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I’m on the Meta Oversight Board. We need AI protections now | Suzanne Nossel

ai, AI (Artificial Intelligence), Artificial Intelligence, Meta, Social media, Technology

AI is transforming our world. Accepting independent oversight is the least companies can do to protect our rightsThe speed with which AI is transforming our lives is head-spinning. Unlike previous technological revolutions – radio, nuclear fission or the internet – governments are not leading the way. We know that AI can be dangerous; chatbots advise […]

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Hundreds of UK teenagers to pilot social media bans and restrictions

ai, AI (Artificial Intelligence), Artificial Intelligence, Children, Digital media, Instagram, Internet, Labour, Media, Mental health, Meta, Politics, Social media, Social media ban, Social media bans, Society, Technology, TikTok, UK news, X, Young people

Trials to form part of three-month consultation on Keir Starmer’s plans to tackle negative effects of smartphone useHundreds of teenagers will be enlisted to trial social media bans in the coming months with overnight digital curfews and daily screen time limits also tested as part of Keir Starmer’s plan to crack down on the negative

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FireRedTeam Releases FireRed-OCR-2B Utilizing GRPO to Solve Structural Hallucinations in Tables and LaTeX for Software Developers

ai, AI (Artificial Intelligence), AI Shorts, Artificial Intelligence, Editors Pick, Language Model, New Releases, OCR, Staff, Tech News, Technology

Document digitization has long been a multi-stage problem: first detect the layout, then extract the text, and finally try to reconstruct the structure. For Large Vision-Language Models (LVLMs), this often leads to ‘structural hallucinations’—disordered rows, invented formulas, or unclosed syntax. The FireRedTeam has released FireRed-OCR-2B, a flagship model designed to treat document parsing as a

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How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, explainable AI, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract

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Google AI Introduces STATIC: A Sparse Matrix Framework Delivering 948x Faster Constrained Decoding for LLM Based Generative Retrieval

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Large Language Model, Staff, Tech News, Technology

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items as Semantic IDs (SIDs)—discrete token sequences—and treat retrieval as an autoregressive decoding task. However, industrial applications often require strict adherence to business logic, such as enforcing content freshness or

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How to Design a Production-Grade Multi-Agent Communication System Using LangGraph Structured Message Bus, ACP Logging, and Persistent Shared State Architecture

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, Artificial Intelligence, Editors Pick, Technology, Tutorials

In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style message schema that allows agents to communicate via a shared state rather than calling each other directly, enabling modularity, traceability, and production-grade orchestration. We implement three specialized agents, a

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Datacentre developers face calls to disclose effect on UK’s net emissions

ai, AI (Artificial Intelligence), Artificial Intelligence, Computing, Energy, Environment, Green politics, Greenhouse gas emissions, Labour, Liz Kendall, Politics, Technology, UK news

Campaign groups write to technology secretary amid concerns that sites could double overall electricity demandDatacentre developers are facing pressure to reveal whether their projects will increase the UK’s net greenhouse gas emissions, amid concerns the sites could double national electricity demand.Campaign groups have written to the UK technology secretary, Liz Kendall, warning that the energy

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Readers reply: what would happen to the world if computer said yes?

ai, AI (Artificial Intelligence), Artificial Intelligence, ChatGPT, Computing, Life and style, Technology

The long-running series in which readers answer other readers’ questions asks whether we could cope with a world where computer gave up saying no …This week’s question: what if Shakespeare were dropped in modern-day London?After years of computer saying no, and giving us all migraines and premature grey hair, I’m starting to worry that computer

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Alibaba Team Open-Sources CoPaw: A High-Performance Personal Agent Workstation for Developers to Scale Multi-Channel AI Workflows and Memory

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, New Releases, Open Source, Python, Staff, Tech News, Technology

As the industry moves from simple Large Language Model (LLM) inference toward autonomous agentic systems, the challenge for devs have shifted. It is no longer just about the model; it is about the environment in which that model operates. A team of researchers from Alibaba released CoPaw, an open-source framework designed to address this by

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A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, Machine Learning, MLOps, Staff, Tech News, Technology, Tutorials

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and artifact store, enabling us to track experiments in a scalable, reproducible manner. We then train multiple machine learning models using a nested hyperparameter sweep while automatically

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