The Elusive Definition of Artificial General Intelligence
Key Points
- The definition of AGI is a topic of debate among experts
- Depending on the definition, AGI may already exist or be physically impossible to achieve
- Researchers have attempted to create objective benchmarks to measure progress toward AGI
- The Abstraction and Reasoning Corpus (ARC-AGI) is a benchmark that tests AI systems’ ability to solve novel visual puzzles
- Benchmarks face limitations, including the problem of data contamination and the challenge of reducing intelligence to a score
A minimalist illustration showing a dark silhouette of a head with an open top, from which yellow circuit board-like patterns emerge upward against a deep purple background. The circuit patterns branch out in tree-like formations with nodes and connections, suggesting a technological or AI-related concept.
Sam Altman speaks onstage during The New York Times Dealbook Summit 2024 at Jazz at Lincoln Center on December 04, 2024 in New York City.
The Challenge of Defining AGI
The concept of Artificial General Intelligence (AGI) is a complex and multifaceted one, with no clear definition or benchmark to measure its achievement. According to some experts, current language models may already possess AGI capabilities, while others argue that true AGI is still a distant goal.
The lack of a clear definition has led to confusion and disagreement among researchers and experts. Some define AGI as “AI that performs better than most humans at most tasks,” while others argue that this definition is too narrow or too broad.
The Limitations of Benchmarks
Researchers have attempted to create objective benchmarks to measure progress toward AGI, but these efforts have been met with challenges. The Abstraction and Reasoning Corpus (ARC-AGI) is one such benchmark, which tests AI systems’ ability to solve novel visual puzzles that require deep and novel analytical reasoning.
However, even sophisticated benchmarks like ARC-AGI face limitations. They are still trying to reduce intelligence to a score, which is a complex and multifaceted concept that cannot be captured by a single metric. Furthermore, the problem of data contamination, where test questions end up in training data, can lead to models appearing to perform well without truly “understanding” the underlying concepts.
Source: arstechnica.com