digitado

Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and competing objectives. However, its deployment in dynamic environments is hindered by the need for task-specific retraining and substantial computational cost. We introduce MIRACL (Meta multI-objective Reinforcement leArning with Composite Learning), a hierarchical Meta-MORL framework that allows for a few-shot generalisation across diverse tasks. MIRACL decomposes each task into structured subproblems for efficient policy adaptation and meta-learns a global policy […]

Ver mais

How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 1T total parameters and 68.8B activated parameters. The model architecture is designed to optimize performance in enterprise-specific tasks while maintaining competitive general-purpose capabilities. Unlike traditional dense models, Yuan3.0 Ultra utilizes sparsity to scale capacity without a linear increase […]

Ver mais

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and machine learning. By the end, we are not just manipulating data, we are building a complete model-ready pipeline powered by Daft’s scalable execution […]

Ver mais

In cybersecurity applications where replicability is critical, or when building pseudo-random number generators, it is typical to perform a large number of various tests to check if a sequence of bits is random enough for practical purposes. This is also true in scientific research, to assess whether or not the digits of π or other constants mimic randomness well enough, as discussed in my previous article, here. Technically, one could use the Kolmogorov-Smirnov (KS) distance between two joint […]

Ver mais

I am putting all the new code and documentation about xLLM v 2.0 on GitHub, starting with various AI agents. At least, what is open-source and public (there is a lot more behind the public material). All home-made from scratch with radically different technology. You can check the new repository, here. Start with the README file at the top of the repository, here. No Blackbox, no external API calls, and minimum reliance on Python libraries such as TensorFlow, […]

Ver mais

Here are Google’s latest AI updates from February 2026

Ver mais

Learn more about AI Mode in Search’s query fan-out method for visual search.

Ver mais