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Artificial Intelligence Approaches: Different Schools of Thought and Interpretations
- Symbolic AI or Good Old-Fashioned AI was the dominant paradigm in AI research from 1950s to 1980s and based on intelligence attained through manipulation of symbols and logic.
- The Sub-symbolic AI approach relies on statistical and numerical methods that learn patterns from the data.
- The Connectionist approach excels in pattern recognition and classification tasks and involves neural network-based Artificial Intelligence.
- In Logical Approach, reasoning is done based on formal logic systems and results are meaningful and verifiable.
- Biologically Inspired AI, based on mimicking structures and processes of biological systems, focuses on creating general intelligence.
- The Engineering-Focused Design mainly focuses on solving specific tasks efficiently using domain-specific knowledge and heuristics.
- There is a continuing debate on approaches to Artificial Intelligence between logical reasoning and deep learning, scruffy versus neat approaches.
- Understandably, to make truly intelligent machines, a hybrid approach will be necessary for Artificial Intelligence researchers.
- AI is still evolving, and it is crucial for policymakers, ethicists, researchers, and the public to understand the different approaches and methods being used.
- This detailed understanding will influence the impact of AI systems on society and make a difference in the innovative and exciting progress of ensuring the future of AI research.
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