technocracy

In conversations about customer data, one question comes up again and again: if both CDPs and MDMs help create a more complete view of the customer, are they basically doing the same thing? It is an understandable question. After all, both technologies are often positioned around customer unification, identity resolution, and creating better visibility across systems. On the surface, they can sound very similar. But while CDPs and MDMs do overlap in some areas, they are not the […]

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arXiv:2509.22124v2 Announce Type: replace Abstract: Regularized linear regression is central to machine learning, yet its high-dimensional behavior with informative priors remains poorly understood. We provide the first exact asymptotic characterization of training and test risks for maximum a posteriori (MAP) regression with Gaussian priors centered at a domain-informed initialization. Our framework unifies ridge regression, least squares, and prior-informed estimators, and — using random matrix theory — yields closed-form risk formulas that expose the bias-variance-prior tradeoff, explain double descent, […]

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arXiv:2603.20220v1 Announce Type: new Abstract: The integration of artificial intelligence (AI) in higher education underscores the growing importance of faculty AI literacy and competency across teaching, research, and service. Existing AI literacy instruments, however, primarily target the general public, students, or K-12 teachers, and therefore lack the role-embedded indicators and psychometric validation needed for scalable assessment among university faculty. Grounded in the Critical Tech-resilient Literacies (CTRL) framework, this study develops and validates the Faculty Artificial Intelligence Literacy and […]

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arXiv:2602.08185v1 Announce Type: new Abstract: Discriminative Random Walks (DRWs) are a simple yet powerful tool for semi-supervised node classification, but their theoretical foundations remain fragmentary. We revisit DRWs through the lens of information geometry, treating the family of class-specific hitting-time laws on an absorbing Markov chain as a statistical manifold. Starting from a log-linear edge-weight model, we derive closed-form expressions for the hitting-time probability mass function, its full moment hierarchy, and the observed Fisher information. The Fisher matrix […]

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Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We address these limitations by reformulating episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solving it with quantum-inspired samplers. Our method, MC+QUBO, integrates a combinatorial filtering step into standard MC policy evaluation: from each batch of trajectories, we select a subset that maximizes cumulative reward while promoting state-space coverage. This selection is encoded […]

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Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others’ behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents’ actions without knowledge of their rewards. The agents independently pursue […]

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Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a complex task requiring cryptographic expertise, and finding the optimal sequence of program transformations is often intractable. In this paper, we propose CHEHAB RL, a novel framework that leverages deep reinforcement learning (RL) to automate FHE code optimization. Instead of relying on predefined heuristics or combinatorial search, our method trains an RL agent to […]

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With explainable AI, intuitive parameters easy to fine-tune, versatile, robust, fast to train, without any library other than Numpy. In short, you have full control over all components, allowing for deep customization, and much fewer parameters than in standard architectures. Introduction I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adaptive loss function introduced here for the […]

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