Future perspectives of artificial intelligence

Magomedov, Islam; Bagov, Artur; Tkachenko, Alexey

doi:10.1051/bioconf/20248402008

BIO Web Conf.

2024

DOI: 10.1051/bioconf/20248402008

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Future perspectives of artificial intelligence

Islam Magomedov,

Artur Bagov,

Alexey Tkachenko

Abstract: New technologies are emerging almost every day, and it is hard to follow the trends. However, there is one technology that fill all the empty and taken space. This technology transforming the world we live is dramatically. Artificial intelligence is a key player in modern time to elevate any field to the next level. Therefore, the article will be focusing on how it will be improving over the years. Also, some of the main aspects of the technology will be covered. The common usage of the technology is also cove… Show more

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2024

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Cited by 3 publications

References 10 publications

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Optimizing structures and bio environments through generative design in engineering

Abdulkadyrov,

Aygumov,

Abdulmukminova

2024

BIO Web Conf.

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Structure optimization is one of the main fields that provides the most efficient results for the application. If correctly used the method can minimize all the unwanted aspect and maximize the chosen objectives. Nowadays, optimization on structure can be reached through using tools that are powered by artificial intelligence. The technology is well adopted and implemented in the fields of engineering. The positive impact from this method yet to be harvested. Therefore, the work will look at the use of artificial intelligence in engineering fields. Especially the focus of the work will be aimed at the structure optimization. Generative design is a method used in engineering to optimize the structure by different aspects. In this work some of the main components of generative designs will be outlined.

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Optimizing structures and bio environments through generative design in engineering

Abdulkadyrov,

Aygumov,

Abdulmukminova

2024

BIO Web Conf.

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Q-Learning: Advancing various systems, including real and virtual environments – current and future states

Matygov,

Aygumov,

Abdurashidov

2024

BIO Web Conf.

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As we delve into the realm of artificial intelligence, we understand that the technology is diverse in the techniques it utilizes. these approaches are utilized to enhance the artificial intelligence depending on the tasks. each task requires a unique approach and hence must be dealt with individually, of course, most of the tasks require a complex approach and hence all methods must be implemented to achieve the best outcome of the task. the one approach that will be covered in this work is Q-learning. it is a component of the reinforcement method, which is a subpart of machine learning. this approach is used to explore dynamic environments. This approach is used in various fields and provides solid results. hence, the work will try to explain what is Q-leaning approach in reinforcement learning is and in which fields it is applicable. the focus will also be on its current state and its future perspectives.

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Issues associated with data generation in artificial intelligence

Gairabekova,

Aygumov,

Belashova

2024

E3S Web Conf.

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Artificial intelligence is the late popular technology that can be found in any possible field. The spread of this technology like a virus is due to the improvement of technology and the methods and the wide knowledge of humanity. The technology is conquering areas that one not even imagine and it is turning them into refined products. However, there are always some issues that occur at the beginning or in the process. In this case, the second one is the case. The issue with technology, in some areas, is that it can collapse on itself. The overgeneration of data could lead to unwanted outcomes. Therefore, the work will illustrate how artificial intelligence, in some cases, can be destroying itself. Meaning losing the quality that it had at the beginning of its use.

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Future perspectives of artificial intelligence

Cited by 3 publications

References 10 publications

Optimizing structures and bio environments through generative design in engineering

Optimizing structures and bio environments through generative design in engineering

Q-Learning: Advancing various systems, including real and virtual environments – current and future states

Issues associated with data generation in artificial intelligence

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