Supporting AI Safety Research

I'm Sheldon K Salmon, an independent AI safety researcher who has spent 9 months designing AION-BRAIN - a comprehensive cognitive architecture for making AI systems safer across critical domains.

What I've Built

AION-BRAIN is a complete architecture for 30 specialized AI safety engines covering:

• Medical reasoning validation
• Legal accuracy verification
• Financial analysis safety
• Temporal attack detection
• Cross-domain consistency

The architecture includes 1,350+ files of specifications, methodologies, and test frameworks. It represents a systematic approach to AI safety that goes beyond single-domain solutions.

Current Status & Need

✅ Architecture Complete: 30-engine design with full specifications
✅ Documentation: Comprehensive methodology and test frameworks
🔄 Implementation: Python code exists but needs API funding for validation
🔄 Validation: All metrics currently design targets, not validated results

I need funding for:

• API credits to test and validate the engines
• Dataset licensing for medical/legal validation
• Development time to move from architecture to implementation

Why Sponsor This Work?

1. Novel Contributions: My temporal security framework detects attacks beyond step-by-step reasoning (biological, social, narrative time manipulation)

2. Transparent Research: All work is open-source with honest status reporting

3. Practical Impact: The architecture addresses real-world AI safety needs in healthcare, legal, and financial domains

4. Foundation for Collaboration: This isn't just my project - it's a foundation for community research in AI safety

Funding Goals

Immediate Goal: $500

• Validate Decision Engine with temporal attack detection
• Test 1,000 scenarios across medical/legal domains
• Publish initial validation results

Medium-term: $2,000/month

• Full validation of 7 core engines
• Collaboration with academic researchers
• Production of peer-reviewed papers

What Sponsors Get

All sponsors:

• Name in research acknowledgements
• Monthly progress updates
• Early access to research findings

Higher tiers:

• Consultation on AI safety architecture
• Co-authorship opportunities
• Custom safety analysis for your projects

Transparency Promise

I commit to:

• Monthly progress reports showing exactly how funds are used
• Open sharing of all results (positive or negative)
• Honest communication about challenges and setbacks
• Collaborative approach - this is community research, not a closed project

Security Transparency

✅ Live Security Dashboard - Real-time vulnerability monitoring
✅ Monthly Security Reports - Automated transparency updates
✅ Dependabot Protection - Automatic dependency updates
✅ Enterprise-Validated Methodology - [View Report]

All using sustainable, free open-source tooling.

This work represents the kind of systematic, transparent AI safety research we need as we approach more capable AI systems. Your sponsorship helps move from architecture to validated implementation.

Thank you for considering support.