Francisco Robles

Hola: Me refería a un prototipo que estoy desarrollando llamado OpenSymbolic. Es un sistema experimental que representa información mediante formas, colores y tonos, lo que yo llamo conceptrones. Cada conceptrón puede codificar datos o significados de manera visual y acústica, así que sí, pueden parecer pequeños símbolos animados que emiten sonidos. Pensé que podría ser interesante para personas que trabajan en bioinformática e interpretación de datos multimodales, porque permite visualizar conjuntos de datos complejos de forma simbólica.

Thank you very much for your interest. I truly appreciate your openness. I’m working on OpenSymbolic, a new symbolic communication system that connects human expression, AI and neuroscience. I believe it could complement your field of work in bioinformatics and multimodal data interpretation. Would you be open to a short conversation to share details and possible research directions? Best regards, Francisco

Yes — the discussion point is whether symbolic encoding (through conceptrons) can serve as a deterministic multimodal representation system. I’m proposing a reproducible experiment: the same structured input (JSON or sensory data) always produces the same Σ (symbol chain) and identical SHA-256 hash. This could form a universal layer for communication between human and machine inputs. I’d like to discuss its theoretical validity and possible applications.

OpenSymbolic — Verifiable Summary OpenSymbolic is a symbolic encoding system that transforms sensory and structured inputs (color, shape, tone/sound, or JSON data) into minimal, verifiable units called conceptrons. It is fully reproducible: the same input always generates the same Σ (Sigma chain) and the same SHA-256 hash. What can be verified now (in 5 minutes) 1. Available artifacts: voice-to-conceptron (Talkboard), retina+voice (Talkboard Retina), NGS→conceptrons (JSON→Σ), and the Universal Translator v0.1 (ES→Σ→EN via speech synthesis). 2. Determinism: load the same JSON example, apply rules, export the Σ output, compute SHA-256, reimport it—hash must remain identical. 3. Sensory determinism: record short audio input and capture camera color. The system maps audio energy→frequency (200–800 Hz) and color→C; capture produces a conceptron {C,F,T,M}. 4. Integrity: every Σ can be exported as JSON and wrapped inside an OSNet envelope (timestamp + payload) for traceability. What has been practically demonstrated • Deterministic behavior: same input → same Σ → same SHA-256. • Multimodal encoding: voice + color + shape → unique conceptron. • Interoperability: Σ exported/imported among prototypes (Talkboard ↔ NGS ↔ Reader). • Clinical proof of concept: an early-stage communication tool for children showed real interest at a therapeutic center. How to reproduce and validate 1. Run any demo locally or under HTTPS. 2. Load the built-in example → click “Apply rules → Σ”. 3. Export JSON output, compute SHA-256, record hash. 4. Re-import JSON, confirm identical hash. 5. Capture screen or short video of the process. 6. For sensor demo: record short audio and capture color, create conceptron, replay tones, record resulting sound. Simple experiments for scientific validation • Technical reproducibility: give any peer the same JSON and expect same hash. • Perceptual consistency: with 10 audio+color samples from one user, check if experts assign the same meaning to Σ outputs (confusion-matrix test). • Noise tolerance: inject noise in audio and observe frequency deviation; chart RMS error. Limitations (for honesty) • The system does not "understand" meaning—it encodes multimodal input deterministically. • Accuracy depends on calibration of mic/camera and rule definitions. • Ethical approval and GDPR compliance required for clinical data use. Proposed minimal validation protocol 1. 2-minute video showing JSON → Σ → same SHA-256; mic+cam → conceptron → tone replay. 2. Provide replication instructions to other users (6 steps above). 3. Optional small-scale pilot with 5 users: measure recognition accuracy and usability. 4.Integrity: every Σ can be exported as JSON and wrapped inside an OSNet envelope (timestamp + payload) for traceability. What has been practically demonstrated • Deterministic behavior: same input → same Σ → same SHA-256. • Multimodal encoding: voice + color + shape → unique conceptron. • Interoperability: Σ exported/imported among prototypes (Talkboard ↔ NGS ↔ Reader). • Clinical proof of concept: an early-stage communication tool for children showed real interest at a therapeutic center. How to reproduce and validate 1. Run any demo locally or under HTTPS. 2. Load the built-in example → click “Apply rules → Σ”. 3. Export JSON output, compute SHA-256, record hash. 4. Re-import JSON, confirm identical hash. 5. Capture screen or short video of the process. 6. For sensor demo: record short audio and capture color, create conceptron, replay tones, record resulting sound. simple experiments for scientific validation • Technical reproducibility: give any peer the same JSON and expect same hash. • Perceptual consistency: with 10 audio+color samples from one user, check if experts assign the same meaning to Σ outputs (confusion-matrix test). • Noise tolerance: inject noise in audio and observe frequency deviation; chart RMS error. Limitations (for honesty) • The system does not "understand" meaning—it encodes multimodal input deterministically. • Accuracy depends on calibration of mic/camera and rule definitions. • Ethical approval and GDPR compliance required for clinical data use. Proposed minimal validation protocol 1. 2-minute video showing JSON → Σ → same SHA-256; mic+cam → conceptron → tone replay. 2. Provide replication instructions to other users (6 steps above). 3. Optional small-scale pilot with 5 users: measure recognition accuracy and usability. This is not theoretical; it’s an executable, auditable framework. Each conceptron and Σ chain is deterministic and cryptographically traceable. I’m open to collaboration or independent replication to verify these claims under scientific observation.

Hello everyone, I’m currently researching a new model I call OpenSymbolic, based on symbolic communication units I’ve named conceptrons. The idea is to encode meaning using color, shape, and tone — forming structured “symbolic chains” similar to words or data packets. I’d like to discuss its possible applications in communication, assistive technologies, and information systems. Would it be appropriate to share a demo or a short paper for peer feedback here?