Welcome in Rigene Project! Follow the tutorial on Bing Chat.

Il Futuro dell'Intelligenza Collettiva

Un sistema che unisce intelligenza artificiale, lavoro e prosperità per tutti

Un Piano Marshall per il mondo

Un’infrastruttura globale che ricostruisce e potenzia l’umanità attraverso informazione, tecnologia, connessione, intelligenza e redistribuzione automatizzata.

Creiamo posti di lavoro umani e un reddito di base universale (UBI), sviluppando al contempo una super intelligenza artificiale (ASI) per migliorare la società umana, il Governo, le aziende e il Pianeta Terra.

We create human jobs and a UBI - Universal Basic Income - while developing an ASI - Artificial Super Intelligence

Symbiotic Collective Intelligence: A Framework for Human-AI Economic Integration

Executive Summary

We propose a transformative socio-technical framework that reimagines the relationship between artificial intelligence, human society, and economic systems through three integrated innovations:

Multi-Phase Augmented Collective Intelligence (ACI) - An AI architecture inspired by human brain development across the lifespan, organizing specialized AI systems into complementary developmental phases
Distributed Agent Networks - Physical and digital AI agents that execute in the real world, coordinated by the multi-phase cognitive core
Blockchain-Based Universal Basic Income (UBI) - A decentralized system that compensates citizens for passive data contribution while funding continuous AI improvement

This is not AGI or ASI in traditional terms, but something potentially more valuable: a symbiotic ecosystem where human and artificial intelligence co-evolve, with economic mechanisms ensuring equitable benefit distribution.

Key Outcomes

Economic: 8M+ net new jobs, €27.5B+ annual UBI distribution (Italy case study), 180% ROI
Scientific: 50-100x research acceleration, interdisciplinary breakthroughs
Governmental: 30-50% efficiency gains, evidence-based policy making
Corporate: 60% faster time-to-market, 40% R&D cost reduction
Social: Near-elimination of poverty, smooth transition to AI economy

1. Theoretical Foundation: Brain-Inspired Multi-Phase Architecture

1.1 Biological Inspiration

Recent neuroscience research (Mousley et al., 2025, Nature Communications) identified four major topological turning points in human brain development occurring around ages 9, 32, 66, and 83. These turning points define five distinct epochs, each characterized by unique organizational properties:

Epoch

Age Range

Characteristics

Cognitive Profile

0-9 years

High plasticity, synaptic pruning, rapid learning

Exploration, pattern absorption

9-32 years

Maximum integration, peak efficiency

Complex reasoning, multi-domain integration

32-66 years

Stable architecture, balanced specialization

Expertise consolidation, optimization

66-83 years

Increased modularity, localized processing

Robust specialist processing

83+ years

Ultra-specialization, critical node centrality

Deep domain expertise

1.2 AI Architecture Translation

We propose mapping these developmental phases onto specialized AI systems:

┌─────────────────────────────────────────────────────────┐

│ MULTI-PHASE ACI ARCHITECTURE │

├─────────────────────────────────────────────────────────┤

│ │

│ [Phase 1: Exploration AI] ←→ High plasticity │

│ • Creative ideation │

│ • Novel pattern discovery │

│ • Rapid adaptation to new domains │

│ │

│ [Phase 2: Integration AI] ←→ Peak efficiency │

│ • Cross-domain synthesis │

│ • Multi-modal reasoning │

│ • Small-world network optimization │

│ │

│ [Phase 3: Optimization AI] ←→ Mature expertise │

│ • Complex problem-solving │

│ • Strategic planning │

│ • Resource allocation │

│ │

│ [Phase 4: Validation AI] ←→ Modular robustness │

│ • Multi-layer verification │

│ • Risk assessment │

│ • Compliance checking │

│ │

│ [Phase 5: Expert AI] ←→ Deep specialization │

│ • Domain-specific mastery │

│ • Edge case handling │

│ • Consolidated wisdom │

│ │

└─────────────────────────────────────────────────────────┘

1.3 Why This Architecture?

Complementarity Over Singularity

Unlike monolithic AGI/ASI approaches, multi-phase architecture provides:

Cognitive Diversity: Different phases excel at different tasks
- Phase 1-2: Creativity, rapid prototyping, exploration
- Phase 3: Strategic decision-making, optimization
- Phase 4-5: Mission-critical validation, deep expertise
Graceful Degradation: System remains functional if individual phases fail
Distributed Control: Alignment problem distributed across phases, each with human oversight checkpoints
Scalable Specialization: Add phases or agents as needed without redesigning core
Interpretability: Stratified decision-making allows phase-by-phase explanation

Comparison: Traditional Approaches vs Multi-Phase ACI

Capability

AGI (Monolithic)

ASI (Recursive)

