- The paper’s main contribution is formalizing AGI as a system that emphasizes learning and adaptation under limited resources, offering a clearer definition than prior views.
- Xu introduces two axioms—learning as indispensable and operating under resource constraints—that ground AGI in practical, measurable concepts.
- The study contrasts AGI definitions and highlights the need to prioritize adaptability over problem-specific algorithms for achieving genuine intelligence.
What is Meant by AGI? On the Definition of Artificial General Intelligence
Introduction
Let's dive into one of the big questions in AI: defining AGI. This paper by Bowen Xu takes a clear stance on this concept, urging the AI community to align on what AGI actually means. Given that discussions around AGI often circle back to fundamental questions—like "Is AGI even possible?" or "How would we build one?"—it's critical to pin down a solid definition.
Building Blocks of the Definition
Intelligence and Its Many Faces
The paper outlines various existing ideas about intelligence:
- Emergent from the brain
- Manifested through sophisticated behaviors
- Capability to solve problems
- Summation of cognitive functions
- Adapting to environments
Xu zeroes in on the idea that adaptation or learning is essential to intelligence. In simpler terms, if a system can't adapt or learn, it's not intelligent, even if it's great at solving specific problems.
The Two Axioms
Xu formalizes this with two foundational axioms:
- Learning is crucial: If a system is intelligent, it can learn and adapt to its environment.
- Limited resources: An intelligent system operates with limited computational resources—space (memory) and speed (processing).
These axioms ensure the discussion stays grounded in reality. Even the most cutting-edge AI models like LLMs operate within resource limitations.
Defining the Terms
Intelligence
Xu's definition of intelligence takes into account the ability of a system to adapt using limited resources. This makes room for traditional Machine Learning methods and highlights the importance of the adaptability process.
Artificial
The term artificial differentiates AGI from natural intelligence, like human or animal intelligence. The boundary here can blur when it comes to bio-technology, but Xu sticks to conventional views, excluding biotechnological creations like cloned organisms from the AGI category.
General Intelligence
General intelligence is characterized by adaptability in an open environment, meaning:
- Unpredictable situations
- Future inconsistent with past experiences
This adaptability to unknown and evolving circumstances is what sets general intelligence apart from problem-specific algorithms.
What Exactly is AGI?
Xu proposes that AGI is a computer system capable of adapting to open environments with limited resources, adhering to a set of principles. This definition helps distinguish AGI from systems designed for specific problems.
Comparing Definitions
Xu’s definition isn't completely disconnected from other popular definitions:
- The "Sparks of AGI" paper emphasizes intelligence features like reasoning, planning, and learning (Xu agrees on the learning part as fundamental).
- DeepMind's "levels of AGI" focus on problem-solving breadth and depth but may overlook adaptability (Xu sees adaptability as crucial).
- Pei Wang’s definition, which sees intelligence as an adaptive capacity under limited resources, aligns closely with Xu’s view on general intelligence.
Implications and Future Directions
The paper underscores that focusing too much on problem-solving might sidetrack us from understanding true intelligence, which involves adapting and learning. Identifying AGI principles remains an open discussion, but a better definition guides researchers in the right direction.
As the definition clarifies, tackling AGI involves more than just scaled-up models or better algorithms. It’s about building systems that can navigate and adapt to changing, unpredictable environments with finite resources. That’s the ultimate goal, and understanding this nuanced take on AGI moves the field one step closer.
Conclusion
In wrapping up, the paper urges the AI community to use this unified definition as a base. By clearly differentiating intelligence from mere problem-solving capability and emphasizing adaptation in open environments, this definition aims to bring us closer to creating thinking machines.
Without sensationalism, Xu’s approach provides clarity and a structured framework that current and future AI researchers can build upon. Certainly, as AI continues to grow, these foundational definitions will shape how we think about and develop AGI.