I think I made some progress on the pure symbolic AI approach!
A new C++ framework mimics transformer architectures and backpropagation using pure symbolic logic, not neural networks.
A new open-source project called InfoCell is challenging the dominance of neural networks by proposing a fresh take on symbolic AI. Created by researcher Péter Hun-Nemeth, the framework is built on the premise that current programming languages excel at giving commands (the 'imperative mood') but lack native ways to express observations (the 'realis mood'). InfoCell introduces a novel pattern-matching algorithm designed to bridge this grammatical gap, enabling the system to synthesize regular programs directly from descriptive statements. Intriguingly, the architecture of this process is noted to bear a structural resemblance to the transformer models that power modern LLMs like GPT-4.
The project is more than a theoretical paper; it's a fully functional proof-of-concept implemented in debuggable C++ and released under an MIT license on GitHub. The framework demonstrates that core algorithms typically exclusive to neural networks—including backpropagation for learning and even image recognition pipelines—can be reconstructed using this symbolic, pattern-based approach. This offers a transparent and interpretable alternative to the often opaque 'black box' nature of deep learning models. While the accompanying documentation is still a work in progress, the available code provides a concrete foundation for researchers and developers to explore a potentially more controllable and explainable path to artificial intelligence.
- The InfoCell framework uses a novel pattern-matching algorithm to connect imperative commands with observational statements, enabling program synthesis.
- It can replicate neural network components like transformers and backpropagation using pure symbolic logic, offering a debuggable C++ implementation.
- The project is open-source (MIT license) on GitHub, presenting a transparent alternative to current black-box LLM architectures.
Why It Matters
It offers a debuggable, transparent path to AI capabilities, potentially addressing the 'black box' problem of modern neural networks.