Master this deck with 21 terms through effective study methods.
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Machines performing tasks requiring human-like intelligence.
Data, Computer Vision, and Natural Language Processing.
AI lacks true originality and emotional depth.
Inaccurate outputs if data is biased or incomplete.
Analyzing data to assist in medical diagnoses.
Privacy, job displacement, and accountability issues.
Results skewed due to training on biased data.
To guide the development and deployment of AI systems.
Collecting relevant data for the AI system.
Learning-based adapts through experience, while rule-based follows predefined rules.
Lack of transparency in how AI systems make decisions.
It helps in clearly defining AI project goals.
Repetitive jobs are at risk, while new AI-related jobs may emerge.
To ensure responsible and fair use of AI technology.
Addressing urban issues like traffic and energy consumption.
Profits may concentrate in AI-driven companies, disadvantaging workers.
To gather feedback from users on the deployed AI solution.
It is used to teach the AI model how to make predictions.
To implement it in real-world scenarios for practical use.
Data quality, model performance, and scalability issues.
Morals are personal beliefs; ethics are societal standards.