Artificial General Intelligence

Artificial General Intelligence (AGI) refers to the capability of an artificial intelligence system to perform cognitive functions such as learning, reasoning, problem solving, abstraction, and transferring knowledge across different tasks at a level equivalent to human mental abilities. In this context, AGI represents a stage of artificial intelligence that goes beyond current narrow AI systems, aiming to develop systems exhibiting the general-purpose cognitive flexibility and adaptability characteristic of humans.

Historical Background and Conceptual Development

The concept of AGI was first systematically addressed in the early 2000s by Ben Goertzel and later gained widespread usage following a proposal by Shane Legg. The foundations of artificial intelligence research, laid at the 1956 Dartmouth Conference, were based on the idea that all forms of intelligence could be modeled by computers. However, in subsequent years, AI research largely shifted toward narrow, task-specific systems. AGI, by contrast, seeks to overcome the limitations of these narrow systems by developing a form of intelligence capable of multidimensional learning and generalization.

Distinguishing Features

• The main features that distinguish AGI from current AI systems are:
• Generalization Ability: The capacity to transfer knowledge acquired in one domain to new and previously unseen domains.
• Common Sense Knowledge: A broad conceptual framework encompassing fundamental knowledge about the world, relationships, and social norms.
• Ability to Learn New Tasks: The capability to learn and adapt to new tasks in diverse contexts, as humans do.
• Flexibility and Robustness: The ability to adapt to environments under conditions of incomplete information and limited resources.

Comparison with Other AI Types

• Narrow AI (ANI): Systems specialized in specific and limited tasks without generalization capabilities.
• Strong AI: Refers to AI systems assumed to possess consciousness and mental content. While AGI is closely related to this concept, not every AGI system needs to be conscious.
• Artificial Superintelligence (ASI): Describes AI systems that surpass human intelligence in all domains and can make advanced autonomous decisions. AGI can exist without reaching superintelligence levels.

Definition Approaches and Tests

• Various theoretical and practical approaches have been developed to define and validate AGI, including:
• Turing Test: A machine is considered intelligent if its responses are indistinguishable from those of a human. However, this test is deemed insufficient for measuring consciousness or understanding.
• Chinese Room Argument: Proposed by John Searle, this argument asserts that a system’s ability to produce appropriate linguistic responses does not imply genuine comprehension.
• Human-Level Performance: The ability to perform cognitive tasks at a level comparable to humans.
• Economic Value-Based Definition: AGI is defined as a system that can outperform humans in economically valuable tasks.
• Flexibility-Oriented Definition: General capabilities such as functioning successfully under varying conditions, planning, and understanding new tasks.
• ACI (Artificial Capable Intelligence): An alternative concept defined as artificial systems capable of completing multi-step, ambiguous tasks in the real world.

Technological Approaches

There are three primary approaches toward achieving AGI:

1. Software Simulation of the Human Brain: Systems aiming to model brain processes with high fidelity, going beyond conventional neural networks.
2. Development of Novel Architectures: Systems based on original algorithms that do not rely on existing neural networks or brain structures.
3. Integration of Narrow AI: Holistic systems created by combining large language models, image models, and reinforcement learning agents.

Debates and Current Status

In the 2020s, some researchers have argued that systems such as large language models (LLMs) are approaching AGI. However, critics emphasize that these models lack common sense, action planning, persistent memory, and real-world experiential grounding. Researchers such as Yann LeCun argue that models trained solely on linguistic data cannot approach human intelligence. In this context, AGI is widely regarded as still a theoretical goal.

Potential Applications and Impacts

If realized, AGI is expected to drive transformative changes across numerous domains, from healthcare and climate challenges to transportation safety and personalized education. It also holds the potential to enhance productivity, optimize efficiency, and unlock new creative possibilities.