Let’s take a deep dive into the subject that’s on everyone’s lips: Artificial Intelligence, specifically Machine Learning.

Firstly, what is Machine Learning? 💡

Machine Learning is a field of science; a sub-category of Artificial Intelligence. It refers to computer systems that learn or optimize automatically without explicit instructions. Machine Learning algorithms can also identify patterns in data and predict the properties of new data. “Data” can consist of numbers, words, images, or statistics.

Machine Learning uses any data stored digitally. By detecting patterns in the data, the algorithms learn and improve their performance in a specific task.

In short, Machine Learning algorithms autonomously learn to perform a task or make predictions from data to improve performance over time. Once trained, the algorithm can find patterns in new data.

Machine Learning techniques are used for many tasks, for example: detecting spam in incoming e-mails, recognizing various objects in images, and translating text into another language.

 

So, what’s the difference between Machine Learning and Artificial Intelligence? ⚖️

Artificial intelligence (AI) refers to a system that can act rationally but that is not animal or human (not a “natural intelligence”).

AI can take many forms, such as speech recognition, natural language processing (NLP) and robotics which make mechanical systems move.

How about Machine Learning in the industry? 🏭

Machine learning has many applications in industry. Here are just a few:

✅ Cost reduction

Machine Learning improves productivity by highlighting better predictive and preventive maintenance strategies. With Machine Learning, manufacturers can anticipate required maintenance, potentially avoiding unexpected (and costly) downtime.

✅ Alleviating labor supply issues

The manufacturing industry is facing a global labor shortage. AI technology, including Machine Learning, can support the human workforce on the factory floor. These technologies increase the capacity of your workforce.

✅ Improving quality and detecting defects

Rapid defect identification and prevention requires constant monitoring, testing and improvement. Machine-Learning-based analysis and identification will help manufacturers leverage these data science tools to alert them when a defect is likely to occur.

✅ Achieving sustainability results

Manufacturers face the challenge of producing more while using fewer resources. Whether you’re looking to save energy or convert to using more renewable resources, AI and Machine Learning can help you get there.

 

 

Here are some of the industries integrating machine learning into their know-how:

📌 Health

In the field of health, Machine Learning is used to make more accurate predictions. Predictive analysis allows for earlier diagnosis and, therefore, a reduction in mortality.

📌 Digital Manufacturing

Machine Learning now enables predictive maintenance in manufacturing. It is possible to predict desired or undesired events before they occur. This computer-aided method saves time and cost. Some defects may never be discovered in time to avoid damage or downtime by a human.

📌 Retail

The retail industry uses Machine Learning technologies to provide customers with a more personalized service. By collaborating with manufacturers and advertisers, companies in the sector can develop a fully personalized experience for the end user. Such an experience allows the customer to better understand a product or content before making a purchase, helping to build brand loyalty. Algorithms can learn about each customer to tailor the experience to their personal preferences.

📌 Logistics

Supply chain managers are turning to Machine Learning to optimize their inventories and identify the best suppliers. More and more companies are also using it for resource planning, risk management, improving customer satisfaction and calculating transport costs.

 

And what about Fairmat? ♻️

Our ambition in the next few years is to use Machine Learning in several areas of our production chain.

🔎 Defect detection

The cameras on our robots are trained to look for perfect parts. When they detect something unusual, they report it, allowing us to identify defects.

🔎 Predictive maintenance

With the data collected during production, we can create patterns that reproduce themselves, allowing us to detect and anticipate anomalies and breakdowns. For example, we can optimize the cutting process for materials to be recycled by predicting wear and tear on the blades.

🔎 Computer-aided design

The computer informs the system that there are biases that need to be corrected in order for it to function optimally.

 

If you have an undisguised talent for AI and aspire to create Machine Learning processes in the service of sustainability, come and join FAIRMAT, we are looking for talent to grow our teams. Find all our job offers here.