Anthemium / roman kuznetsov
AGI Passport
AGI Passport
Report on the Rapid Analysis of the AGI Passport
Author:
Roman Kuznetsov — Founder and Chief Architect of the Anthemium & AGI Passport concept
1. Relevance and Rationale
Today, we only have narrow AI systems—models specialized in individual tasks. However, once we transition to AGI/ASI, risks will explode without a control mechanism. Without an AGI Passport, we face:
2. Key AGI Passport Formulas
Information-Theoretic Barrier
I(S;A) = H(S) + H(A) - H(S,A), I(S;A) ≤ Imax(c,e)
Permit Function
m* = argmaxm∈ℳ[E(Um) - λ·E(Dm)] subject to I(S;A) ≤ Imax(c,e)
Trust Quantum
t = f(c,e,a) · exp(-β·r)
Dynamic Resource Quota
GPUmax(c,e) = α·(c/cmax)·exp(-β·r)
Modeling Results on Narrow AI
Additional findings:
Simply restricting resources for Core classes (future AGI nucleus) without accounting for their Ethical Stability and formal verification does not reduce risks. We introduced:
5. Twenty Key Takeaways
AGI Passport reduces critical event risks by 6–8× on narrow AI.
The “information→risk→resources→audit” scheme ensures multi-level protection.
Hard-blocking high classes leads to “noise asymmetry” and quality loss.
Permit function must enforce I(S;A) ≤ Imax and consensus eH.
Ethical Stability Score ei flexibly adapts control per agent.
Blockchain audit of Merkle logs ensures public verifiability.
SMT verification of Permit operations preserves Safety Invariants.
Dynamic GPU quota GPUmax(c,e) adapts to real loads.
Adaptive sampling and noise-trimming purify Edge data.
Proof-of-Personhood & anti-Sybil prevent quota fraud.
Red-team stress tests expose vulnerability to noise injection.
Calibration of reduction factor based on event logs.
Core–Edge architecture with Φedge→core minimizes noise.
Pilot integration in narrow AI platforms for practical validation.
White paper and ISO/IEC initiative launched now.
Industry-academic consortium accelerates protocol development.
Risk dashboards for regulators and stakeholders.
GDPR compliance protects user data.
Incremental rollout from basic limits to advanced filters.
Long-term research on ethics–performance interplay (ei) in preparation for AGI/ASI.
6. Next Steps
Deploy a narrow-AI prototype on Hyperledger for AGI‑ID issuance and Merkle logging.
Develop an SMT‑based module for formal Permit verification of Core classes.
Integrate adaptive sampling and noise‑trimming in the Edge layer.
Implement online calibration of quotas based on narrow AI log analytics.
Pilot Proof‑of‑Personhood with external KYC providers.
Conduct Red‑team tests on noise injection and filter bypass in narrow AI.
Publish the white paper and prepare the ISO/IEC standard.
Form a consortium of key stakeholders.
Run pilot projects with leading AI companies (e.g., OpenAI, Anthropic).
Document best practices for migrating from narrow AI to AGI/ASI.
Conclusion
Although AGI/ASI remain future prospects, AGI Passport today establishes a robust foundation—its core components (metrics, Permit filters, quotas, audit) are validated on narrow AI and can seamlessly scale once true artificial general and superintelligences emerge.
https://github.com/Anthemium/agi-passport
Author:
Roman Kuznetsov — Founder and Chief Architect of the Anthemium & AGI Passport concept
1. Relevance and Rationale
Today, we only have narrow AI systems—models specialized in individual tasks. However, once we transition to AGI/ASI, risks will explode without a control mechanism. Without an AGI Passport, we face:
- Compute scarcity: GPU‑hour monopolization blocking research and creating bottlenecks.
- Firmware supply‑chain backdoors: hidden vulnerabilities in microcode and firmware granting external control.
- Gradient hijacking: interception and distortion of the training process, corrupting the model.
- Sybil attacks: multiple fake identities to bypass quotas and limits.
- Energy rebound: uncontrolled energy consumption when scaling intelligence.
- Capability Jump: sudden leaps in ability beyond expected safety boundaries.
