Matt Zimak
Project OwnerAs Project Owner, Matt leads overall project direction, strategy alignment, stakeholder management, and integration of technical deliverables to ensure successful execution and outcome.
Symbolica introduces an innovative neural-symbolic architecture, seamlessly integrating differentiable symbolic logic constraints within advanced neural frameworks (Transformers and VAEs). Our unique approach uses improved PyNeuraLogic techniques, allowing neural models to intrinsically learn from symbolic rules. We initially validate Symbolica using music as a practical demo due to music’s dual symbolic and continuous nature, clearly demonstrating the system’s general-purpose strength and applicability across broader domains.
This RFP invites proposals to explore and demonstrate the use of neural-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs. Bids are expected to range from $40,000 - $100,000.
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Conduct comprehensive research to detail innovative neural-symbolic architecture and differentiable symbolic logic integration. Clearly outline methodologies, tasks, timelines, and framework design, establishing the foundational plan for subsequent development.
Detailed research plan, differentiable logic integration plan, agile breakdown of tasks with timelines, architecture design documentation, and clearly defined evaluation criteria.
$20,000 USD
Develop an initial version of Symbolica architecture, explicitly demonstrating integration of differentiable symbolic constraints into neural frameworks. Validate the conceptual underpinnings through initial practical implementations using sound character analysis, MIDI-based chord recognition, and melody reasoning as internal benchmarks.
Draft implementations of neural-symbolic sound character analyzer, MIDI-based chord recognition module, melody reasoning analysis module, preliminary benchmark results (≥25% rule violation reduction), initial testing results, and internal technical documentation.
$40,000 USD
Complete the optimized Symbolica architecture, demonstrating robust integration of differentiable symbolic logic. Conduct comprehensive validation within the music domain, clearly showcasing significant performance improvements and interpretability enhancements.
Fully functional Symbolica architecture with finalized neural-symbolic sound analyzer, MIDI-based chord recognition solution, melody reasoning module, comprehensive final report (≥50% rule violation reduction, coherence improvement), internally accessible documented codebase, detailed technical documentation, and practical demonstration of Symbolica’s capabilities.
$40,000 USD
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