How many of the four misunderstandings about industrial AI did you fall for?



Dec 4, 2023

Artificial intelligence (AI) in the industrial field is rapidly emerging, helping manufacturers maximize normal operating time and identify lost production and defects through equipment monitoring and preventive maintenance plans. Its predictive ability can also be used to create learning and predictive demand models.

However, at the same time, there are also some common misunderstandings about the application of AI. According to IBM’s 2022 Global AI Adoption Index report, 34% of survey respondents (approximately 2550 companies worldwide) stated that a lack of AI expertise hinders implementation efforts. Therefore, this article aims to clarify the four common misunderstandings of industrial AI among the public, so that everyone can have a clearer understanding of the practical application and potential of AI technology in the manufacturing and logistics industries.

Misconception 1:

AI terms are interchangeable and insignificant

Some people mistakenly believe that terms such as industrial AI, machine learning, and deep learning can be used interchangeably. In fact, each term has its unique meaning and scope of application. Industrial AI is a broad category that includes various technical terms. Understanding these subtle differences is the first step in evaluating the applicability of technology.

Below 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 computing technologies aimed at imitating human decision-making activities, using image recognition, natural language processing, and other technologies to automate tasks that are difficult for human intervention to handle.

Deep learning: An AI technology aimed at achieving automation for complex and highly customized applications. By using a graphics processor (GPU) for processing, it is possible to quickly and effectively analyze a large number of image sets to detect subtle defects and distinguish between acceptable and unacceptable anomalies.

Edge learning: AI technology designed for ease of use. Using a set of pre trained algorithms for processing on the device, i.e. “edges”. This technology is easy to set up, requires a smaller image set (as low as 5 to 10 images), and requires a shorter training period compared to traditional deep learning based solutions.

Misconception 2:

AI will replace human work

The goal of AI is not to replace humans, but to collaborate with humans to improve work efficiency and quality. AI can automate tedious tasks, allowing employees to focus on more creative and strategic work. This is an efficient and beneficial tool that can also help solve labor shortages.

Therefore, this technology is gradually being applied more widely in the manufacturing and logistics industries to address persistent labor shortages and other long-standing issues. The combination of AI and robots can achieve tasks such as object avoidance and ground surveying, thereby completing the delivery of goods in various facilities. The combination of AI and machine vision systems can undertake essential repetitive quality assurance tasks, including missing detection and inspection of components.

Misconception 3:

Industrial AI requires thousands of images and large datasets

Some people mistakenly believe that applying AI in the industrial field requires massive datasets and thousands of images. In fact, there are many different types of AI technology, and some applications do require large datasets, but not all cases require such a large amount of data. For certain applications, using limited datasets and experience can also make effective predictions and decisions.

The deep learning and edge learning technologies launched by Cognex are representative of the above two situations:

Deep learning is renowned for its excellent ability to handle complex tasks. This technology is suitable for processing large image sets with a large amount of details and significant changes, and is also an ideal choice for complex or highly customized applications. Due to the numerous details involved in these applications, a large amount of image training and model execution are required in the early stages to achieve automation for complex tasks.

Edge learning is designed for ease of use. It uses a set of pre trained algorithms to process at the “edges” of the device or data source. By embedding application requirements knowledge into neural network connections in advance for training, a large amount of computational load is eliminated, so there is no need for a GPU. With only 5 to 10 images, training deployment can be completed in minutes, quickly expanding application scale and easily adapting to changes.

Misconception 4:

Deploying AI solutions requires a team of professional scientists

Although the development and design of AI require a certain level of professional knowledge, modern AI solutions have become easier to deploy. Especially with Cognex’s edge learning technology, it has greatly simplified the deployment process. Cognex’s edge learning solution can run within smart cameras. This smart camera is equipped with an integrated light source, autofocus lens, and a powerful sensor, all of which work together to provide precise detection functions.

Operators do not need to have professional knowledge in deployment, even non visual experts can train edge learning tools and generate accurate results within minutes. This makes edge learning a feasible automation solution suitable for everyone from machine vision beginners to experts. By eliminating reliance on complex infrastructure and reducing the demand for professional knowledge, Cognex’s edge learning technology enables more people to utilize AI technology to improve work efficiency and quality.

AI is not a short-lived trend, nor is it an exclusive technology that is only applicable to specific markets, but rather involves a wide range of fields and provides multifaceted assistance to industry. With the continuous development of technology, AI has become more convenient. After on-site testing in the manufacturing and logistics industries, it provides support for simplifying quality control, improving product traceability, and identifying production defects early.

By clarifying the common misunderstandings about industrial AI mentioned above, we hope you can have a more accurate understanding of the practical applications and potential of AI. Cognex’s AI technology, especially edge learning solutions, is helping businesses improve detection efficiency, reduce production costs, and improve product quality in an unprecedented way by eliminating reliance on complex infrastructure and large datasets, as well as reducing the demand for professional knowledge.
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8 thoughts on “How many of the four misunderstandings about industrial AI did you fall for?”
  1. Dear friend, I feel that in the future, technology will be very advanced and will help us improve our quality of life.

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