The rise of Neural Machine Translation (NMT) systems has been hailed as a breakthrough in language translation, allowing for faster and more accurate translations than ever before. However, the efficiency of these systems comes at a high cost: their energy consumption is significantly higher compared to other translation technology. This raises an important question: is the improved accuracy of NMT worth its environmental impact? And more importantly, is there a valid way to reduce the environmental impact?
Most NMT systems require large amounts of computing power in order to run their algorithms efficiently. This means that they consume a large amount of electricity, which in turn leads to an increase in carbon dioxide emissions. Additionally, NMT systems use cloud computing services, which rely on a complex mix of energy sources to power their operations; this electricity can come from a variety of sources, including coal, natural gas, nuclear, hydroelectric, and renewable sources like wind and solar.
Furthermore, the energy required for training NMT models is also quite high. Training a model requires a lot of data processing and can take days or weeks depending on the size and complexity of the model being trained. This further adds to the environmental impact of these systems since more electricity is required for this process. A better idea of the environmental impact is provided by a recent study by Strubell et al. (2019) which shows that training a state-of-the-art NMT model on a single graphics processing unit (GPU) can emit as much carbon dioxide as a round-trip transatlantic flight, and that training larger models can result in emissions equivalent to several hundred such flights.
These issues are particularly concerning given that NMT technology is becoming increasingly prevalent in various industries and applications such as online chatbots, machine translation services, automatic speech recognition systems, etc., all of which require large amounts of energy to operate effectively. Therefore, it is essential that measures are taken to reduce the environmental impact of NMT systems in order to ensure sustainable development and protect our planet’s resources.
There are several strategies that can help reduce the environmental impact of NMT while still enabling its improved accuracy and efficiency compared with traditional methods. Smaller and more efficient models and optimization of training procedures are definitely among the best practices and we at Laratech have been developing NeuralDesktop with the priority to reduce the environmental impact while delivering unparalleled machine translation results.
The efficiency of Neural Machine Translation (NMT) systems is important for delivering accurate translations quickly and efficiently, but the question of whether it is worth the high energy consumption is complex and multifaceted, depends on the specific context, and requires careful consideration of both the benefits and costs of the technology.
