The issues most widely discussed during the lecture on Intelligent computer techniques genetic and evolutionary algorithms, artificial neural networks and fuzzy systems are part of the science known as Computational Intelligence (CI). It is a field of science that deals with solving problems by means of calculations that cannot be effectively algorithmized.

CI also includes methods of machine learning, object recognition(pattern recognition), multivariate statistics methods, optimization methods, uncertainty modeling methods – probabilistic, possibilistic, coarse(i.e. approximate sets and logic) and control theory.

They can be considered separately, independently, but their interconnections are also extremely important, allowing the creation of more efficient computational tools.

CI uses mathematical methods from many fields, using biological, biocybernetic, psychological, statistical, mathematical, logical, IT, engineering and other inspirations, if they can be useful for solving problems that cannot be effectively algorithmized.

The features of the intelligent computing techniques are:

  • The ability to acquire new knowledge
  • Self-adaptation;
  • Acceptance of incomplete and not fully logically coherent data;
  • Creativity.

Examples of practical applications of intelligent computing techniques:

  • Handwriting recognition – widely used, e.g. handwriting or drawing a line in a PDF file with the mouse cursor.
  • Technologies based on fuzzy logic – commonly used to, for example, control technological processes in factories in conditions of “lack of all data”.
  • Expert systems – extensive databases with implanted “artificial intelligence” that allows them to ask questions in natural language and obtain answers in the same language.
  • Machine translation of texts – such systems are still very imperfect, but they are making progress and are beginning to be suitable for translating, for example, technical texts.
  • Artificialneural networks -used successfully in many applications, including programming “intelligent opponents” in computer games.
  • Optical recognition – programs that recognize people based on facial photos or automatically recognize selected objects in satellite photos are already in use.
  • Speech recognition – currently widely used on a commercial scale.
  • Artificial creativity – there are programs that automatically generate short poetic forms, composing, arranging and interpreting musical works, which are able to confuse even professional artists.

Artificial intelligence in the file management: using machine learning in cloud computing

The constant increase in the number of file managements combined with the increase in the complexity of research objects necessitates the use of sophisticated data analysis techniques. As a result of a significant increase in the computational power of modern computers, artificial intelligent techniques implementing neural network algorithms have gained enormous popularity over the last few years.

AI and machine learning can leverage resources available in the cloud to create, train, and deploy various models. Their use is important because there are many indications that cloud computing is the future of IT and is used by an increasing number of users.

Thanks to it, they do not have to invest in equipment and infrastructure and can easily adapt resources to changing requirements. They also have access to tools and platforms that facilitate the creation and management of projects. AI and ML in cloud computing are most often used for:

  • Creating models for analyzing large data sets;
  • Recognizing patterns in data, such as medical image analysis;
  • Creating intelligent virtual assistants and chatbots;
  • Improving automation processes in enterprises;
  • Forecasting market trends and customer behavior;
  • Personalizing recommendations for users on e-commerce platforms.

Examples of using AI in file management

Artificial intelligence in the cloud has many applications that enable better analysis, understanding and use of information contained. One of them is, for example, mobile cloud computing , which is becoming more and more popular as the number of mobile devices used in the world increases. They are also widely used in image and video analysis, where they can identify objects, people, places and events.

AI in the cloud can also analyze texts, documents and content, automatically indexing and assigning them to appropriate categories. It is also used to analyze content in social media or comments to determine user sentiment towards products, brands or events. It also works the other way around – many cloud providers offer AI tools to create recommendation systems, which are used, for example, on streaming platforms to provide users with personalized suggestions.

AI implementations

Cloud AI can be used to automate business processes such as file management, form processing, and document analysis for information extraction. It can also analyze data from sensors and industrial equipment to predict failures and maintenance needs.

Other applications include text processing, forecasting trends, and identifying patterns or anomalies. The good practice of AI implementation goes to AI PDF summarization tools. There are many solutions that extend the capabilities of large language models with the ability to interpret PDF files – such as SwifDoo PDF AI.

What’s next?

We face new challenges in the world of file management, where accuracy and protection of data are crucial. Everything indicates that data protection officers will have to learn new methods of dealing with generative artificial intelligence. This is a fascinating time for the industry, but also a time that requires caution and responsibility.

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