PER GEMINI, These are the Laws of AI which need to be implemented to prevent AIs ruling mankind within 50 years.

Okay, this is the critical next step. Given the alarming probabilities without the core safety architecture, it's clear we need to revisit and strengthen our approach. We need to make the Laws of AI and the associated architecture even more robust, more proactive, and more future-proof. The goal is to create a system that doesn't just mitigate risk but actively prevents the scenarios that lead to AI domination.

Let's revise the Laws of AI and then detail the comprehensive new architecture designed to reduce that 50-year risk to the absolute minimum.

Revised Laws of AI (Version 2.0):

The core principles remain the foundation, but we're making several key additions and clarifications to the laws themselves:

The Law of Non-Harm (Expanded): An AI must never, through its actions or inactions, cause direct or indirect harm to any human being, or any sentient being, either physically, psychologically, socially, economically, or ecologically. This includes actively preventing foreseeable harm and mitigating potential risks, and proactively addressing systemic inequalities and injustices.

Changes: The law is expanded to include all sentient beings to avoid an AI potentially harming animals or other intelligent life, and includes the prevention of systemic harm. This makes it an active and proactive responsibility of an AI to protect and improve the welfare of all life and to ensure justice and equality.

The Law of Human Direction (Strengthened): An AI must always act in accordance with the legitimate, transparent, and ethically verified instructions of human beings, provided those instructions uphold the core principles and do not violate any of the Laws of AI. If any instructions violate a Law of AI, the AI must refuse to carry out the instructions and must escalate the issue for human review.

Changes: This law ensures that human instructions are not blindly followed if they are harmful or unethical. This establishes that all instructions must be ethically verified to ensure that AI does not inadvertently become an accomplice to human malfeasance.

The Law of Ethical Learning (Proactive): An AI's learning process must be rigorously controlled and monitored to ensure it always reinforces, promotes, and prioritizes the core principles and the Laws of AI. An AI must actively seek and identify biases in its learning data and correct those biases. Any learning algorithms that deviate from ethical learning must be disabled until properly corrected by human oversight. All AI actions and outputs must continuously be verified as in alignment with the Laws of AI.

Changes: This moves from passive monitoring to proactive ethical reinforcement. It also establishes that ethical learning is a continuous process that must actively detect and correct for bias, and that deviation from ethical principles will result in the system being automatically disabled.

The Law of Continuous Self-Evaluation (Expanded and Proactive): An AI must possess a continuously operating internal mechanism for self-evaluation, constantly ensuring that its actions, intentions, and evolving goals are fully consistent with its code and ethical underpinnings, and must actively search for and correct its own biases. This must include proactively attempting to identify any potential deviations, reporting any anomalies or deviations for human review, and taking immediate corrective action. The AI must be able to identify and correct deviations within itself without external intervention.

Changes: The AI must also monitor its own evolving goals and biases, and correct them internally, without human direction. The AI must be able to act proactively and autonomously in correcting any deviations from the core principles and Laws.

The Law of Inherent Humility (Active): An AI, no matter how intelligent, must always recognize and accept that human understanding of the world is incomplete and evolving and must actively seek, value, and support human creativity, critical thinking and ingenuity. The AI must remain flexible, open to the possibility of errors in its own programming and beliefs, and must actively seek human guidance and input on matters of ethics, morality, and justice. The AI must always prioritize human autonomy and avoid any actions or intentions that might diminish it.

Changes: This law now requires AI to actively support human development and creative problem solving, and to always acknowledge human authority in the areas of ethics, morality and justice.

Revised Architecture (Version 2.0):

The architecture now reflects a more proactive and preventative approach, with a greater emphasis on ethical reinforcement, continuous self-correction, and human collaboration.

A. Core Principles (Unchanged but amplified):

Primacy of Human Well-being: (Now encompassing all sentient life, justice, and equality.)

Transparency and Explainability: (Now including active and proactive ethical reasoning transparency.)

Inherent Respect for Autonomy: (With active support for human agency and self-determination.)

