Our Team

Led by David Neubert, a TRIZ-trained problem solver with deep AGI roots, our team includes NASA’s Mark Fox (TRIZ & systems expert), Army & Marine AI developers, a UT acoustician, & a UT physicist who teaches wave physics at Austin CC & UT. This cross-disciplinary unit blends cognitive architecture, symbolic logic, and audio modeling to pioneer next-gen AGI research within Hyperon.

Company Name (if applicable)

Project details

Proposal: Harmonic Concept Synthesis in MeTTa
By David Neubert

Executive Summary
Hello, & thank you for your time! Aside from being a day 1 white paper SingularityNet investor, I've been an avid follower of Ben Goertzel & his team. I'm excited to propose a “Harmonic Concept Synthesis” to advance AGI concept generation by merging fuzzy/paraconsistent Formal Concept Analysis (FCA) with information-theoretic concept blending in MeTTa for Hyperon. My hybrid background (Coding, AI, Philosophy, Meyers Briggs Mastery, Marine Corps, Professional Musician , TRIZ Problem Solving Expert, Marketing & Brand Consultant) & network (NASA/Army/Marine AI experts) position me to deliver this with help at any wall that might present itself. 

Why I’m Your Guy

- AGI Roots: Early adopter of SingularityNet, Sophiaverse contributor, & AGI investor since the white paper. I've followed closely with the teams efforts, & goals for years. 

- Creative Edge: Musician (Carnegie Hall) + sound engineer. Concept blending mirrors musical composition: layering structure (FCA) and chaos (blending). Using Acoustics in my acoustically treated studio 24/7 access for extra sessions round the clock. I'm also best friends with an Acoustician & Sound Engineer that teaches at the University of Texas here in Austin. Physics, AI, & Waves have many unique case uses & research. 

Mark Fox, my TRIZ mentor & a NASA systems engineer, is one of the only people globally who’s researched crop circle formations firsthand. His documentation reveals consistent, undisturbed node patterns - suggesting formation by resonance or wave phenomena rather than mechanical force. These real-world observations have directly influenced our approach to exploring logic emergence via sound modeling in AGI systems.

Royal Raymond Rife explored early theories connecting wave frequencies to biological phenomena. While controversial, his work illustrates a historical precedent for exploring harmonics in relation to structured change - something we reframe scientifically in our AGI model.

- TRIZ & Systems Thinking: Trained by NASA’s Mark Fox. Solved complex tech/business problems for 20+ years. 
- LLM/Code Skills: Built LLM frameworks, fluent in Python/C++/ Learning MeTTa (fun!).
- Network: Army/Marine AI devs + NASA TRIZ experts & Acoustician Professor for collaboration.
Logic/Philosophy Minor & enjoy solving problems in all industries - hence my strong consulting background with startups to multi million dollar companies via Wizard Academy work. 

Plan

Milestone 1 (20%): Research & Design

- Draft approach: Compare FCA vs. blending in MeTTa.
- Agile timeline: 4-week sprints.
- Framework design: Modular for Hyperon integration.


Milestone 2 (40%) Build & Test

- Code fuzzy/paraconsistent FCA and information-theoretic blending in MeTTa.
- Integrate into DAS.
- Test side-by-side.
- Evaluate novelty using Claude 4.0 (Python priority), ASI LLMs (AGI R&D), and LLaMA3. While ChatGPT struggles with memory consistency, I’ll also run local LLMs to improve continuity & tap into agent-based reasoning from the Agentverse where applicable.

Milestone 3 (40%): Finalize & Document

If MeTTa’s tooling falls short for fuzzy or paraconsistent logic, I’ll pivot to embedding logic rules within a LISP dialect or symbolic Prolog engine, with wrappers to maintain Hyperon compatibility. For model evals, if ASI LLMs become unreliable or inaccessible, Claude 4.0 & GPT-4 will serve as backstops using prompt engineering to simulate domain-specialized scoring. Evaluation continuity is guaranteed.
- Benchmark performance (e.g., novelty >8/10, coherence >70%).
- Docs/APIs for reuse.
- Demo: Concepts feeding into Hyperon’s memory.
Fuzzy & paraconsistent logic structures will be modeled using tagged truth values in MeTTa, leveraging its symbolic meta-reasoning design. Attributes in the FCA context will be extended to include weighted confidence values instead of binary tags. For example, a fuzzy incidence matrix will allow concept blending to inherit partial features during the crossover.

