Topics such as artificial intelligence, blockchain, and automation are shaping the everyday lives of companies. Machine learning, in particular, is attracting increasing interest. At present, relatively few companies use this technology, but it is becoming increasingly popular, and more and more applications are based on machine learning software. Here, we explain how machine learning works and the opportunities that machine learning offers.
Definition of Machine Learning
Machine learning is a subfield of artificial intelligence. It is a technology that identifies patterns and regularities and derives appropriate solutions. It is based on existing databases that are required for pattern recognition. The technology thus generates artificial knowledge based on the experience already acquired. Any knowledge acquired can be generalized and thus applied to other problems. This approach can also be used to process and apply unknown data quickly.
However, this machine-learning process requires human intervention. The human user must provide relevant data and appropriate algorithms to the appropriate systems. In addition, rules for data analysis and pattern recognition should be defined and documented. Once these principles are in place, systems can identify, extract and assemble the relevant data. This will also allow predictions to be made based on previous analyses. Machine learning can be used to calculate the probabilities of different scenarios. Companies can also use this technology to adapt to current market developments. Finally, machine learning can also be used for process optimization.
How Does Machine Learning Work?
How machine learning works is based on human learning. Humans learn by distinguishing and repeating activities. Repetitive pointing can help people distinguish one object from another. Machine learning uses a similar approach. A computer can identify and distinguish between objects by following the programmer’s instructions and inputting the appropriate data. The provision of relevant data plays a special role in this learning process; the system can also learn the difference between a human and another object and make decisions based on this knowledge.
The programmer acts as a teacher in this process, providing continuous feedback to the machine. The algorithm developed uses this feedback to adapt and optimize the model. This also means that the model is adjusted and optimized with each additional set of data fed into the system. The goal is to distinguish between objects and people clearly. Machine learning goes one step further and allows rapid adaptation to the current situation. In practical use, it can react quickly to contextual circumstances and adapt performance accordingly.
What Are the Different Types of Machine Learning?
Algorithms are important in machine learning as they are key to pattern recognition and subsequent decision-making.
In theory, machine learning can be divided into several categories:
- Active learning
- Reinforcement learning
- Unsupervised learning
- Semi-supervised learning
- Partially supervised learning
In active learning, an algorithm responds to input by asking predefined questions to produce relevant results. The algorithm selects the questions based on the relevance of the results. The source of the data is irrelevant. Data can be available online or offline. Data can be reused for the learning process.
Reinforcement learning is based on the principle of reward and punishment. The algorithm receives instructions on how to react to different situations through negative and positive responses.
In unsupervised learning, no target or reward is set before the learning process starts. The emphasis is usually no group learning. The algorithm generally distinguishes and structures the available data according to independently defined characteristics. For example, a machine might classify objects according to their color.
The opposite of this process is supervised learning, where patterns of samples are predetermined. The underlying models are then refined to classify the information further. This means that the system learns from pairs of input and output data. In the learning phase, the programmer determines the appropriate values for each input data and thus contributes to the learning process. Over time, the system can identify the relationships in the data.
Semi-supervised learning is based on separate supervised and unsupervised learning approaches and is a mixture of the two.
Advantages of Machine Learning
Machine learning is designed to help people work more efficiently and give them more freedom to be creative. For example, technology can help organize and manage large databases or take control of repetitive, meaningless tasks. Machine learning can also help people process data and prepare, store, and preserve paper documents.
Self-learning machines can also perform very complex tasks. This includes, for example, identifying errors or predicting future damage. In machines, in particular, this approach opens up unknown applications and helps to improve treatment methods. The real focus of machine learning is the evaluation and processing of large amounts of data.
Applications of Machine Learning
The applications of machine learning are almost limitless. People are no longer aware that this technology works behind the scenes of an application. Machine learning acts as a link between the product and the end user. In particular, streaming providers Amazon and Netflix use machine learning to optimize their offerings. Social network Facebook uses machine learning to tag people in the pictures it uploads. Facebook is currently the largest database of facial data. The available data is typically used to optimize visual recognition further.
On the web, machine learning enables spam detection and the development of appropriate spam filters. Machine learning can also distinguish between individuals and robots in online activities. To prevent interactions with robots, the technology can identify robots based on their patterns and prevent further interactions. Finally, digital voice assistants also use machine learning to recognize speech and text. Technology can prevent fraud, particularly in the financial sector.
Conclusion
Machine learning is a megatrend currently attracting the digital world’s attention. In particular, the growing importance of big data has given machine learning a big boost. Machine learning consists of computers collecting large amounts of data and searching for results. It does not matter whether the data is structured or unstructured. The available data can be analyzed and sent quickly and with relatively little hardware to learning algorithms. Machine learning is the only way to categorize, evaluate and classify large amounts of data into context through rapid pattern recognition.
