Artificial Intelligence and Philosophy of Intelligence

Study Notes

AI simulates human mental abilities and work patterns, emphasizing learning and reacting to unforeseen situations.
AI aims to create systems that can perform human-like functions like learning languages, completing tasks, and understanding.

Intelligence is complex and can be defined in various ways, including logic, abstract thought, understanding, self-awareness, communication, learning, emotional knowledge, memory, planning, creativity, and problem-solving.
Intelligence is the ability to perceive and retain information as knowledge, applying it to itself or other knowledge for creating understanding models.
Intelligence enables applying learned information to new situations and creating new outcomes.

Learning ability: AI programs learn through observation, relying on machine learning strategies to continuously improve performance by analyzing previous mistakes.
Empirical research: AI programs tackle problems without predefined steps, using empirical methods to discover solutions.
Knowledge representation: AI programs utilize symbolic representation to express information and use empirical research for finding solutions.
Data unconfirmed or incomplete: AI programs are designed to handle incomplete or uncertain data, providing solutions even when information is lacking.

Viability knowledge representation: Special rules are used to describe knowledge (facts, relations, rules, and frames).
Symbolic processing: AI languages can handle symbols and shapes.
Flexibility of control: AI languages offer flexible control compared to traditional languages, allowing for more efficient implementation.

Finding, logical thinking, and problem-solving: AI research focuses on algorithms that mimic sequential logical thinking for problem-solving, including puzzles, board games, and logic-based conclusions.
Supervised learning: This includes classification (determining category based on models) and regression (discovering input-output relationships from data).
Natural language processing: AI aims to enable machines to understand and process human languages, with applications in information retrieval and text analysis.
Movement and change: Robotics closely links with AI, requiring intelligent robots to handle tasks like manipulation, navigation, localization, mapping, and movement planning.
Perception: Machine perception involves using sensors (cameras, microphones, sonar) to analyze aspects of the world. This includes computer vision, speech recognition, and object identification.

Turing test: This proposes a test of intelligence by observing a machine's ability to behave indistinguishably from a human in a conversation.
Expert Turing tests: These evaluate AI based on specific tasks like chemistry, handwriting recognition, and games.
Intelligent measures classification: AI test results are categorized based on performance:
- Optimization: The best possible performance.
- Extraordinary human: Performance surpassing human capabilities.
- Above human: Performance exceeding most humans.
- Below human: Performance inferior to most humans.

Technological singularity: Some futurists predict AI surpassing human capabilities, leading to rapid technological advancements and a fundamental shift in humanity's future.
Transhumanism: AI's potential to merge with humans, creating cyborgs with enhanced capabilities.

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