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What are some challenges of using neural networks? 


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Neural networks face several challenges in their use. Firstly, designing the intended function of the network and the monitors needed to achieve a deterministic and fail-safe behavior of the system is extremely difficult . Secondly, neural networks require large amounts of training data, which can be limited by issues such as labeller subjectivity, training data quality, and quantity . Additionally, the structure of neural network models makes them hard to understand and interpret by human developers, posing challenges in checking their conformance to established scientific laws . In the context of coalition operations, energy and bandwidth constraints are a key challenge, which can be addressed by splitting deep neural networks across multiple edge nodes and using spiking neural networks to lower energy consumption . Finally, verifying neural networks is challenging due to their lack of explainability and the inability to exhaustively test them across all operating conditions .

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The challenges of using neural networks include their lack of explainability, difficulty in exhaustive testing, and inability to handle variations in operating conditions.
The challenges of using neural networks in coalition operations include energy and bandwidth constraints.
Some challenges of using neural networks include the need for large training datasets, biases introduced by multiple labellers, and the requirement for manual annotation.
Some challenges of using neural networks include designing the intended function of the NN and designing monitors for deterministic and fail-safe behavior.
One key challenge of using neural networks is checking that they conform to established scientific laws.

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