{"id":341141,"date":"2022-03-24T14:46:15","date_gmt":"2022-03-24T13:46:15","guid":{"rendered":"https:\/\/www.optimal-systems.de\/?p=341141"},"modified":"2026-07-03T11:59:54","modified_gmt":"2026-07-03T09:59:54","slug":"ai-machine-learning-and-deep-learning-in-a-nutshell","status":"publish","type":"post","link":"https:\/\/www.optimal-systems.de\/en\/digitalization\/blog\/ai-machine-learning-and-deep-learning-in-a-nutshell","title":{"rendered":"AI, Machine Learning, and Deep Learning in a Nutshell"},"content":{"rendered":"\n
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AI, Machine Learning, and Deep Learning in a Nutshell<\/h1>\n\n\n\n

By Viola Ganter<\/p>\n<\/div>\n<\/div>\n<\/div><\/div><\/div>\n\n\n

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About 30% of activities in 62% of German occupations could be automated through Artificial Intelligence<\/a>. But, aside from humanoid robots in sci-fi movies, what exactly is Artificial Intelligence? Is there a difference between terms like Machine Learning and Deep Learning, or are they just buzzwords for the same thing? We explain the basic concepts and how they can be applied in business optimization processes.<\/p>\n\n\n\n

What is Artificial Intelligence?<\/h2>\n\n\n\n

Artificial Intelligence (AI) is a broad term for a variety of concepts, the definition of which varies across industries and research fields. In layman\u2019s terms, AI is often described as machines mimicking human cognition. This, however, both under- and overestimates what AI can do. A more accurate description of Artificial Intelligence<\/a> is that of a system that \u201cperceives\u201d its environment (meaning data input) and takes action to maximize its chance of achieving its goal.<\/strong><\/p>\n\n\n\n

Areas of Artificial Intelligence range from following a basic set of logical rules to even completing small creative tasks. However, most business applications in AI are based on Machine Learning, which is why the terms Artificial Intelligence and Machine Learning are often used interchangeably in the business context.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n

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Deep Learning is a form of Machine Learning.
Both are a type of Artificial Intelligence.<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n\n
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What is Machine Learning?<\/h2>\n\n\n\n

Machine Learning (ML) is a specific kind of AI that reacts to its environment without needing individual rules for every new situation.<\/strong> At the core of ML, there is a mathematical function (or several). The objective of that function is to make stochastic predictions based on input data. A Machine Learning algorithm \u201clearns\u201d how to make the best prediction by adjusting the parameters of the function to the input data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n

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For a simplified analogy: Imagine a small child learning the concept of a bird. At first, it might refer to any animal as \u201cbird\u201d. After a while, however, the child will have learned to pay attention to whether the animal in question has feathers, a beak, or a high-pitched tweet. It will have learned that these characteristics are important to determine the probability of an animal being a bird. Other factors, such as whether or not it has eyes, are less important in making that determination. Machine Learning works in a very similar way.<\/p>\n\n\n\n

Examples of Machine Learning in Enterprise Information Management<\/h3>\n\n\n\n

Machine Learning has many uses, but is particularly helpful for digitizing existing paper documents. Here are some examples of how Machine Learning is making business processes easier:<\/p>\n\n\n\n