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Research area

AI, Causality & Physical Computation

Research on contextual inference, causal reasoning, representational ethics and memory-rich computational architectures grounded in dynamical systems.

Scope

What this theme investigates

We investigate computation as a context-sensitive physical and inferential process. Current work spans probabilistic structure in language models, dynamical reservoirs with multiple timescales, causal explanation and the relation between machine representations and experienced worlds. Our work on representational ethics draws conceptual motivation from Sāṃkhya’s layered account of cognition, mediation and experience.

The programme keeps intelligence, representation and consciousness conceptually distinct. This distinction supports both better experiments and more responsible governance, especially when AI systems generate persuasive representations without evidence of subjective experience.

Current directions

01

Contextual inference in language models

Experimental tests of probabilistic structure under controlled contextual modulation.

02

Causality and pramāṇa

Dialogue between hierarchical causal models, classical epistemology and explainable AI.

03

Representational ethics

A Sāṃkhya-motivated account of nested perceptual and artificial worlds, semantic coherence, perceptual sovereignty and governance beyond alignment.

04

Multi-timescale reservoirs

Separable rotation and dissipation for memory-rich dynamical computation.

Research record

Publications in this area

Peer-reviewed work, chapters and current manuscripts connected with this research theme.

Complete publication record →