Artificial General Intelligence

Defines Artificial General Intelligence, distinguishes it from narrow AI, and explores its foundational concepts and ultimate objectives within cognitive science and AI research.

Traces the conceptual origins of AGI from early philosophical inquiries into mind and mechanism through the dawn of computing, the Dartmouth Conference, cycles of AI optimism and 'winters', to the modern resurgence driven by computational advances.

Examines the key theoretical pillars of AGI, including cognitive architectures, learning paradigms (supervised, unsupervised, reinforcement), transfer learning, meta-learning, reasoning, common sense, and the integration of perception, action, and cognition.

Analyzes the major technical strategies pursued to achieve AGI, such as symbolic AI, connectionist (neural network) approaches, hybrid systems, whole brain emulation, and developmental robotics, assessing their respective strengths and limitations.

Details the principal obstacles to creating AGI, including the frame problem, the symbol grounding problem, computational complexity, the need for vast, multimodal knowledge, achieving robust and safe generalization, and the current lack of a unified theory of intelligence.

Explores the profound ethical, safety, and societal questions raised by the prospect of AGI, including value alignment, control problems, economic disruption, existential risk, governance, and the potential impact on human identity and society.

Surveys current cutting-edge research areas and speculative future trajectories, including neuromorphic computing, artificial consciousness, human-AI collaboration, the feasibility and timelines for AGI development, and its potential long-term evolutionary consequences.