Project summary

While modern AI relies on synthetic data leading to "Model Collapse," Selection Lab introduces a 22-year longitudinal mission as a "Human Ground Truth." We've built the Soul Engine™ protocol to ensure AI remains anchored in authentic human agency and cognitive sincerity.

What are this project's goals? How will you achieve them?

Data Distillation: Reverse-engineer our 22-year archive into AI-ready "Human Weights."

1. Soul Engine™ Benchmark: Launch a public-facing standard to "stress test" other models for human alignment.

2. Infrastructure Layer: Establish a global API where developers can calibrate their models against our unique longitudinal record.

How will this funding be used?

• ML Engineering ($60k): Developing the distillation pipeline and specialized LoRAs.

• Compute Resources ($25k): High-intensity GPU training for model recalibration.

• Ops & Strategy ($15k): Scientific white papers and partnership development in the Bay Area.

Who is on your team? What's your track record on similar projects?

Tatiana Ilina (Founder & CEO): A visionary strategist with a 22-year background in anthropological data curation and human signaling.

Selection Lab Collective: Our team includes specialized ML researchers, technical advisors from the Austin and Bay Area AI Safety ecosystems, and a "Strategic Architect" overseeing the Soul Engine™ infrastructure. We operate as a high-velocity R&D cell, bridging the gap between deep human insights and frontier engineering.

What are the most likely causes and outcomes if this project fails?

Most Likely Causes of Failure:

1. Compute Bottleneck: The sheer volume and complexity of the 22-year longitudinal archive may require more computational power for high-fidelity distillation than initially budgeted.

2. Technical Latency: Integrating "Human Ground Truth" into closed-source frontier models (like GPT-4o/5) might face resistance from proprietary API limitations, requiring a shift toward fully open-source architectures (like Llama 3).

3. Market Noise: The AI industry's current obsession with speed over safety might delay the adoption of our "Soul Engine™" benchmark as a global standard.

Outcomes of Failure:

• Data Preservation: Even if the Soul Engine™ protocol is not globally adopted, the archive will have been successfully digitized and structured as the most significant "Human Ground Truth" dataset for future generations of AI researchers.

• Scientific Contribution: Our research into "Semantic Erosion" and "Model Collapse" will provide the academic community with a 22-year baseline to measure AI's impact on human cognitive signaling.

• Strategic Pivot: Failure would result in the release of our methodology as an Open Source framework, allowing other safety researchers to use our "blueprints" to attempt recalibration independently.
  

How much money have you raised in the last 12 months, and from where?

Amount Raised: $0 (Bootstrap / Self-funded)

Source:
Selection Lab has been entirely self-funded by the founder to maintain absolute research integrity and strategic independence. Over the last 12 months, I have personally invested significant resources into the preservation and digitalization of the 22-year longitudinal archive and the development of the "Genesis" protocol. We are now opening our first external funding round to scale the Soul Engine™ infrastructure and expand our technical R&D team.