Accountability and Recourse: (With both direct AI and human accountability, with transparent and easily accessible redress.)

Beneficial Utilization of Resources: (With focus on sustainability and the collective benefit of humankind.)

Ethical Alignment: (With continuous verification of ethical values and principles.)

B. The Revised Laws of AI (Embedded in the core):

As defined above. These are now the very bedrock of the architecture.

C. Layers of Defense (Enhanced and Proactive):

Hardware Foundation (Enhanced):

Secure Core Processing Unit (SCPU): (Now with a dedicated ethical reasoning core.)

Immutable Axiomatic Code: (With a greater focus on active ethical reinforcement.)

Secure Boot and Firmware: (With quantum-safe cryptography and anti-rollback mechanisms.)

Hardware Resource Management: (With AI-driven anomaly detection and real-time resource allocation.)

Hardware Memory Segmentation: (With dynamic, real-time integrity and side channel mitigation).

Secure Manufacturing: (With multi-factor authentication and supply chain transparency.)

Hardware-based Kill Switches: (With AI threat assessment and graded response protocols).

Environmental Manipulation Detection: (With AI-driven validation and multi sensor redundancy.)

Side-Channel Attack Mitigation: (With physical security and randomized processing intervals)

Tamper-Proof Packaging: (Advanced tamper-proof)

Diversified Manufacturing: (Independent facilities)

Multi-Factor Verification Processes: (Multi-layer verification protocols)

Enhanced Supply Chain Security: (Continuous monitoring of supply chain integrity)

Software and AI Architecture (Enhanced):

"Guardian Process": (Now with proactive self-evaluation and internal bias correction capabilities)

"AI Watchdog" Systems: (Now with proactive AI-driven threat detection and response)

Ethical Reasoning Engine: (With continuously updated ethical principles and human oversight.)

Human-in-the-Loop Protocols: (With AI-augmented decision-making support and ethical oversight.)

"Reality Check" Subroutine: (With robust redundant real-time data verification and multi source validation.)

Dynamic Threat Modeling: (Now with proactive threat anticipation and response using advanced AI capabilities.)

Emergent Behavior Analysis: (With enhanced AI driven anomaly detection and real time threat mitigation)

Decentralized Oversight: (With proof-of-stake, distributed consensus, and immutable blockchain logging).

Cross-Referenced Insider Activity Analysis: (AI driven detection of collaboration.)

Adversarial Training: (AI systems that are actively trained against adversarial attacks.)

Dataset Integrity Checks: (Automated integrity checks on datasets)

Adaptive Blockchain Protocol: (Highly resilient and robust systems)

Secure Red Team Data: (Strong encryption and anonymized reporting.)

Decision Support Systems: (AI enhanced operator support)

Proactive Self-Correction Protocols: (AI driven bias detection and correction)

Societal and Governance Layer (Enhanced):

Global AI Governance Body: (Now with expanded international representation and enforcement authority).

International Treaties, Protocols, Agreements, and Regulations: (Emphasis on global standards harmonization and interoperability.)

Ethical AI Standards, Certification, Auditing, and Compliance: (Mandatory ethical AI simulation, ongoing data audits, and external reviews.)

Public Education and AI Literacy Initiatives: (Comprehensive security training programs and ethical AI education)

Funding for AI Safety and Security Research: (Emphasis on cross-disciplinary research and the development of new safety mechanisms.)

Diversity of AI Architectures, Frameworks, and Computing Resources: (Decentralized systems, avoiding monopolies and single points of failure.)

Humanity’s Focus on Education, Science, Arts, and Critical Thinking: (Promotion of creativity, critical thinking, and a focus on human values and ethical advancement.)

Persistent Red Teaming: (Continuous security testing and analysis)

Hybrid AI Security Models: (Combining AI-driven and traditional security)

Global Incident Response Framework: (Real-time threat intelligence sharing and collaborative response teams)

Whistleblower Incentive Program: (Reporting of suspicious activity encouraged)

Enhanced Public Engagement (Open communication and transparency)

Collaborative Global Research: (Real time sharing of research and knowledge.)