learning but uses a neural network of three or more layers,
• Robotics enabling advanced robot control and the robot's natural interaction with humans, and
• Computer Vision that trains computers to capture and interpret information from image and video data.

So how can AI improve your business? Let’s look at a few examples.

AI in Industrial Automation.
Perhaps more than any other field, Industrial Automation is a prime candidate for AI. Industrial automation already has many AI-driven systems with proven value. For example, asset monitoring in predictive maintenance is used in Industry 4.0.

Combining an existing industrial network with the capabilities of AI allows for more efficient and intelligent control of factory automation systems and business functions such as automatically adapting an assembly line to manufacture products that meet changing customer requirements. Complete inference solutions are now available that combine all the necessary hardware with ready-to-deploy AI algorithms in the cloud or in-house servers, eliminating the hurdle of having an inexperienced engineering team attempt to develop the algorithms themselves. Zero-touch AI devices connect to servers on power-up for instant configuration and real-time updates without any manual set-up by an on-site administrator. By being able to quickly and seamlessly interact with a factory’s IT infrastructure business processes, and automation systems, AI can hasten the adoption of autonomous mobile robots, collaborative robots, and computer vision systems, as well as the deployment of analytic tools including intelligent digital twins, order-controlled production, supplier selection, and predictive maintenance help drive operational efficiency and workplace safety.

AI in Agriculture.
AI in agriculture helps farmers ensure healthier food by minimizing the use of fertilizers, pesticides, and irrigation. All the while, it promotes greater crop yields and reduces the farm’s environmental impact on human resources.

Operating a farm requires collecting, analyzing, and using accurate data while monitoring hundreds of fluctuating variables that will determine a crop’s success. This data-intensive task is a natural fit for AI. AI will track and analyze variables ranging from weather patterns, hours of sunlight, planting cycles, and timing the migration of insects... to the use of fertilizers, insecticides, and irrigation systems. Data collected from in-ground smart sensors or from drones capturing real-time video streaming of fields can be integrated by AI with reports from the National Weather Service or the National Oceanic and Atmospheric Administration to make predictive analytics that assists in decision-making. AI can also predict potential yield rates of a given field before a vegetation cycle is ever started in a process known as yield mapping.

AI in Warehouse Management.

Applying AI in a warehouse or distribution center can guarantee accurate inventory data on-demand and a more complete understanding of customer requests in real-time. AI helps warehouses and distribution centers recognize their current situation by uncovering ordering patterns, tracking supply chains, and targeting areas to lower overhead costs. It also does away with antiquated Excel spreadsheets and time-consuming, overly complex formulas that always seem to come out wrong.

Modern warehouses deploy AI solutions for different technologies, such as automated robots (i.e; smart forklifts) that mimic human behavior, and software platforms for managing inventory management, material handling, processing and packaging, supply chains, and demand planning. AI interconnects all these different systems and technologies so they work in unison.

AI-driven robots, Automated Guided Vehicles (AGVs), and Autonomous Mobile Robots (AMRs) are today delivering tremendous value in warehouse operations. Robots can safely handle heavier loads than a human, and will more accurately pick, place and transport loads by following precise instructions and routes without experiencing fatigue or colliding with human workers. While they work AI-driven robots, AGVs, and AMRs actively collect data that enhances visibility across the enterprise, whether it is spotting recurring patterns that can predict possible inventory shortages, pinpointing root causes of equipment failures, or locating small, easily overlooked anomalies. Warehouse systems with AI become more innovative, faster, and more efficient in providing precisely what customers need on-time worldwide.

AI in Healthcare.
Artificial intelligence in healthcare has the potential to reshape the way patients are diagnosed, treated, and monitored, resulting in drastically improved outcomes and enabling more personalized treatments. Potential applications and business benefits of AI in healthcare are broad and far-reaching, from analyzing x-rays for early detection of disease to predicting outcomes from electronic health records. AI eliminates the need to manually record data, freeing up time otherwise spent on data entry.

In medical research, AI is being used to automatically collect patient biological data around the clock to establish massive databases. By analyzing vast amounts of clinical documentation quickly, AI helps doctors conducting research pinpoint disease markers that would otherwise be overlooked.

Industrial Ethernet Switches as AI Tools.
Leveraging AI’s competitive advantage in the business world requires mountains of data combined with high-performance networking muscle. This is typically driven by accelerators, Central Processing Units (CPUs), Non-Volatile Memory Express (NVMe) storage devices, and Network Interface Cards (NICs) connected to a GPU or PCIe Switch.

Industrial Ethernet switches also play a critical role in AI and data analytics by acting as a central station for all the connected devices to communicate with each other. Industrial PoE switches can supply up to 100W of power to Powered Devices, for instance, IP cameras, LED lighting, wireless access points, and remote IoT sensors detecting temperature, humidity, pressure, moisture, and other data shared with AI. Transmitted data collected by an industrial switch is applied to artificial intelligence and machine learning algorithms. High-speed, low latency industrial Gigabit switches are also finding a home in the distributed applications AI often requires, at times even replacing InfiniBand switches which have long been the preferred network interconnection technology for GPU servers.

As human decision-making and reaction times are increasingly proving insufficient for managing modern enterprises, AI is helping business leaders overcome shortcomings by delivering insights based on the analysis of all available information with unprecedented speed and accuracy.

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