Neural Machine Translation (NMT) represents an advanced technology method utilized in translating languages powered by artificial intelligence and deep learning. These translations are primarily between human languages, but can cater to various domains where language translation becomes crucial. NMT systems are built around deep neural networks consisting of countless interconnected nodes, contributing to the learning and interpretation processes that generates translations.

Neural Machine Translation exhibits key characteristics that set it apart from the traditional machine translation systems:

• Deep Learning: NMT relies on deep learning, implying that the system learns to translate by being trained on enormous datasets that include numerous language pairs, which thereby influences the responses and translations.

• Seamless Translation: Unlike phrase-based translation methods which break down sentences into multiple phrase blocks, NMT translates entire sentences at once, thereby achieving a more fluent and coherent translation.

• Context Sensitivity: NMT comprehends and considers the context of words and sentences, leading to more accurate and contextually correct translations, unlike previous methods capable of considering only a limited scope of surrounding words.

• Flexibility: NMT systems can be trained to translate multiple languages, an attribute which opens up exciting possibilities for multi-lingual machine translation.

• Improved Accuracy: Owing to its end-to-end learning structure, NMT often generates translations with fewer errors and higher overall accuracy when compared with traditional machine translation methods.

Implementing NMT in an organization requires a comprehensive understanding of the fundamental needs and demands of the organization or task at hand. The NMT model used should correlate with criteria such as the type and amount of data available for training, the nature of the translations needed, and the computational resources available. It’s not a one-size-fits-all solution and needs careful evaluation and strategic planning. A poorly selected NMT system might not provide the quality of translation expected, leading to inefficiency and increased costs.

Taking into account all its possibilities and challenges, Neural Machine Translation offers a promising path forward in the realm of machine translation. It is the blend of deep learning algorithms and multi-layer artificial neural networks that brings NMT closer to achieving human-like translations. It is reshaping the course of machine translation by breaking language barriers more efficiently and economically, thereby fostering global communication without any hassles.

Neural Machine Translation offers an extensive range of advantages over traditional machine translation systems:

• Enhanced Quality: The translations generated by NMT systems are generally of superior quality with greater coherency and fluency as the system "understands" the full context of the sentence before translating.

• Scalable Solution: With NMT, a single neural network model can be trained to translate between multiple languages simultaneously. This makes NMT highly scalable, allowing expansions in correlation with an organization's growing needs.

• Faster Real-time Translation: Due to the nature of processing the whole sentence at once, NMT can often provide faster translations in real-time when compared to traditional translation methods.

• Cost-effective: Training a single NMT model for multiple languages results in cost-effectiveness. Resources are utilized more efficiently, making it an affordable choice for companies requiring frequent translations in numerous languages.

• Less Human Intervention: NMT can produce accurate translations without any human assistance, thereby lowering the cost of human resources required for post-editing processes.

Despite numerous advantages, there are certain challenges associated with NMT:

• Resource Intensive: Training the NMT models requires significant computational power and memory, as well as a large amount of bilingual text. This can be daunting for SMEs and organizations with limited resources.

• Difficult to Control Quality: NMT translations are not as easily tweakable as rule-based or statistical machine translation systems. It makes quality control a challenging aspect, particularly when it comes to industry-specific or highly specialized translations.

• Unknown Errors: NMT systems occasionally make bizarre translation errors which can be challenging to debug due to the complexity of deep learning.

• Data Needs: NMT performs best when it has a large amount of high-quality training data. This can be a problem for the languages where datasets are scarce.