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Answer all of the following: (a) Industry 4.0 (4th industrial revolution) is a fusion of leading-edge production techniques and smart systems that integrate with or... show full transcript
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Artificial Intelligence (AI) is a broad field within computer science focusing on creating systems capable of performing tasks that typically require human intelligence. This includes problem-solving, understanding language, recognizing patterns, and making decisions. AI can simulate cognitive functions, enhancing productivity and efficiency in various domains.
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Machine learning is a subset of AI that uses statistical techniques to enable systems to learn from data and improve over time without being explicitly programmed. It involves algorithms that analyze data to identify patterns and make predictions. Deep learning, on the other hand, is a specialized area of machine learning that utilizes neural networks with many layers (hence 'deep') to process complex data such as images and speech, learning to make decisions based on this information.
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Artificial Narrow Intelligence (weak AI) refers to AI systems designed to perform specific tasks, such as voice recognition or image classification, without possessing consciousness or understanding. These systems can excel in their limited scope but lack the ability to adapt to new tasks. In contrast, Artificial General Intelligence (strong AI) represents a theoretical form of AI that would possess the ability to understand, learn, and apply intelligence in a generalized manner, akin to human cognitive capabilities.
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Reactive machines are AI systems that can react to current situations but do not retain past experiences or learn over time. An example is IBM's Deep Blue, which plays chess based on the current state of the game without prior knowledge. Limited memory refers to AI systems that can retain information for a short duration, enabling them to learn from past data, such as self-driving cars that remember recent activities to make informed driving decisions.
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Predictive maintenance employs data analytics to anticipate equipment failures before they occur, thus optimizing repair schedules and reducing downtime. This is achieved by analyzing historical performance data and operational conditions. Supply chain management leverages AI to enhance decision-making by predicting demand and optimizing logistics, ultimately improving efficiency and reducing operational costs.
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Economic benefits of AI include increased automation, which drives efficiency and productivity across industries. AI can lead to innovation in services and products, enhancing customer experience and contributing to overall economic growth. Ethical concerns arise from potential job displacement due to automation, privacy issues related to data usage, and the ethical implications of autonomous decision-making systems, necessitating careful consideration in AI deployment.
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