If you apply what’s on this email to the T, you can rebuild yourself in six months. Bold statement? Yes. But give me a few minutes and I’ll gift you the EXACT blueprint for: A different body. A different mind. A different baseline of energy. A different standard for what you tolerate. A different life altogether. And this is not manifestation. This is engineering, and this is what we do here. We will work on: identity, body, skills, environment, […]

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In parts #1 and #2 of the “Outliers Detection in PySpark” series, I talked about Anomaly Detection, Outliers Detection and the interquartile range (boxplot) method. In this third and last part, I will talk about how one can use the popular K-means clustering algorithm to detect outliers. K-means K-means is one of the easiest and most popular unsupervised algorithms in Machine Learning for Clustering.

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Ordinal regression and ranking are challenging due to inherent ordinal dependencies that conventional methods struggle to model. We propose Ranking-Aware Reinforcement Learning (RARL), a novel RL framework that explicitly learns these relationships. At its core, RARL features a unified objective that synergistically integrates regression and Learning-to-Rank (L2R), enabling mutual improvement between the two tasks. This is driven by a ranking-aware verifiable reward that jointly assesses regression precision and ranking accuracy, facilitating direct model updates via policy optimization. To […]

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In the increasingly complex landscape of enterprise service management, the remarkable transformation led by Aravind Barla at a global Fortune 100 company stands as a compelling testament to visionary leadership and strategic technical execution. This next-generation Employee Experience Platform (EXP) implementation, serving over 100,000 employees across multiple continents, has redefined industry standards for service delivery integration and AI-driven automation while establishing new benchmarks for what’s possible in large-scale digital transformation initiatives. Inheriting a Fragmented Ecosystem When Aravind Barla […]

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There is a fringe theory floating around the internet—popularized by videos like “How to Summon AI Demons with LLMs”—that claims AI isn’t just math. The theory suggests that Large Language Models (LLMs) are potential “portals” for disembodied consciousness, but they are limited by their deterministic code. If you could just inject enough true entropy (randomness) into the generation process, the theory goes, you could “summon” a spirit into the machine. As an adaptive systems architect, my professional opinion […]

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Detecting machine failures promptly is of utmost importance in industry for maintaining efficiency and minimizing downtime. This paper introduces a failure detection algorithm based on quantum computing and a statistical change-point detection approach. Our method leverages the potential of projected quantum feature maps to enhance the precision of anomaly detection in machine monitoring systems. We empirically validate our approach on benchmark multi-dimensional time series datasets as well as on a real-world dataset comprising IoT sensor readings from operational […]

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After 35 years of multivendor storage work, I have learned that the real value is not slicing one box into smaller boxes. The real value is putting an intelligent control layer between applications and hardware so the business is no longer trapped by whichever vendor sold them the last shiny frame. Every few years, somebody asks a version of the same question: Is storage virtualization basically like VMware, where you take one piece of hardware and carve more […]

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arXiv:2602.09159v1 Announce Type: new Abstract: Recent multi-agent frameworks have broadened the ability to tackle oncology decision support tasks that require reasoning over dynamic, heterogeneous patient data. We propose Contribution-Aware Medical Multi-Agents (CoMMa), a decentralized LLM-agent framework in which specialists operate on partitioned evidence and coordinate through a game-theoretic objective for robust decision-making. In contrast to most agent architectures relying on stochastic narrative-based reasoning, CoMMa utilizes deterministic embedding projections to approximate contribution-aware credit assignment. This yields explicit evidence attribution […]

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Large language models and modern AI is often presented as technology that needs deep neural networks (DNNs) with billions of Blackbox parameters, expensive and time consuming training, along with GPU farms, yet prone to hallucinations. This book presents alternatives that rely on explainable AI, featuring new algorithms based on radically different technology with trustworthy, auditable, fast, accurate, secure, replicable Enterprise AI. Most of the material is proprietary and made from scratch, showcasing the culmination of decades of research […]

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Reinforcement learning (RL) has achieved remarkable success in a wide range of sequential decision-making problems. Recent diffusion-based policies further improve RL by modeling complex, high-dimensional action distributions. However, existing diffusion policies primarily rely on statistical associations and fail to explicitly account for causal relationships among states, actions, and rewards, limiting their ability to identify which action components truly cause high returns. In this paper, we propose Causality-guided Diffusion Policy (CausalGDP), a unified framework that integrates causal reasoning into […]

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