Word count: 2500 words
Objectives to cover:
Introduction – Defining Artificial General Intelligence (AGI), difference between AGI and Narrow AI, importance of AGI in modern AI research.
Historical Context and Evolution of AGI – Early concepts and theoretical foundations, key milestones in AGI development, influence of cognitive science and neuroscience.
Defining Characteristics of AGI – Generalization and adaptability across domains, human-like reasoning and decision-making, learning from minimal data and transfer learning, self-awareness and consciousness debates.
Core Technical Approaches Toward AGI – Symbolic AI vs. connectionist models, evolutionary and hybrid approaches, reinforcement learning and meta-learning in AGI.
Benchmarks for Evaluating AGI – Standardized AGI evaluation metrics, Turing Test and its modern variants, Winograd Schema Challenge and other cognitive benchmarks, measuring AGI’s ability for lifelong learning.
Ethical and Philosophical Considerations – Potential societal impact of AGI, ethical dilemmas in AGI development, safety and control mechanisms for AGI.
Challenges in Achieving AGI – Computational and hardware limitations, algorithmic bottlenecks and current gaps, alignment problem and value alignment strategies.
Future Prospects of AGI – Predicted timelines and roadmaps, AGI in real-world applications, policy and governance frameworks for AGI development.
Conclusion – Summary of AGI’s key traits, challenges, and the road ahead in achieving human-level intelligence.
Reference: APA style