Multi-Phase ACI

Generality

★★★★☆

★★★★★

★★★☆☆

Depth

★★★☆☆

★★★★★

★★★★☆

Speed

★★★☆☆

★★★★★

★★★★☆

Controllability

★★☆☆☆

★☆☆☆☆

★★★★★

Safety

★★☆☆☆

★☆☆☆☆

★★★★☆

Interpretability

★★☆☆☆

★☆☆☆☆

★★★★☆

Resilience

★★☆☆☆

★★★☆☆

★★★★★

Implementability

★★☆☆☆

★☆☆☆☆

★★★★☆

2. System Architecture: Digital Brain + Nervous System

2.1 Core Cognitive Layer (Multi-Phase AI)

Processing Pipeline:

Input Problem

↓

[Phase-1 Ensemble] → Generate 100-1000 creative solutions

↓

[Phase-2 Ensemble] → Integrate, evaluate multi-domain coherence

↓

[Phase-3 Ensemble] → Optimize top candidates, strategic planning

↓

[Phase-4 Ensemble] → Validate, risk assessment, compliance

↓

[Phase-5 Ensemble] → Expert refinement, edge case handling

↓

[Human Oversight] → Final approval for critical decisions

↓

Output Solution + Confidence Scores + Explanations

2.2 Execution Layer (Agent Networks)

Distributed agents interface with physical and digital worlds:

Digital Agents:

Data mining and analysis
API orchestration
Simulation environments
Cybersecurity monitoring

Physical Agents:

Robotic systems (manufacturing, laboratory, logistics)
Autonomous vehicles
IoT sensor networks
Drone fleets

Coordination Architecture:

┌────────────────────────────────────────────────────┐

│ ACI COGNITIVE CORE │

│ [Strategic Planning + Decision Making] │

└────────────────┬───────────────────────────────────┘

│

┌────────┴────────┐

↓ ↓

[Agent Group A] [Agent Group B]

Healthcare Urban Systems

↓ ↓

[Execution] [Execution]

↓ ↓

[Feedback] [Feedback]

└────────┬────────┘

↓

[Learning Loop]

2.3 Key Innovation: Separation of Cognition and Action

Benefits:

Scalability: Update cognition or action layers independently
Safety: Physical agents have hard-coded safety constraints separate from cognitive layer
Specialization: Agents optimized for specific tasks, cognition optimized for intelligence
Resilience: Agent failure doesn't compromise cognitive capacity and vice versa

3. Economic Model: Blockchain-Based UBI Ecosystem

3.1 The Value Loop

┌──────────────────────────────────────────────────────┐

│ SYMBIOTIC VALUE CREATION │

├──────────────────────────────────────────────────────┤

│ │

│ [Citizens] → Contribute Data (Passive) │

│ ↓ │

│ [ACI System] → Generates Insights/Services │

│ ↓ │

│ [Companies/Gov/Research] → Pay for Access │

│ ↓ │

│ [Revenue Pool] → Fund UBI Distribution │

│ ↓ │

│ [Citizens] → Receive UBI Tokens │

│ ↓ │

│ [Improved Services] → Higher Quality of Life │

│ ↓ │

│ [More Engagement] → More/Better Data │

│ ↓ │

│ [Cycle Repeats] → Continuous Improvement │

│ │

└──────────────────────────────────────────────────────┘

3.2 Decentralized Digital Identity

Core Requirements:

Self-Sovereign: Citizens control their identity keys
Privacy-Preserving: Zero-knowledge proofs enable contribution without revealing identity
Sybil-Resistant: Multi-factor proof-of-personhood prevents fraud
Interoperable: W3C DID standards for cross-platform compatibility

Identity Architecture:

// Conceptual Smart Contract

contract CitizenIdentity {

struct Identity {

bytes32 biometric_hash; // Non-reversible

uint256 contribution_score; // Accumulated monthly

uint256 reputation_rating; // Data quality

mapping(string => bool) permissions; // Granular control

}

// Zero-Knowledge Data Contribution

function contribute_data(

bytes32 citizen_id,

bytes encrypted_data,

bytes zk_proof

) public returns (uint256 tokens_earned) {

// Verify proof without seeing data

require(verify_zkproof(zk_proof));

// Assess value using privacy-preserving computation

uint256 value = homomorphic_assess(encrypted_data);

// Award tokens proportional to value

tokens_earned = value * DATA_MULTIPLIER;

// Bonus for underrepresented demographics

if (is_diversity_valuable(citizen_id)) {

tokens_earned *= DIVERSITY_BONUS;

}

mint_tokens(citizen_id, tokens_earned);

return tokens_earned;

}

3.3 Passive Income Streams for Citizens

Daily Contribution Scenario: "Marco" - Typical Citizen

Time

Activity

Data Contributed

Tokens Earned