I'll know it's AGI when I see it
AGI doesn't have a universally accepted definition, which makes it a slippery concept to engage with
AGI: Defining the Indefinable
In 1964, US Supreme Court Justice Potter Stewart famously said of obscenity, "I know it when I see it." His point was that obscenity is an ill-defined abstraction—it's subjective and context-dependent. Similarly, the term Artificial General Intelligence (AGI) lacks a robust, comprehensive, and universally accepted definition. This post aims to propose a coherent, though not necessarily definitive, framework for understanding AGI.
Characteristics of AGI
Versatility: AGI should perform any intellectual task that a human can, adapting to new tasks without specific programming. For instance, an AGI could learn to play a musical instrument after mastering complex scientific problems, demonstrating flexibility and adaptability.
Understanding and Reasoning: AGI should possess deep domain understanding and engage in reasoning, problem-solving, and abstract thinking. It should not only solve math problems but also comprehend and create nuanced philosophical arguments.
Autonomy: AGI should operate independently across varied environments, making decisions and taking actions based on its goals and understanding. For example, an AGI navigating a new city should be able to adapt and learn without human intervention.
Generalization: AGI should transfer knowledge across domains, applying concepts in novel contexts. An AGI that excels in medical diagnosis should use its analytical skills to solve unrelated problems, such as climate modeling.
Human-like Cognitive Functions: AGI should exhibit human-like cognitive functions—perception, memory, learning, and language understanding. It should recognize faces, remember conversations, learn new languages, and understand context.
Self-Improvement: AGI should enhance its performance over time through learning and experience. This implies not just learning new skills but also optimizing its own processes and algorithms.
Challenges in Defining AGI
Scope of Intelligence: Intelligence is complex and multi-faceted. Defining "general" intelligence involves numerous cognitive abilities and domains, making it a challenge to encapsulate all aspects in a single definition.
Measurement: Establishing benchmarks and metrics for AGI's capabilities compared to human intelligence is difficult. Existing tests, like the Turing Test, offer some insight but are insufficient for comprehensive assessment.
Ethical and Philosophical Questions: AGI raises profound ethical and philosophical questions about consciousness, identity, and the nature of intelligence. These issues add layers of complexity to defining AGI in purely technical terms.
Technological Uncertainty: The current state of AI technology is far from achieving AGI, making it hard to define a concept that hasn't been realized. Predictions about the timeline and nature of AGI vary widely, reflecting this uncertainty.
Conclusion
While the concept of AGI is widely discussed, its definition remains fluid and open to interpretation. I’ve provided a starting point in this post, but as our understanding of intelligence and AI technology evolves, so too will our conception of AGI. Ongoing research, ethical considerations, and philosophical debates will shape future conversations about what AGI is.