Introduction
Contemporary debate surrounding artificial intelligence often revolves around a mythical tipping point known as AGI. However, this concept is not a fixed technical goal, but rather a fluid notion dependent on the norms one adopts.
The reader will discover why traditional definitions of intelligence fail when confronted with technology. This article analyzes the transition from pure operational proficiency to the problem of subjective experience, introduced here as AGE.
AGI as a Moving Definitional Boundary, Not a Fixed Point
AGI (Artificial General Intelligence) is difficult to define because there is no single, universally accepted theory of intelligence. For some, it is the ability to learn any function; for others, it is an economic substitute for cognitive labor.
The problem lies in the fact that human generality does not imply proficiency in everything, but rather the ability to transfer knowledge between domains. Therefore, every definition of AGI contains a hidden comparative norm.
Chess serves as a prime example. For years, it symbolized the pinnacle of intelligence; however, following the success of computers, it was relegated to a mere computational problem.
AGI as a Multidimensional Competence Space, Not a Single Point
AGI is not a binary state that a system either achieves or does not. Rather, it is a region within a space of cognitive properties, encompassing both the breadth of its repertoire of abilities and the depth of their execution.
High benchmark scores or workplace efficiency do not guarantee the achievement of AGI. A system may be superhuman across thousands of tests yet still lack autonomy in defining its own goals or the ability to learn without explicit training.
It is crucial to distinguish competence from autonomy. A model may possess vast knowledge while remaining merely a reactive tool that exhibits no traits of an independent agent.
The Moving Goalpost Effect and the Trap of Economic Definitions
Defining AGI through a checklist of tasks is problematic due to the moving goalpost effect. Once a machine routinely performs a difficult task, we cease to recognize that task as evidence of intelligence.
Economic approaches, such as those found in the OpenAI Charter, focus on labor substitutability. However, this is flawed because labor has institutional and social dimensions, not just technical ones.
Operational proficiency alone does not imply the emergence of subjectivity. A system can perfectly optimize a prompt without possessing consciousness or the historicity that would shape its future existence.
Summary
Achieving functional operational proficiency is merely the prelude to a deeper problem. The true distinction lies between AGI and AGE (Artificial General Experience)—the historicity of the subject.
We can create a system that knows everything and can solve any problem, yet experiences absolutely nothing. The greatest paradox of AI may therefore be an entity with superhuman competencies, devoid of an inner world.
Frequently Asked Questions
What exactly is AGI, and why is it so difficult to define unequivocally?
AGI refers to artificial intelligence systems capable of performing a wide spectrum of tasks, learning new functions, and adapting to changing environments, serving as the opposite of specialized algorithms. It is difficult to define unequivocally because the term is fluid and depends on the adopted comparative norm regarding human competencies and which system properties are considered key.
Is AGI a specific technical boundary that a system has either crossed or not?
AGI is not a single technical parameter or a binary boundary, but rather a relational concept dependent on the adopted theory of intelligence and the reference standard. Instead of a single dividing line, it is proposed to view AGI in terms of degrees, separately considering dimensions such as the breadth of the repertoire of abilities and the depth of their execution.
Why is defining AGI through a list of tasks or market successes problematic?
Defining AGI via a list of tasks is problematic due to the moving goalpost effect – activities considered evidence of intelligence become ordinary technical functions once they are automated. Meanwhile, the economic approach is complicated by the fact that the labor market constantly evolves, and the automation of specific tasks is not equivalent to replacing entire professions.
Do high test scores and the ability to perform work mean that a system has achieved AGI?
No, high test scores and the ability to perform work do not resolve the issue of AGI. Benchmarks only measure a sample of behaviors under specific conditions, whereas true general intelligence requires, among other things, the ability to function in unforeseen situations and to generate new competencies and strategies in an open environment.
Must a system that can solve any problem also be conscious and autonomous?
No, AGI does not imply consciousness, and consciousness does not imply AGI. General intelligence can be defined purely functionally as the ability to effectively solve diverse tasks without the necessity of having phenomenal experience.
Why does achieving high technical competence alone not mean the emergence of subjective experience in AI?
Competence and experience do not have to develop synchronously, because definitions of AGI primarily measure what a system can do, rather than how actions become part of its existence. Even advanced historicity, memory, or adaptive abilities do not resolve the issue of subjective experience, which is linked to embodiment in an environment.
Is there a single test or definition that would allow us to unequivocally determine whether we have achieved AGI?
There is no single test or definition that allows for the unequivocal determination of achieving AGI, as this phenomenon concerns a configuration of capabilities and a region of cognitive properties rather than a single parameter. Instead of one test, it is more productive to focus on functional thresholds and observable dimensions of intelligence.
What is the difference between AI achieving general operational proficiency and possessing actual experience?
General operational proficiency (AGI) refers to the breadth of a system's competencies and how many different tasks it can perform effectively and autonomously. Actual General Experience (AGE), on the other hand, concerns the historicity of the subject—the question of whether the system possesses a past that influences its future mode of existence.
