Published on September 14th, 2020 | by Emergent Enterprise0
A.I. For Smarter Factories – The World of Industrial Artificial Intelligence
We all are recipients of the untiring work of artificial intelligence but we don’t take time to acknowledge it. Industrial businesses certainly are leveraging the powerful technology as noted in this post by Michael Sharp at Metrology News. In your company tasks and objectives, take a moment to consider how rules-based AI or machine learning can improve productivity, safety or more. Machines, devices and computers usually take over tasks that are mundane and laborious and don’t really require a human to do. Why not let AI do the work and switch the human employees to more satisfying roles?
As the digital age moves forward, it’s becoming impossible to avoid interacting with artificial intelligence (AI) systems. Computer assistants and AIs perform an ever-growing range of tasks that are broadly intended to improve our quality of life. This extends to industry as well.
But first, what do we mean by artificial intelligence? In simple terms, it’s any machine (usually a computer) that does things normally associated with human intelligence, such as reasoning, learning and self-improvement.
AI systems in industry are the same technologies you use in daily life but applied to industrial problems. The same kind of AI that makes our phone calls clearer can listen for bad blades in a sawmill. Programs built with AI like those that help us find new movies and music suited to our unique preferences can help guide designers to selecting the right materials to mix to make the perfect concrete for the job. The same math behind teaching a toy dog to walk helps manufacturing facilities plan and schedule maintenance well into the future!
When these tools and algorithms target problems in physical (non-digital) industries, they fall into the special realm of industrial AI, or IAI. The many unique needs and challenges of industry set these algorithms apart from their more broadly used counterparts. Specific industries even have special names for the adoption of IAI technologies. For example, manufacturing engineers use terms such as Industry 4.0 and “smart manufacturing.” These all reflect the growing adoption and application of AI to problems previously thought unable to be automated.
So how can the same technologies be applied to such vastly different problems and still get good results? By understanding not only the tool, but also the problem faced and the environment where it will be used!
Generally, IAI is applied to tasks that are tedious, time-consuming or simply too difficult for humans to accomplish. The goal of IAI, like any tool, is making both worker and facility more productive. As part of this, ongoing efforts at the National Institute of Standards and Technology (NIST) aim to educate and guide users towards selecting the right IAI tool for the right job.
In broadest terms, IAI tools fall into two categories: predefined rules-based tools and machine learning tools. Some tools use combinations or hybrids of these two groups, such as reinforcement learning, but most IAI tools fit one of these descriptions.
Following The Rules
Rules-based AI operates strictly on predefined rules and requirements set during its creation. These AI tools are generally easier for humans to understand, both during creation and operation. These rely on equations or sets of “if-then”-type rules that tell the machine what to do. In their purest form, these AI tools tend not to change after creation. This makes them very stable and makes it easier to know why they did what they did during operations. This type of IAI is often so simplistic in its creation and execution that some people forget it is considered AI. However, the seemingly simple ideas and methods of rule-based IAI can build up to incredibly complex and sophisticated systems.
Rules-based IAI tools are ideal for well-understood processes or environments that allow a small set of possible outcomes. Simple decision-making processes or systems that can be sufficiently modeled with simple equations represent typical applications of these tools. A simple rules-based decision engine could measure and reject machined shafts that are too long or short with very basic “if-then” rules. Another example of rules-based IAI uses equations about the physical properties of spinning equipment to identify tiny cracks in bearings.