How Can AI Reach its Full Potential?
AI vision, such as image processing with artificial intelligence, is a heavily debated subject. However, the promise of innovative, new technology has not yet materialized in many areas, such as industrial applications. Therefore, as of yet, there are no long-term empirical values for AI vision.
Even though there are several embedded vision systems on the market that make it possible to use AI in industrial settings, many facility managers are still hesitant to upgrade their applications and buy one of these platforms.
In situations where rule-based image processing has run out of options or has generally failed to find a solution, AI has already shown creative possibilities. Therefore, the question remains as to what is stopping the widespread uptake of this technology.
One of the main obstacles to the technology’s further development is user-friendliness. Thus, one requirement is that anyone should be able to create their own AI-based image processing applications, even without extensive training in artificial intelligence or the required application programming.
While AI can speed up a variety of work processes and reduce sources of error, edge computing also makes it possible to do away with pricey industrial computers and the intricate infrastructure required for high-speed image data transmission.
However, AI and machine learning (ML) operate very differently from traditional, rule-based image processing. This affects how image-processing tasks are approached and handled.
The quality of the results is now ascertained by the learning process of the neural networks used with suitable image data, as opposed to the manual development of program code by an expert in image processing.
In other words, the object features needed for inspection are no longer predetermined by predefined rules, but rather, the AI must be trained to recognize them. Additionally, the likelihood that the AI/ML algorithms will be able to identify the features that are particularly important later in operation increases with the diversity of the training data.
When combined with enough knowledge and experience, what appears to be straightforward can also result in the achievement of the desired outcome.
Errors will also happen in this application without a trained eye for the right image data. As a result, working with machine learning methods requires different key competencies than working with rule-based image processing.
However, not everyone has the time or resources to dive into the topic from the ground up to learn a set of new critical skills for using machine learning techniques.
The main problem with new technology is that it becomes difficult to believe in and place trust in such a system if they produce excellent results with little effort but the decisions can’t be checked by simply reviewing the code.
Logically, learning how AI vision functions seems imperative. Yet, without understandable, simple explanations, it is challenging to assess the results.
Acquiring confidence in a new technology typically requires the development of skills and experience, which can take a long time to acquire before one is fully aware of what the technology can do, how it operates, how to use it, and how to manage it effectively.
The fact that the AI vision competes with an established system for which the right environmental conditions have been created in recent years through the application of knowledge, training, documentation, software, hardware, and development environments only serves to complicate matters further.
Contrarily, AI still seems to be very undeveloped and raw, and despite the well-known advantages and the high levels of accuracy that can be attained with AI vision, it is typically challenging to spot mistakes.
The other side of the coin is that the algorithms’ development is hampered by a lack of clear understanding of their inner workings or by unexpected results.
