Discover which of the following is not a promise of artificial neural networks: their ability to explain results, survive node failures, handle noise, or exhibit inherent parallelism.
Table of Contents
Question
Which of the following is not the promise of artificial neural network?
A. it can explain result
B. it can survive the failure of some nodes
C. it has inherent parallelism
D. it can handle noise
Answer
A. it can explain result
Explanation
Artificial neural networks (ANNs) are powerful computational models inspired by the human brain, capable of learning from data and making predictions. However, they come with certain limitations, particularly regarding their interpretability.
Lack of Explainability
While ANNs excel in tasks such as pattern recognition and predictive analytics, they are often criticized for their “black box” nature. This means that while they can produce accurate results, understanding how they arrive at those results can be challenging. Current advancements in explainable AI (XAI) aim to improve this aspect, but inherent limitations remain in fully elucidating the decision-making processes of ANNs.
Fault Tolerance
Option B states that ANNs can survive the failure of some nodes, which is true. The distributed nature of neural networks allows them to maintain functionality even if certain neurons fail, making them robust against such failures.
Inherent Parallelism
Option C refers to the inherent parallelism of ANNs, which is also a true statement. They are designed to process multiple inputs simultaneously, allowing for efficient computation and faster training times.
Noise Handling
Lastly, option D indicates that ANNs can handle noise effectively. This capability is one of their strengths; they can learn from imperfect data and still generalize well to unseen inputs.
In summary, while ANNs have numerous advantages such as robustness to node failures, inherent parallelism, and noise resilience, their ability to explain results is limited and remains an area of active research and development in the field of artificial intelligence.
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