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This blog is the second in a four-part series on the beginner’s guide to Industrial Artificial Intelligence applications.
Understanding the inner workings of your Industrial Artificial Intelligence (IAI) system is crucial if you want it to add measurable value to your manufacturing operations. In this blog, we will dig into one important aspect of every AI — the inputs, a.k.a. your data. Including the right type and the right amount of data is key to success when it comes to AI.
To understand the full potential of an IAI system, it’s important to look at what data, assumptions, rules, and shortcuts feed into the system during its training, testing, development, and deployment stages. You won’t be able to know or anticipate everything that goes into AI from your system, but understanding these things (to the extent possible) can give you a better understanding and more realistic expectations of how your AI will behave.
This is your classic “garbage in, garbage out” problem. Not all data is created equal, and bad data will result in bad decisions from your IAI tool. Be sure that the training data you’re feeding into the system is relevant, accurate, and connected to the task at hand. It needs to have meaningful relationships to the problem you want to solve.
If you're using data that doesn’t match up with real-world conditions, you're in for trouble. Look for these red flags:
Industrial AI holds tremendous potential for revolutionizing various sectors by automating complex processes and providing actionable insights. However, the journey to effectively implementing IAI systems holds many challenges. When assessing the adequacy of your data, ask two key questions:
Let’s set one thing straight: More data isn’t always better. Bigger datasets don’t automatically mean better insights. What's important is the quality and relevance of the data. Too much irrelevant data just muddies the waters, while the right, focused data will help the AI system make better, more informed decisions.
Think of it this way: More data is like more bricks for building a house, but it’s the quality of those bricks that determines how strong the house is. If you have the wrong kind of bricks — or too few — you won’t have a solid structure. The same goes for your AI.
As a small to medium-sized manufacturer, integrating IAI into your operations can offer great benefits, but it’s important to approach it thoughtfully. You don’t need to be an AI expert to understand the basics of how it works for your business — just follow these steps:
By focusing on quality data, understanding what the AI is doing with that data, and setting clear expectations for what the tool will deliver, you’ll be in a better position to make informed decisions and get the most out of your IAI investments.
Ultimately, the successful deployment of an IAI system hinges on a balanced approach to data collection, preprocessing, and evaluation. By systematically addressing the key elements of your IAI, you can make informed decisions about the value and feasibility of investing in these systems. Be sure to check out other blogs in this series including Simple Questions for Assessing Industrial Artificial Intelligence Applications.
If you have questions about Industrial AI or other technologies, contact your local MEP Center. Local experts can discuss available technologies and help you assess what technologies will align with your individual business needs.