19. INPUT
The chapter examines the critical role of input data in Artificial Intelligence systems, emphasizing how various data types are essential for effective learning and decision-making processes. It discusses the significance of quality input for accurate predictions, explores notable data collection methods, and highlights ethical considerations surrounding data use. Through various applications, the chapter underscores the importance of input in powering real-life AI functionalities ranging from virtual assistants to self-driving cars.
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What we have learnt
- Input is crucial for AI systems, serving as the foundational data for processing.
- Input data can be structured, unstructured, or semi-structured, each with its own uses and challenges.
- Proper methods of data collection, validation, and cleaning significantly impact the effectiveness of AI systems.
- Ethical considerations, including privacy, consent, and security, are essential in the collection and use of input data.
Key Concepts
- -- Input Data
- Data or information fed into an AI system for processing, learning, and decision-making.
- -- Structured Data
- Organized information formatted in rows and columns, such as databases, making it easy to analyze.
- -- Unstructured Data
- Data without a predefined format, including images, audio, and social media posts, requiring special tools for analysis.
- -- Data Collection Methods
- Various techniques used to gather data, including manual entry, web scraping, APIs, and sensors.
- -- Data Cleaning
- The process of correcting or removing erroneous data to improve the quality before analysis.
- -- Ethical Considerations
- Principles guiding the responsible collection and usage of data, focusing on privacy, consent, bias, and security.
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