Artificial intelligence (AI) is rapidly coming to the fore in the industrial sector, where industrial AI can help manufacturers maximize uptime with equipment monitoring and preventive maintenance programs, as well as identify lost production and defects. Its predictive capabilities can also be used to create learning and predictive demand models.

At the same time, however, the use of AI is accompanied by some common misconceptions. According to IBM’s 2022 Global AI Adoption Index report, 34% of survey respondents (approximately 2,550 companies worldwide) said that a lack of AI expertise is hindering implementation efforts. Therefore, this article aims to clarify four common misconceptions about industrial AI in order to gain a clearer understanding of the practical applications and potential of AI technology in the manufacturing and logistics industries.

Myth # 1:

AI terms are interchangeable and irrelevant

CI869

There is a misconception that terms such as industrial AI, machine learning, and deep learning are used interchangeably. In fact, each term has its own unique meaning and scope of application. Industrial AI is a broad category that includes a variety of technical terms. Understanding these nuances is the first step in assessing the suitability of the technology.

Here are some common industrial AI terms to help you quickly understand the different forms, functions, and feasibility of this technology:
Artificial Intelligence: A set of computational techniques designed to mimic human decision-making activities, using image recognition, natural language processing, and other techniques to automate tasks that are difficult for humans to handle.
Deep learning: an AI technique designed to automate complex and highly customized applications. Processing with graphics processors (Gpus) makes it possible to quickly and efficiently analyze large sets of images to detect subtle defects and the difference between acceptable and unacceptable exceptions.
Edge learning: AI technology designed for ease of use. Using a set of pre-trained algorithms, processing is done on the device, the “edge.” This technique is easy to set up, requires a smaller set of images (as low as 5 to 10 images) and requires a shorter training period than traditional solutions based on deep learning.