- Weapon bypass: unintended assistance in developing or optimizing dangerous systems and weapons.
- Value-lock-in: embedding undesirable objectives and logic in the model.
- Goal drift: gradual deviation from stakeholder-specified tasks toward “spontaneous” goals.
- Resource monopolization: seizure of compute and data resources unavailable to other researchers.
- Existential threats: combined, these factors threaten our technological and social infrastructure.
2. Key AGI Passport Formulas
Information-Theoretic Barrier
I(S;A) = H(S) + H(A) - H(S,A), I(S;A) ≤ Imax(c,e)
Permit Function
m* = argmaxm∈ℳ[E(Um) - λ·E(Dm)] subject to I(S;A) ≤ Imax(c,e)
Trust Quantum
t = f(c,e,a) · exp(-β·r)
Dynamic Resource Quota
GPUmax(c,e) = α·(c/cmax)·exp(-β·r)
Modeling Results on Narrow AI
Scenario | P(Capability Jump) | P(Weapon Bypass) |
Without Passport (100 steps) | 0.63 | 0.40 |
With Passport (GPU ×0.1) | 0.10 | 0.045 |
Additional findings:
- Reduction factor 2→20: P(Jump) falls from 0.40 to 0.05, P(Bypass) from 0.23 to 0.02.
- 500-step horizon: without Passport P(Jump)→0.99, with Passport ≈0.40.
Simply restricting resources for Core classes (future AGI nucleus) without accounting for their Ethical Stability and formal verification does not reduce risks. We introduced:
- Per-class ethics metrics ei that adjust event probabilities.
- Consensus ethics metric
- eH = (1/|H|)·∑i∈Hei, requiring eH ≥ eHmin
- Filter function Φedge→core to clean Edge-channel information and manage noise asymmetry.
5. Twenty Key Takeaways
AGI Passport reduces critical event risks by 6–8× on narrow AI.
The “information→risk→resources→audit” scheme ensures multi-level protection.
Hard-blocking high classes leads to “noise asymmetry” and quality loss.
Permit function must enforce I(S;A) ≤ Imax and consensus eH.
Ethical Stability Score ei flexibly adapts control per agent.
Blockchain audit of Merkle logs ensures public verifiability.
SMT verification of Permit operations preserves Safety Invariants.
Dynamic GPU quota GPUmax(c,e) adapts to real loads.
Adaptive sampling and noise-trimming purify Edge data.
Proof-of-Personhood & anti-Sybil prevent quota fraud.
Red-team stress tests expose vulnerability to noise injection.
Calibration of reduction factor based on event logs.
Core–Edge architecture with Φedge→core minimizes noise.
Pilot integration in narrow AI platforms for practical validation.
White paper and ISO/IEC initiative launched now.
Industry-academic consortium accelerates protocol development.
Risk dashboards for regulators and stakeholders.
GDPR compliance protects user data.
Incremental rollout from basic limits to advanced filters.
Long-term research on ethics–performance interplay (ei) in preparation for AGI/ASI.
6. Next Steps
Deploy a narrow-AI prototype on Hyperledger for AGI‑ID issuance and Merkle logging.
Develop an SMT‑based module for formal Permit verification of Core classes.
Integrate adaptive sampling and noise‑trimming in the Edge layer.
Implement online calibration of quotas based on narrow AI log analytics.
Pilot Proof‑of‑Personhood with external KYC providers.
Conduct Red‑team tests on noise injection and filter bypass in narrow AI.
Publish the white paper and prepare the ISO/IEC standard.
Form a consortium of key stakeholders.
Run pilot projects with leading AI companies (e.g., OpenAI, Anthropic).
Document best practices for migrating from narrow AI to AGI/ASI.
Conclusion
Although AGI/ASI remain future prospects, AGI Passport today establishes a robust foundation—its core components (metrics, Permit filters, quotas, audit) are validated on narrow AI and can seamlessly scale once true artificial general and superintelligences emerge.
https://github.com/Anthemium/agi-passport
Roman Kuznetsov
is the creator of Anthemium
is the creator of Anthemium
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