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At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower […]
NVIDIA and Google infrastructure cuts AI inference costs Read More »
In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework,
Managing the economics of multi-agent AI now dictates the financial viability of modern business automation workflows. Organisations progressing past standard chat interfaces into multi-agent applications face two primary constraints. The first issue is the thinking tax; complex autonomous agents need to reason at each stage, making the reliance on massive architectures for every subtask too
How multi-agent AI economics influence business automation Read More »
Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration
NyRAG: Building Production-Ready RAG Applications with Zero Code Read More »
The partnership announced this week between Microsoft and Hexagon Robotics marks an inflection point in the commercialisation of humanoid, AI-powered robots for industrial environments. The two companies will combine Microsoft’s cloud and AI infrastructure with Hexagon’s expertise in robotics, sensors, and spatial intelligence to advance the deployment of physical AI systems in real-world settings. At
From cloud to factory – humanoid robots coming to workplaces Read More »
Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a small 3B class model that still targets on device and edge deployment. Where LFM2-2.6B-Exp Fits
In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We walk through each component, from the maze environment and reward-model network to preference generation, training loops, and evaluation, while observing how the agent gradually improves
