Q17 /20 Which one of the following is not true about Deep Learning?
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Which one of the following is not true about Deep Learning?
Show answer & explanation
Deep learning models, particularly neural networks, are notorious for being 'black boxes'—they lack interpretability despite producing accurate results. Options A, B, and D are all factually correct: deep learning is part of machine learning with proven applications in vision/speech/NLP, it solves real-world problems like machine translation and image tagging, and AlphaGo does use deep reinforcement learning. Option C falsely claims deep learning results are easy to interpret, which contradicts the well-known interpretability challenge in the field.
Step-by-step Derivation:
Evaluate each statement against known deep learning characteristics: (A) TRUE—deep learning outperforms traditional ML in many domains. (B) TRUE—all listed applications are well-documented use cases. (D) TRUE—AlphaGo employed deep neural networks with reinforcement learning. (C) FALSE—deep learning models are inherently difficult to interpret; this is a major limitation addressed by emerging explainable AI (XAI) research. The question asks which is NOT true, so C is the answer.