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    Industry 4.0

    What is Machine learning? What kind of applications does it have?

    Artificial intelligence has been a topic of concern in recent years. Among them, machine learning has been widely used in many fields and has brought a more effective, smooth, and safe experience to the public. How does this technology work? What other application cases are there?

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    What is Machine learning?

    Machine learning is a type of artificial intelligence. Artificial intelligence mainly refers to systems or machines that simulate human intelligence. Machine learning is often proposed and discussed together, but machine learning focuses more on building a system that learns and improves performance based on data. These two have different meanings.

    Machine learning is regarded as a Partial of artificial intelligence. Its algorithm builds mathematical models based on sample data and can make predictions or decisions without explicit programming. It is currently used in many applications, such as email filtering, machine vision, or even the use of banking transactions, online shopping, or social media in life, these are more difficult to rely on traditional development algorithms, but machine learning can bring a more effective, smoother and safer user experience.


    How Does Machine Learning Work?

    Machine learning operates through various types of learning models, utilizing different algorithms based on the nature of information and desired outcomes. There are four main learning models in machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Depending on the type of data and expected results, a combination of algorithms are applied within these four models.

    Algorithms are primarily used for tasks such as classification, pattern recognition, and prediction, aiming to make accurate judgments. When dealing with complex and predictive data, one or multiple algorithms can be chosen to achieve the highest level of accuracy.


    • Supervised Learning: In this learning model, the machine is trained using labeled examples that consist of paired "inputs" and "outputs," where the outputs represent the expected values.


    • Unsupervised Learning: In this learning model, there are no reference examples provided. The machine analyzes the input data, which is mostly unlabeled and unstructured, to identify patterns and correlations using relevant and accessible data.


    • Semi-Supervised Learning: In many cases, the available data is mostly raw and unstructured. This learning model is a preferred solution where a small amount of labeled data is used to enhance the unlabeled dataset, resulting in significant improvements in learning speed and accuracy.


    • Reinforcement Learning: This model does not involve reference answers. Instead, it relies on inputting permissible rules, actions, and potential terminal states. When the desired objectives are fixed, the machine can learn through example-based learning. However, when uncertainty arises, it learns through experience and rewards.

    Types of Machine Learning

    Machine learning algorithms can currently be divided into supervised and unsupervised learning, both of which learn and predict data differently.


    • Supervised machine learning: This is the most common type. It will guide and adjust the algorithm first, so that the algorithm can make a conclusion, just like when a child learns vocabulary, he uses the pictures, words, and phonetics in the book to memorize. The algorithm of supervised learning is also completed through the marked and pre-defined data as the training medium.


    • Unsupervised machine learning: This method is relatively independent and defines complicated processes and models by machine learning. People do not continuously provide detailed information. Unlike supervised learning, which is trained by unmarked or undefined data

    Machine Learning Use Cases

    1. Virtual assistants: Through voice inquiry, personal virtual assistants will help people find the information they want. For example, Siri is one of them. It can not only help to find information, but also give instructions to complete specific tasks. Virtual personal assistants can work based on the collection and improvement of information so that the data can be tailored according to preferences, and then developed into various platforms.


    2. Traffic prediction: It is believed that you often use GPS services. When our location is saved, a map of the current traffic will be constructed, so we can know whether there is a traffic jam on the map. When using a car-hailing or rental car service, prices are also estimated according to peak or off-peak hours in this way during, so machine learning is essential for the industry in this regard.


    3. Surveillance system: In the past, monitoring of several cameras was always carried out manually, which was not only very difficult but also too simple for people. Therefore, with the assistance of artificial intelligence, people’s behaviors on the screen can be detected and alerted or reminders, helping to improve monitoring services, are all things that can be done after having machine learning.


    4. Social media services: When many people use social media, they are often referred to as friends, fan pages or advertisements, etc. They will also help you identify the person in the photo after uploading the photo and ask you if you want to mark it. The functions mentioned above are all services that machines can provide after continuous learning.


    5. Email filtering: The reason why emails can block spam or malicious emails is that the machine learning-driven security program system understands the encoding model, thereby detecting spam or malicious letters, and providing protection against them.


    6. Online customer service: Many websites have options for in-site navigation or customer service, and most of them will reply in the form of chatbots, which can help customers solve relatively simple problems, and this function can only be achieved by relying on machine learning methods.


    7. Search engine results: Many search engines will use machine learning to adjust the search results displayed by each person, and will also record the dwell time on an individual's website earlier in the search results so that the search results can truly match the information people want to get.

    The potential of machine learning

    In addition to the above-mentioned daily applications, machine learning has great potential for enterprises, especially because enterprises have a lot of data and can make use of them. At this time, efficient workflow is required, otherwise, it will be impossible to make use of machine learning, therefore, before the introduction of this technology, the enterprise can discuss and plan first, so that the expected benefits will be obtained after the introduction.

    Main image photo by Adobestock

    References Market Project / OCI

    This article was created by Machsync, please do not distribute, give or sell for commercial use or in any way. Any use such as reprinting, excerpting, copying, and creating images is prohibited without permission. For reprinting, please contact Machsync.