| # BrahmAI | |
| ## Science-Driven Foundation Models | |
| Building foundation models through rigorous scientific principles and fundamental research. | |
| ## Vision | |
| BrahmAI develops foundation models that prioritize scientific understanding over empirical scaling. Our approach integrates principles from computational neuroscience, physics, mathematics, and cognitive science to create genuinely intelligent systems. | |
| ## Approach | |
| ### Core Principles | |
| - **Scientific Rigor**: Every architectural decision grounded in empirical research | |
| - **Theoretical Foundations**: Built on robust mathematical and computational frameworks | |
| - **Efficiency by Design**: Optimizing for both performance and computational resources | |
| - **Interpretable Intelligence**: Transparent and explainable decision-making processes | |
| ### Research Areas | |
| - Casual reasoning and understanding | |
| - Information-theoretic optimization | |
| - Multi-modal representation learning | |
| - Compositional generalization | |
| - Continual learning systems | |
| ## Models | |
| | Model | Focus Area | Status | | |
| |-------|------------|---------| | |
| | **BrahmAI-Core** | General intelligence | Research | | |
| | **BrahmAI-Sci** | Scientific reasoning | Research | | |
| | **BrahmAI-Code** | Program synthesis | Research | | |
| ## Capabilities | |
| ### Target Domains | |
| - Natural language understanding and generation | |
| - Mathematical reasoning and theorem proving | |
| - Code synthesis and analysis | |
| - Scientific hypothesis generation | |
| - Multi-modal processing | |
| - Complex system modeling | |
| ### Key Differentiators | |
| - First-principles architectural design | |
| - Reduced computational requirements for comparable performance | |
| - Built-in alignment and safety mechanisms | |
| - Cross-domain transfer capabilities | |
| ## Technical | |
| ### Architecture | |
| Novel approaches to: | |
| - Attention mechanisms | |
| - Memory systems | |
| - Representation learning | |
| - Optimization dynamics | |
| ### Infrastructure | |
| - Distributed training framework | |
| - Efficient inference systems | |
| - Comprehensive evaluation suite | |
| ## Resources | |
| - [Research Papers](https://papers.brahmai.ai) | |
| - [Technical Documentation](https://docs.brahmai.ai) | |
| - [GitHub](https://github.com/brahmai) | |
| - [Blog](https://blog.brahmai.ai) | |
| ## Collaboration | |
| We collaborate with leading research institutions and organizations advancing the frontiers of artificial intelligence. | |
| For research partnerships: research@brahmai.ai | |
| For general inquiries: contact@brahmai.ai | |
| ## Team | |
| Interdisciplinary team spanning: | |
| - Machine Learning | |
| - Theoretical Computer Science | |
| - Computational Neuroscience | |
| - Physics & Mathematics | |
| - Systems Engineering | |
| <div align="center"> | |
| [](https://github.com/brahmai) | |
| [](https://papers.brahmai.ai) | |
| [](https://docs.brahmai.ai) | |
| </div> |