Ancient Philosophical Frameworks for AI
My research draws inspiration from classical philosophical traditions to design more robust reasoning architectures for AI systems, connecting millennia of human wisdom with cutting-edge artificial intelligence.
Philosophical Traditions Informing My Research
Nyāya Epistemology
A school of Indian logic that developed sophisticated frameworks for valid knowledge acquisition, reasoning patterns, and error detection that can inform AI verification systems.
Pratyabhijñā Recognition
Kashmir Shaivism's approach to self-recognition and reflexive awareness, providing models for metacognitive capabilities in advanced AI architectures.
Aristotelian Logic
Classical Western frameworks for syllogistic reasoning and categorization that have influenced formal logical approaches in AI system design.
Buddhist Madhyamaka
Nāgārjuna's emptiness doctrine and logical examination of inherent existence offers strategies for handling uncertainty and contextuality in AI reasoning.
My Writings on Philosophical Frameworks
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Applications in AI Architecture
Epistemological Validation
Implementing Nyāya's pramāṇa system to create specialized validation mechanisms for different types of knowledge in AI systems, improving reliability and reducing hallucinations.
Metacognitive Monitoring
Developing neural circuits inspired by Pratyabhijñā's reflexive awareness concepts to create self-monitoring capabilities that improve reasoning transparency.
Error Classification
Applying Nyāya's khyātivāda (theory of error) to develop more sophisticated error detection and correction mechanisms in reasoning pathways.
Contextual Reasoning
Using Buddhist dialectical approaches to enhance contextual understanding in LLMs, improving performance on tasks requiring nuanced interpretation.