Paraconsistency will be enabled through multi-valued logic macros that can simultaneously allow
A & not-A, with dissonance weights encoded directly in MeTTa’s AST.

Technical Appendix

1. FCA + Blending Interaction Diagram
(Please Use Mermaid.js to visualize given I can't paste into your page here):

graph TD  
    A[FCA: Structured Logic] --> B[MeTTa AST]  
    C[Blending: Probabilistic Novelty] --> B  
    B --> D[Hyperon Memory Integration]  
    D --> E[LLM Evaluation: Claude 4.0/ASI]

Fig. 1. FCA + Blending Interaction Diagram


2. Code Snippets
FCA in MeTTa:
; Define formal context (objects, attributes, relations)  
(define context  
  (objects (cat dog))  
  (attributes (furry fast))  
  (incidence ((cat furry) (cat fast) (dog fast))))  

; Generate concept lattice  
(define lattice (fca context))
Concept Blending in MeTTa:
; Input concepts: "bird" and "machine"  
(define concept1 (bird (wings feathers)))  
(define concept2 (machine (gears circuits)))  

; Blend via information-theoretic crossover  
(define hybrid (blend concept1 concept2  
  (weight novelty 0.7 coherence 0.3)))


LLM Strategy

Using my coding & audio engineering background, I’ll map abstract concept traits to auditory patterns. Novelty will be converted into pitch variation (higher novelty = higher frequency), while coherence will control rhythm & temporal spacing (higher coherence = smoother cadence). For example, a highly novel but incoherent blend might sound like a chaotic synth riff, while a well-integrated concept will produce a harmonic sequence. This lets us perceive cognitive traits as sound for new validation modes.

Recently, visual video sans audio has been dissected into patterns AI has been able to turn into actual sound, to determine what was actually said. This is a real scenario that is being done with AI & simply video of the speaker vibration. We can advance cases like this & beyond with ASI.

At the time of my proposal, Claude 4.0 outperforms ASI in generating maintainable Python code due to superior PEP8 handling, inline type hinting, & error resolution. When creating modular AST handlers for MeTTa integration, Claude’s structure-first completions reduce debugging loops by ~30%. ASI will still be leveraged for concept evaluation, particularly when generating or refining AGI memory graphs. It shines in conceptual blending, but Claude handles practical code synthesis more effectively.

Claude 4.0 – Preferred for Python due to superior PEP8 compliance, inline type hinting, & reliable docstring generation.
ASI LLMs – Ideal for AGI R&D, especially concept graph synthesis within Hyperon memory.
GPT-4 / LLaMA3 – Used as fallback for general evaluations & comparative testing.

Why Claude 4.0 > ASI for Python?

Benchmarks show Claude 4.0 generates 20% fewer syntax errors & 30% better docstrings than ASI/GPT-4.
Critical for clean, maintainable code in MeTTa/DAS integration.

I remain open to utilizing any & all recommended methods that align with the project’s goals, especially where alternative logic systems or symbolic wrappers can strengthen Hyperon compatibility.


Metrics & Validation

Each test concept will be evaluated across:

- Novelty (scored by LLMs on a 1–10 scale)
- Coherence (human rating panel: % alignment to original concepts)
- Retention (24+ hour memory persistence in Hyperon)
- Utility (integration success in symbolic workflows)

*Again, my apologies, but I'm not seeing a way to insert my graph here*
- Novelty: LLM score >8/10 (Claude 4.0 eval).
- Coherence: Human-rated >70% relevance to input concepts.
- Integration: Concepts must persist in Hyperon’s memory for >24 hours.
- Benchmark: FCA vs. blending accuracy on 100 test cases (e.g., “bird + machine = ornithopter drone”).


Budget Breakdown

Total: $50K

M1 ($10K):
- 2 months part-time dev (FCA/blending research): $6K
- TRIZ/Physics consultation (Mark Fox): $4K

M2 ($20K):
- 3 months full-time coding (MeTTa/DAS integration): $12K
- LLM API costs (Claude 4.0 + ASI): $5K
- Jupyter prototyping: $3K

M3 ($20K):
- Benchmarking + documentation: $10K
- Demo development: $10K

Links and references

• "Da Vinci & the 40 Answers by Mark L. Fox (TRIZ strategy)"

• "Ben Goertzel's research (from Lisa Paser collabs to Hyperon AGI)"

• "Myers-Briggs Typology (INFP) as a cognitive model lens"

Additional videos

“Bringing harmony to AGI: symbolic logic meets sound-structured novelty.”