What is Machine Learning?

What is Machine Learning?

Machine learning refers to a specific approach within AI: instead of fixed rules, ML follows a learning process. This means that ML models are not explicitly programmed but instead learn patterns or regularities independently using sample data. An ML model can use past data to identify which factors led to certain results and derive predictions for the future. A subcategory of machine learning is deep learning, which uses neural networks.

Background and context

The roots of machine learning go back to the early days of AI research. The term “machine learning” was coined in 1959 by Arthur Samuel, researcher at IBM. Over the decades, the concepts have continued to evolve, from simple decision trees to modern deep learning networks. Improved computing power, huge amounts of data, and new algorithms have led to major advances in the last 10 to 15 years. According to a Fraunhofer study, a leading German applied research institute, the rediscovery and application of neural networks in the 2010s was a catalyst for the breakthrough in deep learning. Today, machine learning is considered a key technology of the digital economy.

How does Machine Learning work?

Application examples

Machine learning is now used in practical applications in almost all industries. Especially for INFORM customers in logistics, production, or workforce management, there are many scenarios in which ML creates added value. Examples include:

• Fraud detection: Financial institutions use ML to detect suspicious activity in transactions. Algorithms learn typical fraud patterns from historical data, such as unusually high debits or deviating user behavior. They alert users as soon as new transactions indicate such patterns. This allows fraudulent payments to be detected in real time.
• Production: In industrial manufacturing, ML ensures greater efficiency and adherence to delivery dates. Based on historical production and delivery data, accurate forecasts can be made, for example, when certain components will arrive or when machines need to be serviced. This allows bottlenecks to be identified early on and the production process to be optimized.
• Workforce management: When planning staffing requirements, ML can learn from past work schedules and sick leave reports to predict future bottlenecks. This allows duty rosters to be optimized. Understaffing and overstaffing become less common, personnel costs decrease, and the workload is distributed more evenly.

Advantages and Challenges

What are the advantages of machine learning?

Machine learning offers numerous possibilities:

• Automated pattern recognition: ML analyzes large amounts of data and recognizes complex relationships that humans would miss.
• More accurate forecasts: Well-trained models deliver more precise predictions, e.g., for sales, demand, or maintenance requirements.
• Increased efficiency: Recurring tasks can be automated, speeding up processes and reducing the workload for employees.
• Learning ability: ML models continuously improve through new data and adapt to changing conditions – without manual code adjustments.

What are the challenges or disadvantages?

Despite its many advantages, ML also has its hurdles:

• High data requirements: Large, high-quality data sets are necessary for reliable results. For small amounts of data, classic methods such as decision trees are often more suitable.
• Lack of transparency (“black box”) in complex models: Deep learning models in particular are difficult to understand, which can make trust and control difficult.
• Risk of bias: If training data is flawed or unbalanced, ML models can adopt and reinforce stereotypes.
• Implementation effort: Setting up ML solutions requires specialized know-how, suitable IT infrastructure, and often adaptation of existing processes.

FAQ about Machine Learning

What is the difference between machine learning and artificial intelligence?

Machine learning is a subfield of AI. AI also includes systems based on hard-coded rules, while ML specifically refers to processes in which programs learn from sample data.

What types of machine learning are there?

In practice, there are three main types of ML: supervised learning (with known correct results), unsupervised learning (the model finds patterns independently), and reinforcement learning (the model learns through reward and punishment).

What is machine learning used for?

Machine learning is used today wherever insights or predictions are to be gained from data. Examples range from image and speech recognition to recommendation systems (e.g., personalized product recommendations). In companies, ML is used, among other things, in demand forecasting and supply chain optimization.

Does machine learning require a lot of data?

As a general rule, the more and better data available, the more powerful an ML model will be. Many successful applications rely on very large amounts of data – think of millions of images for image recognition. However, the amount of data required depends heavily on the task at hand and the algorithm. There are also models that can manage relatively little data, provided it is of high quality, or that benefit from existing knowledge through techniques such as transfer learning. The decisive factor is data quality: a moderate but clean and representative data set is often more valuable than a huge collection of unadjusted data.

Important: Machine Learning is not the same as Data Mining. While Data Mining focuses on uncovering hidden patterns and relationships in historical data, Machine Learning aims to build models that learn from data and automatically generate new predictions or decisions. Although both methods complement each other within the data process, they serve different purposes and play distinct roles.

Conclusion

Machine learning has become an indispensable technology for businesses. ML enables more efficient processes, more informed decisions, and even new business models – from the automation of repetitive tasks to the real-time optimization of complex processes. Despite some challenges, the advantages outweigh the disadvantages in many cases.

Would you like to learn more about how machine learning can help your business move forward? INFORM will be happy to support you on your way to finding the right AI solution. Contact us now!