Artificial Intelligence

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‘Godfather of AI’ to engineers: Computer Science degree will remain valuable for a longtime, just learn t – The Times of India

‘Godfather of AI’ to engineers: Computer Science degree will remain valuable for a longtime, just learn t  The Times of India’Godfather of AI’ says CS degrees ‘will remain valuable for quite a long time’ — and students should still learn to code  Business InsiderShould you still learn to code? Godfather of AI says says don’t give up

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Soft Computing, Volume 29, Issue 23-24, December 2025

1) Filters and ideals in pseudocomplemented posetsAuthor(s): Ivan Chajda, Helmut LängerPages: 5925 – 59322) Natural fuzzy negations and ordinal sums of uninormsAuthor(s): Ivan Mezzomo, Benjamin Bedregal, Matheus da Silva MenezesPages: 5933 – 59533) Unlearning in distributed budget support vector machineAuthor(s): Angel Navia-VázquezPages: 5955 – 59704) Determination of the optimal sizes and locations of hydrogen refueling stations for deploying hydrogen

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Cisco Released Cisco Time Series Model: Their First Open-Weights Foundation Model based on Decoder-only Transformer Architecture

Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face under an Apache 2.0 license, and it targets forecasting workloads without task specific fine tuning. The model extends TimesFM 2.0

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Google Colab Integrates KaggleHub for One Click Access to Kaggle Datasets, Models and Competitions

Google is closing an old gap between Kaggle and Colab. Colab now has a built in Data Explorer that lets you search Kaggle datasets, models and competitions directly inside a notebook, then pull them in through KaggleHub without leaving the editor. What Colab Data Explorer actually ships? Kaggle announced the feature recently where they describe

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A Coding Implementation of a Complete Hierarchical Bayesian Regression Workflow in NumPyro Using JAX-Powered Inference and Posterior Predictive Analysis

In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a probabilistic model that captures both global patterns and group-level variations. Through each snippet, we set up inference using NUTS, analyze posterior distributions, and perform posterior

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