AI, ML, and DL are distinct domains within computer science focused on mimicking human intelligence.

AI encompasses various techniques, while ML emphasizes learning from data, and DL utilizes deep neural networks.

ML consists of supervised, unsupervised, and reinforcement learning methods

DL employs multi-layered neural networks for automated feature learning.

AI, ML, and DL have applications in healthcare, finance, and transportation sectors.

Challenges include data dependency and ethical concerns regarding AI autonomy.

Future trends involve advancements in technology and interdisciplinary integration.

Understanding these technologies is crucial for leveraging their potential.

Real-world examples include virtual assistants, autonomous vehicles, and recommendation systems.

Ethical implications include privacy, bias, and job displacement concerns.

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