Attention mechanisms in Neural Networks, also known as attention models, are computational models that can be incorporated into Neural Network architectures. They were primarily developed to improve the performance of deep learning models in tasks such as natural language processing (NLP), computer vision (CV), and even medical diagnostics. These advanced computational models are designed for specific tasks and tend to mimic the human cognitive process of focusing on certain aspects while ignoring others based on their importance or relevance to the given context.

Research and application of attention mechanisms have brought to light several key characteristics of this technology:

• Selective Focus: Just like human cognition, attention mechanisms allow a neural network to learn where the focus should be directed, thereby allocating computational resources more adeptly.
• Context-Awareness: Attention based models have an enhanced ability to understand and apply context in the processing of data. This capability is particularly beneficial for tasks necessitating understanding the relationships between different parts of the data such as word dependencies in a sentence.
• Scalability: Attention mechanisms help in handling long-range dependencies in sequences, enabling deep learning models to process much larger input sequences than was previously feasible.
• Model Interpretability: While deep learning models are often critiqued for being 'black boxes', the usage of attention mechanisms can enhance model interpretability. An attentional map, which shows where the model is focusing, provides more transparency into the AI decision-making process.
• Performance Boost: The application of attention mechanisms can significantly enhance model performance, especially in cases where input data has varied relevance levels. Tasks such as machine translation and image recognition have benefited greatly from the use of attention-based models.

Deploying an attention-based model requires a deep understanding of how attention mechanisms work and a thorough analysis of the problem at hand. A careful evaluation of whether the attention mechanism adds substantial value to the current model is imperative before undertaking the complex task of integrating it into the existing system.

The implementation should be carried out with meticulous attention ensuring proper alignment between the task requirements and the workings of the attention mechanism to achieve successful results. Additionally, constant monitoring of the model’s performance after incorporation of the attention mechanism is crucial to address any issues that may arise.

The use of attention mechanisms in Neural Networks brings several inherent advantages:

• Enhanced Model Accuracy: Particularly in tasks which require understanding the dependencies between different parts of the data, attention mechanisms have been proven to enhance the accuracy of deep learning models.
• Scalability: Large input sequences can be processed smoothly ensuring that long-range dependencies in the data are well-identified and understood.
• Increased Model Transparency: Attention maps provide an insight into the workings of the Neural Network, therefore, elucidating the decision-making process of the AI.
• Addressing Critical Tasks: Attention-based models have contributed significantly to advancements in fields like natural language processing, computer vision, and healthcare diagnostics, amongst others. Their ability to focus on relevant parts of the data makes them highly efficient in addressing complex and critical tasks.

Despite the benefits, there are certain disadvantages to consider:

• Increased Model Complexity: The inclusion of attention mechanisms increases the complexity of Neural Network models. The ramifications include additional computational resources and increased need for expert knowledge during design and implementation.
• Risk of Overemphasis: Though the selective focus is a strength of attention mechanisms, there is a risk of overemphasis on certain parts of the data, leading to the negligence of other potentially critical information.
• Challenges in Implementation: The successful implementation of attention mechanisms requires an in-depth understanding of the model, keen attention to detail, and caution in ensuring the applicability of the attention mechanism to the task in hand.

In conclusion, attention mechanisms in Neural Networks have revolutionized the field of artificial intelligence. With their unique ability to selectively focus computational power, they have enabled AI to tackle complex tasks more efficiently and with improved accuracy. Despite some challenges, their benefits outweigh the difficulties, making attention mechanisms a formidable tool in the realm of advanced machine learning. By understanding the utility and potential of attention mechanisms, organizations can harness its full potential to address intricate tasks, drive innovation, and gain a competitive edge.