Artificial Intelligence Commentary

The IoT and Artificial Intelligence (AI) are two very distinct concepts that complement each 

other. When operational, IoT devices create and gather data. In turn AI analyzes the data to 

provide insights, interpretation, and decision making that can then and improve items on the IoT 

device such as its efficiency and productivity. Artificial intelligence (AI) can be defined as a 

collection of technologies and approaches that allow a machine to perceive its environment and 

take actions towards a specific goal. It encompasses several different technologies that give 

computers human-like abilities of perception. 

Most of the AI systems today are machine learning (ML)-based systems, which allow computers 

to learn data patterns in a supervised or unsupervised manner, and then apply these learnings 

to make predictions, classify data, recognize objects or images, and understand speech or text. 

Other techniques that are often used in AI systems include deep learning (DL), natural language 

processing/understanding (NLP/NLU), computer vision (CV), and machine reasoning (MR)

Within the manufacturing industry, AI is being used in a variety of environments. These range 

from the factory floor, where it improves the production and distribution of manufactured goods 

and enhances safety, to the back office, where it streamlines administrative tasks and bolsters 

customer service efforts. AI is also being incorporated into manufactured goods to allow others 

along the value chain, including distributor, retail, and service partners, to leverage the 

intelligence provided by the technology to provide better customer service. In addition, these 

partners can use AI to improve aspects of product design and lifecycle management. 

IoT technologies in industrial markets together with components like sensors, data storage and 

integration, data analytics, and machine learning, can be applied to SCADA systems to improve 

interoperability and coordination among different machines. The sensors collect new data from 

various equipment and continuously feed the data into the analytics. This way, machine learning 

algorithms can learn from past data and fine-tune the settings on different machines for 

thousands or even millions of cycles to reach the optimal point of the entire system. The use of 

AI within the manufacturing sector is being driven by specific enabling market factors that 

include the digitization of data, the development of IoT networks, and the steady improvements 

in ML and DL algorithms. AI technology introduces scale and efficiency and is best applied to 

two types of problems:

1. Data analysis and subsequent predictive recommendations and actions: ML and DL 

technologies excel at analyzing massive datasets very quickly. They can complete data 

analysis computations much more quickly than manual human analysis or hardcoded 

computer analysis.

2. Routine, redundant tasks: AI technologies are successfully handling redundant, linear 

thought-focused tasks (clerical work, order taking, food service), freeing up human 

resources to focus on higher value, human-exclusive skills (creative thinking, problem 

solving, interpersonal skills, emotional intelligence, reasoning, negotiation, and decisionmaking).

Within specific vertical markets (manufacturing, health care, energy, and transportation) there 

are several use cases that leverage the power of AI to deliver ROI while employing ML, DL, 

NLP, and CV approaches that are commonly used across vertical segments. A list of some of 

these use cases are provided below.
1. Digital Twins. A digital twin is a digital representation providing the elements and the 

dynamics of how a device or ecosystem operates and lives throughout its lifecycle. Digital 

twins combine sensor data with ML and software analytics, which are then used to create 

spatial graphs that provide a digital simulation model that is updated and changes in real 

time in tandem with their physical counterparts.

2. Energy Management. Within manufacturing, the consumption of energy remains a primary 

cost and concern for plant managers and the key decision makers of the company. While 

the cost of energy may be variable a company’s energy use is fully within its control. 

However, in order to better assess and control energy consumption within a manufacturing 

environment, machines must be equipped with sensor technology. Energy usage must be 

tracked at a granular level in order to assess key ratios, such as energy consumption versus 

productivity. The use of AI can make this tedious and data-intensive process much more 

efficient and effective.

3. Medical Image Analysis. Analyzing images is a strong application for DL and CV within the 

realm of patient data processing. DL is now being applied to automate the analysis and 

increase the accuracy, precision, and understanding of images down to the pixel. Some of 

the more common applications include 3D CV (images analyzed and rendered into detailed 

3D models), auto grading of eye diseases, and detection and segmentation of radiology 

images.

4. Safety Enhancement in Buildings. Employers have an incentive to ensure better 

compliance with safety standards and protocols. One example of how DL is being used to 

help ensure better compliance includes tools that allow employers to leverage photos and 

videos to identify workers who are missing hard hats, gloves, or other safety equipment.

5. Street Lighting. Street lighting is an essential element for any city. In addition to providing 

better visibility for pedestrians and motorists, it adds a feeling of safety and security and can 

often deter criminal activity. Smart cities are adding AI capabilities to street lighting, which is 

designed to not only provide lighting, but also perform other tasks by incorporating CV, ML, 

and IoT connectivity. Streetlights can be equipped with an array of sensors to monitor traffic 

flow, as well as send signals to traffic lights and other traffic control devices. 

Manufacturers that have successfully incorporated AI technology generally have been able to 

achieve the following:

• An understanding of how analytics and AI can work together: Data analytics can and should 

be used to augment and support AI. 

• An understanding of the goals and benchmarks needed to assess AI use cases: AI leaders 

need to be able to review the output from AI use cases and ensure that proper processes 

are in place for confirming or overriding questionable results. 

• A modicum of trust in AI: All stakeholders need to have confidence that AI can deliver 

benefits if properly deployed.

• A strong culture of oversight: Regular oversight over the use of AI is critical to ensure that 

algorithms are delivering the benefits they should, while also remaining in compliance with 

applicable regulations, is a major key to success. Because the technology is still relatively 

new, stakeholders are much more likely to stay engaged if they are confident that there is 

proper oversight occurring on a regular basis.