TY - GEN
T1 - Generative AI for the Maritime Environments
AU - Reddy, G. Pradeep
AU - Sinha, Shrutika
AU - Park, Soo Hyun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Generative AI is a new and rapidly developing field of artificial intelligence that focuses on creating new data or content. Although the concept of generating content was invented long back, the latest breakthroughs in neural network architectures have made it possible to implement this concept at a much faster pace. With the availability of vast amounts of data and computational power, it has become increasingly feasible. Generative models aim to generate new data resembling the training data distribution by learning its underlying patterns. They capture the inherent complexity of the data to produce outputs similar to the training examples. Discriminative models, on the other hand, focus on learning boundaries between different classes or categories within the data. The maritime industry is a major contributor to the global economy, but it is also a high-risk industry, as it is exposed to a variety of hazards. Generative AI can be used to improve maritime safety and efficiency in several ways. Although the maritime industry is still in the early stages of adopting generative AI, the potential benefits are significant. The integration of generative AI into maritime operations plays an increasingly important role in the maritime industry. In this view, this paper discusses generative AI, its technology, various applications, and key challenges and research prospectives.
AB - Generative AI is a new and rapidly developing field of artificial intelligence that focuses on creating new data or content. Although the concept of generating content was invented long back, the latest breakthroughs in neural network architectures have made it possible to implement this concept at a much faster pace. With the availability of vast amounts of data and computational power, it has become increasingly feasible. Generative models aim to generate new data resembling the training data distribution by learning its underlying patterns. They capture the inherent complexity of the data to produce outputs similar to the training examples. Discriminative models, on the other hand, focus on learning boundaries between different classes or categories within the data. The maritime industry is a major contributor to the global economy, but it is also a high-risk industry, as it is exposed to a variety of hazards. Generative AI can be used to improve maritime safety and efficiency in several ways. Although the maritime industry is still in the early stages of adopting generative AI, the potential benefits are significant. The integration of generative AI into maritime operations plays an increasingly important role in the maritime industry. In this view, this paper discusses generative AI, its technology, various applications, and key challenges and research prospectives.
UR - https://www.scopus.com/pages/publications/85202765195
UR - https://www.scopus.com/pages/publications/85202765195#tab=citedBy
U2 - 10.1109/ICUFN61752.2024.10625100
DO - 10.1109/ICUFN61752.2024.10625100
M3 - Conference contribution
AN - SCOPUS:85202765195
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 618
EP - 623
BT - ICUFN 2024 - 15th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
T2 - 15th International Conference on Ubiquitous and Future Networks, ICUFN 2024
Y2 - 2 July 2024 through 5 July 2024
ER -