Enterprise Digital Twin implementation: build around one real decision
An implementation guide for decision context: source ownership, temporal records, retrieval, permissions, MCP boundaries, failure tests and a staged rollout around one workflow.
From the archive
Ajith Prabhakar
Articles
Decision-centric AI: improve the path from evidence to action
Design AI around an owned decision and a confirmed outcome. Use a practical decision canvas, workflow example and measured expansion gates.
Neuro-Symbolic AI for Multimodal Reasoning: Foundations, Advances, and Emerging Applications
Neuro-symbolic AI connects learned representations with explicit structures such as rules, graphs, or planners, but the term covers multiple designs. This guide compares representative approaches and applications while examining what…
Red-teaming AI systems: a practical playbook for testing real actions
Scope an AI red-team exercise, test prompts and tool permissions, inspect destination outcomes, protect evidence, and verify fixes without mistaking a refusal for a security boundary.
Persistent AI memory: what to keep, trust, and forget
Separate context, retrieval, and model adaptation, then test persistent AI memory for provenance, corrections, permissions, deletion, and useful continuity.
Small language models: how to choose a model that fits the job
Evaluate a small language model against the task, deployment constraints and cost of an accepted result, with current examples and a practical selection process.
Liquid Neural Networks & Edge‑Optimized Foundation Models: Sustainable On-Device AI for the Future
Liquid Neural Networks (LNNs) are transforming the landscape of edge AI, offering lightweight, adaptive alternatives to traditional deep learning models. Inspired by biological neural dynamics, LNNs operate with continuous-time updates,…
Chain-of-Tools: Scalable Tool Learning with Frozen Language Models
Tool Learning with Frozen Language Models is rapidly emerging as a scalable strategy to empower LLMs with real-world functionality. This article introduces Chain-of-Tools (CoTools), a novel approach that enables frozen…
How SEARCH-R1 is Redefining LLM Reasoning with Autonomous Search and Reinforcement Learning
SEARCH-R1 is a groundbreaking reinforcement learning framework for search-augmented LLMs, enabling AI to think, search, and reason autonomously. Unlike traditional models constrained by static training data, SEARCH-R1 dynamically retrieves, verifies,…
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