Candide de Copilot In a century where dashboards replace stained glass and algorithms preach sermons, Candide wanders through clouds, Silicon Valley, Davos, and quantum labs, asking naïve yet devastating questions: If machines think for us, what remains for humans? Can optimization cheat death? Can AGI write a constitution for intentions it does not have? This tale, sharpened with irony and polished with paradox, exposes our cult of metrics, our dream of perfect convergence, and our terror of imperfection. […]

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DeepSeek, a Chinese AI startup, is rapidly gaining prominence by developing and openly releasing highly competitive, cost-effective AI models. Its innovative techniques, including reinforcement learning and a mixture-of-experts architecture, allow for superior performance while minimizing training costs. This has disrupted the AI market, sparking price wars and forcing established companies like OpenAI and Google to adapt. While facing challenges such as access to advanced computing resources and potential censorship concerns, DeepSeek’s open-source approach and focus on efficiency are […]

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Wireless Sensor Networks (WSNs) have diverse applications in urban, industrial and environmental monitoring. However, the design complexity of this type of network is high, due to conflicting objectives such as latency, energy consumption, connectivity and coverage. This article addresses the need for structured and reproducible approaches to developing WSNs. We propose a modular and scalable system designed to integrate simulators and evolutionary algorithms for multi-objective optimization in WSNs. We present a formalized process and supporting architecture that combines […]

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Mutuum Finance (MUTM), a new crypto project focused on decentralized lending and borrowing, has confirmed the completion of its V1 smart contract audit conducted by Halborn Security. The update marks an important step in the project’s roadmap as it prepares for the initial deployment of its protocol. With core contracts reviewed and development milestones progressing, Mutuum Finance continues to move from design into execution within the DeFi crypto sector. Overview of The Mutuum Finance (MUTM) Mutuum Finance is […]

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arXiv:2601.02532v1 Announce Type: new Abstract: We study the parameterized complexity of the Cograph Deletion problem, which asks whether one can delete at most $k$ edges from a graph to make it $P_4$-free. This is a well-known graph modification problem with applications in computation biology and social network analysis. All current parameterized algorithms use a similar strategy, which is to find a $P_4$ and explore the local structure around it to perform an efficient recursive branching. The best known […]

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The convergence characteristics of an online gradient learning technique for training Pi–Sigma higher-order neural networks under a smoothed L1 regularization framework with adaptive momentum are examined in this research. Although Pi-Sigma networks can effectively represent high-order nonlinear interactions, nonconvexity, parameter coupling, and noise sensitivity in online contexts make training them difficult. In order to overcome these problems, we avoid the nondifferentiability of the traditional L1 norm while promoting sparsity by using a differentiable approximation of the L1 penalty. […]

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We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiasedness of the DP-GLM regression mean function estimate; we then give a practical example for when those conditions hold. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression including regression trees and […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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