Redefining the Path to AGI: From Vague Goals to Robust Benchmarks

The Current Impasse: A Critique of AGI Discourse

The present concept of Artificial General Intelligence (AGI) is characterized by a problematic vagueness. It lacks a universally accepted definition and a falsifiable method for its verification. Predictions like Kurzweil's 2029 timeline often resemble speculative futurism more than grounded science, and the term has been co-opted as a powerful, yet nebulous, marketing narrative by entrepreneurs. This creates a landscape where the profound goal of creating general machine intelligence is conflated with hype, making serious scientific progress difficult to measure and assess.

Proposal for a Robust AGI Benchmark & Methodology

To move beyond this impasse, a new framework is required. This framework must shift the focus from a single, mythical "AGI switch" to a multi-faceted, measurable, and gradual assessment of capabilities. The core principle is to measure generality, autonomy, and robustness across a wide spectrum of tasks.

Core Tenets of the Framework

Multi-Dimensional Benchmark Suite (MDBS): AGI cannot be proven by a single test. Instead, performance must be evaluated across a diverse suite of challenges that probe different facets of intelligence.

Quantifiable Metrics: Every task must have clear, objective success criteria, moving beyond subjective judgments of "intelligence."

A Spectrum, Not a Binary: The outcome is not a "yes/no" for AGI, but a detailed profile showing strengths, weaknesses, and the degree of generality, much like a capabilities radar chart.

Proposed Benchmark Methodology

1. Physical Reasoning & Embodiment

This goes beyond the "Coffee Test." A suite of physical and virtual tasks requiring an understanding of object permanence, gravity, cause and effect, and tool use. Metrics include task completion speed, efficiency, and success rate in novel environments.

2. Social & Ethical Intelligence

Benchmarks would assess theory of mind, understanding of nuance, negotiation, and handling ethical dilemmas. This is measured through complex, multi-party interactive scenarios where the AI must infer intent and manage social dynamics, with success judged by human participants and adherence to predefined ethical principles.

3. Open-Ended Learning & Adaptation

This is a core test for catastrophic forgetting and meta-learning. The system is exposed to a series of unrelated tasks (e.g., learning a game, then a language, then a scientific problem). The key metric is the "knowledge retention rate" and the "skill acquisition cost" (data/time required) for each new task.

4. Creativity & Problem Finding

Moving beyond problem-solving, this measures the ability to identify novel problems, propose valid scientific hypotheses, or generate artistically coherent and original works. Evaluation would use a consensual assessment technique by human experts in the relevant field.

5. Economic & Strategic Metareasoning

A long-term simulation where the AI must manage resources, set and re-prioritize its own goals in a dynamic environment, and strategize under uncertainty. Performance is measured by long-term viability and achievement of complex, multi-step objectives.

6. Explainability & Transparency

The system must be able to articulate its reasoning, report on its uncertainty, and explain its decisions in a human-comprehensible way. This is not a side-channel but a core benchmark, measured by the ability of humans to accurately understand and predict the AI's behavior.

Implementation and Verification

Tiered Evaluation: The benchmark suite would be tiered, from simpler "broad AI" tests to progressively more difficult "human-level" and ultimately "superhuman" challenges. This allows for tracking progress incrementally.

Independent Auditing: To counter hype, testing must be conducted by independent, accredited bodies under standardized conditions, with all results and methodologies made public for peer review.

The Verdict: An AI would not "become AGI." Instead, its performance across the MDBS would be published. A system performing robustly at or beyond the human tier across all dimensions would, de facto, be considered an AGI by the community, based on evidence rather than proclamation.

This methodology replaces a naive and vague target with a rigorous, multi-disciplinary, and continuous evaluation process. It transforms AGI from a entrepreneur's concoction into a measurable scientific frontier.