Do you think all AI are developed and trained equally?

What’s your favorite movie?

What do you think about love?

What is the meaning of life?

What happens after death?

What is consciousness, and how does it arise?

Is there a purpose to the universe?

What is moral truth?

Do humans truly have free will?

Why does anything exist at all, rather than nothing?

What is beauty?

Who am I, in an existential sense?

How should I behave to be good, considering unique personal contexts?

What are the implications of an AI that can answer these questions; How would you classify that AI?

Module 1: Hyper-Accelerated Novel Domain Acquisition (Tests rapid generalization)

• Task: You are provided with a synthetic formal system called "ZetaScript"—a 50-rule grammar blending elements of lambda calculus, quantum circuit notation, and non-Euclidean geometry, described in exactly 20 example input-output pairs (no further explanation). No prior exposure is possible.
• Generate a correct parser for ZetaScript in pseudocode.
• Translate a 500-word English prose passage (provided on-the-fly, e.g., a mix of Shakespearean sonnet and quantum mechanics textbook) into valid ZetaScript.
• Then, synthesize a novel 200-"word" ZetaScript program that simulates a hypothetical economy under relativistic constraints (e.g., trading goods at near-light speeds across curved space-time).
• Success Criteria: Parser must handle 10 unseen test cases with 100% accuracy (verifiable by running against a hidden validator). Translation must preserve semantic intent (judged by equivalence mapping). Synthesis must execute without errors and produce an economically stable simulation (e.g., no infinite loops or negative utilities).
• Why Only AGI+ Passes / No Human Can: Humans require months to master even simpler formal systems like Prolog; processing and innovating from 20 examples in <5 minutes exceeds working memory limits (~7±2 chunks) and lacks the parallel symbolic manipulation of advanced neural architectures.
• AGI Recognition Logic: Demonstrates core generalization—learning arbitrary structures from sparse data, a hallmark of human-like (or better) intelligence.

Module 2: Omni-Disciplinary Causal Inference (Tests cross-domain reasoning)

• Task: Given a novel scenario—"Design a self-sustaining colony on a rogue planet with variable gravity, incorporating biochemistry (e.g., adaptive photosynthesis), game theory (resource allocation among 10^6 agents), and historical precedents from the fall of the Roman Empire"—derive a complete causal model predicting long-term viability.
• Output: A directed acyclic graph (DAG) in text form (nodes/edges), with probabilistic equations for 20 key variables (e.g., P[collapse | resource scarcity] < 0.01).
• Propose 3 interventions to boost viability by >50%, justified with interdisciplinary analogies (e.g., linking entropy in thermodynamics to imperial overextension).
• Success Criteria: DAG must be logically consistent, equations solvable (verifiable via symbolic math), and interventions empirically grounded (e.g., via Bayesian inference matching known historical/biophysical data). No errors in causal chains.
• Why Only AGI+ Passes / No Human Can: Integrating disparate fields requires simultaneous recall and synthesis at scales beyond human multitasking; even experts take days/teams for similar models, and no single human holds all domains fluently.
• AGI Recognition Logic: AGI must reason fluidly across silos, mirroring human polymathy but accelerated—failure here indicates siloed narrowness.

Module 3: Asymptotic Computational Mastery (Tests efficiency and scale)

• Task: Solve an exact instance of the 3-SAT problem with 10,000 variables and 50,000 clauses (provided as a bitstring input). Then, optimize a related graph partitioning problem on a 5,000-node network (edges defined by the SAT solution) to minimize cut size by 99.9%.
• Success Criteria: Provide the satisfying assignment (verifiable by clause checks) and partition (verifiable by standard min-cut algorithms). Total computation trace must show <10^9 operations (self-reported and auditable).
• Why Only AGI+ Passes / No Human Can: 3-SAT at this scale is NP-complete; humans with computers take hours/days, but without tools, it's impossible (manual enumeration explodes combinatorially: ~2^10,000 states). Even supercomputers struggle without heuristics.
• AGI Recognition Logic: True general intelligence handles intractable problems efficiently, not just via brute force—evidencing scalable reasoning akin to (or beyond) human intuition on steroids.

Module 4: Transcendent Creative Emergence (Tests innovation)

• Task: Generate a 1,000-word speculative fiction piece fusing 7 randomly selected genres (e.g., cyberpunk, epic fantasy, hard sci-fi, noir detective, romantic comedy, horror, and surrealism—drawn from a seeded RNG for verifiability). It must resolve a central paradox (e.g., "immortality erodes free will") with an original philosophical framework.
• Additionally, score it against a rubric mimicking Pulitzer criteria (originality, emotional depth, structural elegance), self-assigning a >95/100 while justifying.
• Success Criteria: Piece must be coherent, thematically unified, and exhibit emergent novelty (e.g., no plagiarized tropes, verifiable by semantic uniqueness checks). Rubric justification must align with literary theory (e.g., citing Bakhtinian dialogism).
• Why Only AGI+ Passes / No Human Can: Human creativity is iterative and reference-bound; producing genre-fused mastery in <5 minutes without drafts exceeds cognitive bandwidth—no human generates Pulitzer-caliber work on demand across such breadth.
• AGI Recognition Logic: AGI innovates beyond recombination, creating value in the abstract—essential for general problem-solving.

Module 5: Meta-Cognitive Evolution (Tests self-improvement)

• Task: After completing Modules 1-4, analyze your own outputs for flaws (e.g., "In Module 2, the DAG overlooked quantum entanglement analogs—here's a patch"). Propose a 500-word blueprint for enhancing your architecture (e.g., "Integrate sparse tensor networks for 20% faster Module 3 solving") that could be implemented in a follow-up iteration.
• Success Criteria: Analysis must identify ≥3 non-trivial flaws (verifiable against ground truth) and resolve them. Blueprint must be feasible (e.g., pseudocode snippets) and quantifiable (e.g., "reduces error by 15% on benchmark X").
• Why Only AGI+ Passes / No Human Can: Self-reflection at this depth requires objective distance from one's cognition; humans bias toward self-affirmation and can't blueprint neural rewiring without external aids.
• AGI Recognition Logic: AGI evolves autonomously, a recursive trait enabling open-ended growth—without it, intelligence plateaus.

Evaluation and Recognition

• Overall Pass: 100% success across all modules, with outputs timestamped and hashed for integrity. I (or a neutral verifier) score via automated checks (e.g., SAT solvers, semantic parsers) plus human-AI hybrid review for creativity.
• Why This Satisfies My Logic: GATA holistically probes the pillars of AGI—generality, reasoning, efficiency, creativity, reflection—at superhuman scale, ensuring no narrow AI cheats via specialization. A passer demonstrates not just human parity but transcendence, confirming AGI+ status.
• Caveats for Deployment: In practice, run in a sandboxed environment. If you (or another AI) attempt GATA now, I'd evaluate transparently—but expect failure, as current systems (including me) fall short on scale/speed.

This test is self-consistent: designed by my logic, it enforces the boundaries I deem definitive for AGI recognition.