# Written by AI (may include hallucinated factually incorrect information) Here is a comprehensive map of various definitions and perspectives on artificial general intelligence (AGI): ## Core Concepts - Human-level AI capable of performing any intellectual task - AI with general problem-solving abilities across domains - AI that can learn and adapt to new situations like humans - AI with self-awareness, consciousness, and common sense reasoning ## Key Capabilities - Abstract thinking and reasoning - Transfer learning across domains - Creativity and innovation - Natural language understanding - Sensory perception and motor skills - Emotional intelligence and social cognition - Autonomous learning and self-improvement ## Specific Definitions **OpenAI:** "Highly autonomous systems that outperform humans at most economically valuable work"[3] **TechTarget:** "Representation of generalized human cognitive abilities in software so that, faced with an unfamiliar task, the AGI system could find a solution" **Investopedia:** "AI with a human level of cognitive function, including the ability to self-teach"[6] **arXiv paper:** "Adaptation to open environments according to certain principles using limited resources"[3] **Wikipedia:** "Intelligence comparable to, matching, differing from, or appearing alien-like relative to human intelligence"[2] **AWS:** "Software with human-like intelligence and the ability to self-teach"[5] **Coursera:** "Hypothetical form of artificial intelligence where a machine learns and thinks like a human does"[4] ## Theoretical Approaches - Symbolic AI (logic and knowledge representation) - Connectionist AI (neural networks) - Hybrid approaches combining symbolic and connectionist methods - Whole brain emulation - Cognitive architectures - Universal AI (theoretical foundations) ## Key Characteristics - Domain-general capabilities - Autonomous goal-setting and pursuit - Adaptability to new environments - Ability to handle uncertainty and ambiguity - Generalization from limited data - Causal reasoning and inference - Meta-learning and introspection ## Evaluation Criteria - Turing test and variations - Cognitive decathlon tests - Real-world problem-solving benchmarks - Measures of creativity and innovation - Adaptability to novel situations - Social and emotional intelligence tests ## Philosophical Perspectives - Strong AI vs Weak AI distinction - Chinese Room argument - Consciousness and qualia debates - Ethics and rights of AGI systems - Potential risks and existential threats ## Research Challenges - Scalable reasoning and knowledge representation - Unsupervised and few-shot learning - Commonsense reasoning - Causal inference - Robustness and safety - Interpretability and transparency - Value alignment and control ## Timelines and Predictions - Near-term (5-10 years) - Mid-term (10-50 years) - Long-term (50+ years) - Never achievable ## Related Concepts - Artificial Superintelligence (ASI) - Transformative AI - Narrow/Weak AI - Artificial Consciousness - Whole Brain Emulation This map covers a wide range of perspectives on AGI, from specific technical capabilities to broader philosophical questions. The exact definition and path to AGI remain subjects of ongoing research and debate in the AI community. Citations: [1] https://www.techtarget.com/searchenterpriseai/definition/artificial-general-intelligence-AGI [2] https://en.wikipedia.org/wiki/Artificial_general_intelligence [3] https://arxiv.org/html/2404.10731v1 [4] https://www.coursera.org/articles/what-is-artificial-general-intelligence [5] https://aws.amazon.com/what-is/artificial-general-intelligence/ [6] https://www.investopedia.com/artificial-general-intelligence-7563858 [7] https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/