What is AGI? Key Terms and Definitions

This list includes key terms and definitions related to Artificial General Intelligence (AGI) and its surrounding ecosystem.

Artificial General Intelligence (AGI): A type of AI capable of performing any intellectual task that a human can, with the ability to learn, reason, and adapt across various domains without being explicitly trained for each one.
Narrow AI (ANI): AI systems designed for specific tasks, such as image recognition or language translation, lacking the general adaptability of AGI.
Strong AI: Synonymous with AGI, referring to AI with the ability to exhibit human-like cognitive functions across a wide range of activities.
Weak AI: AI that is task-specific, focusing on narrow problem-solving without general intelligence.

Neural Network: A computational model inspired by the human brain, used in machine learning to identify patterns and make decisions.
Deep Learning: A subset of machine learning that uses neural networks with many layers to model complex patterns in large datasets.
Reinforcement Learning (RL): A type of machine learning where an agent learns by interacting with its environment to maximize a reward signal.
Neuro-Symbolic AI: A hybrid approach combining neural networks for learning and symbolic reasoning for logic-based decision-making.
Transfer Learning: A technique in AI where knowledge gained from one task is applied to a related but different task.
Transformer Models: AI architectures, such as GPT, that process sequences of data using attention mechanisms, widely used in natural language processing.
Mixture of Experts (MoE): A machine learning technique where multiple specialized models are used collaboratively for decision-making.
Positional Embedding: A mechanism in transformer models that incorporates information about the relative position of data points.
Neural Oscillation: Rhythmic activity in neural networks, often studied for its role in synchronizing computational processes.

AI Ethics: A field of study focusing on the moral implications of AI, including fairness, accountability, and transparency.
AGI Governance: Policies and frameworks to ensure the safe and beneficial development and deployment of AGI.
Explainability: The ability of an AI system to provide understandable reasons for its decisions and actions.
Alignment Problem: The challenge of ensuring that an AGI’s goals and behaviors align with human values and intentions.
Existential Risk (X-Risk): Potential threats posed by AGI that could lead to catastrophic outcomes for humanity.

Autonomous Agents: AI systems capable of acting independently in an environment to achieve specific goals.
Cognitive Computing: AI systems that simulate human thought processes to solve complex problems.
Human-AI Collaboration: The partnership between humans and AI systems to augment decision-making and productivity.
Multi-Agent Systems: Systems involving multiple AI agents working collaboratively or competitively within an environment.
Digital Twin: A virtual representation of a physical object or system, enhanced by AI for real-time monitoring and decision-making.

Turing Test: A measure of a machine's ability to exhibit behavior indistinguishable from that of a human.
Intelligence Explosion: A hypothesized event where an AGI rapidly improves itself, leading to a superintelligent system.
Consciousness in AI: The study of whether and how AI systems might achieve self-awareness or subjective experiences.
Emergent Behavior: Complex behavior that arises from simple rules or interactions within an AI system.

Compute Power: The computational resources, such as GPUs and TPUs, required for training advanced AI models.
AI Hardware: Specialized processors designed to optimize AI computations, such as those developed by NVIDIA, Cerebras, and Graphcore.
Training Dataset: Large collections of labeled or unlabeled data used to train AI systems.
Model Scaling Laws: The hypothesis that larger AI models with more data and compute power exhibit better performance.

Automation: The use of AI to perform tasks traditionally done by humans, leading to efficiency gains but potential job displacement.
Universal Basic Income (UBI): A policy proposal to provide a guaranteed income to all citizens, often discussed as a response to AI-driven unemployment.
AI-Driven Markets: Economic systems influenced or optimized by AI technologies, such as algorithmic trading or personalized e-commerce.
AI and Creativity: The use of AI to augment or generate creative works, such as art, music, and writing.

Kill Switch: A mechanism to disable an AI system in case of unexpected or harmful behavior.
AI Containment Problem: The challenge of preventing AGI from causing harm or escaping its operational boundaries.
Value Alignment: Ensuring that an AGI’s actions reflect ethical principles and societal values.
Capability Control: Methods to limit or regulate the abilities of an AGI to mitigate risks.

AI Researchers: Scientists and engineers driving advancements in AGI and related technologies.
Policymakers: Government officials creating laws and guidelines to manage AI and AGI development.
Tech Corporations: Companies like OpenAI, Google DeepMind, and NVIDIA leading the AGI race.
Civil Society Organizations: Groups advocating for ethical AI development and equitable access.

AGIFrancesca Tabor31 December 2024