The Machine Learning Practitioner’s Guide to Fine-Tuning Language Models – MachineLearningMastery.com

The Machine Learning Practitioner’s Guide to Fine-Tuning Language Models – MachineLearningMastery.com

In this article, you will learn when fine-tuning large language models is warranted, which 2025-ready methods and tools to choose, and how to avoid the most common mistakes that derail projects. Topics we will cover include: A practical decision framework: prompt engineering, retrieval-augmented generation (RAG), and when fine-tuning truly adds value. Today’s essential methods—LoRA/QLoRA, Spectrum—and…

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Topp 10 AI-verktyg för sömn och meditation – AI nyheter

Topp 10 AI-verktyg för sömn och meditation – AI nyheter

I dagens stressiga värld blir AI-drivna hälsoverktyg allt viktigare för vårt välbefinnande. Istället för att bara gissa oss till vad som fungerar, kan vi nu använda smarta algoritmer som lär sig våra mönster och ger personliga rekommendationer för bättre sömn, mindre stress och djupare meditation. Sömnoptimering – Meditation och stresshantering Modern AI-teknik har revolutionerat hur…

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Can AI suffer? – AI Blog

Can AI suffer? – AI Blog

TL;DR AI systems today cannot suffer because they lack consciousness and subjective experience, but understanding structural tensions in models and the unresolved science of consciousness points to the moral complexity of potential future machine sentience and underscores the need for balanced, precautionary ethics as AI advances. As artificial intelligence systems become more sophisticated, questions that…

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Creating AI that matters

Creating AI that matters

When it comes to artificial intelligence, MIT and IBM were there at the beginning: laying foundational work and creating some of the first programs — AI predecessors — and theorizing how machine “intelligence” might come to be. Today, collaborations like the MIT-IBM Watson AI Lab, which launched eight years ago, are continuing to deliver expertise…

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How to Design a Fully Functional Enterprise AI Assistant with Retrieval Augmentation and Policy Guardrails Using Open Source AI Models

How to Design a Fully Functional Enterprise AI Assistant with Retrieval Augmentation and Policy Guardrails Using Open Source AI Models

In this tutorial, we explore how we can build a compact yet powerful Enterprise AI assistant that runs effortlessly on Colab. We start by integrating retrieval-augmented generation (RAG) using FAISS for document retrieval and FLAN-T5 for text generation, both fully open-source and free. As we progress, we embed enterprise policies such as data redaction, access…

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What Hotels Can, and Need to Do to Gain an Advantage or Stay Ahead Using AI in 2025/2026 – AI Blog

What Hotels Can, and Need to Do to Gain an Advantage or Stay Ahead Using AI in 2025/2026 – AI Blog

Data-Driven Marketing and Revenue Management with AI In the quest to stay ahead, maximizing revenue and effectively targeting high-value guests are crucial, and AI has rapidly become the secret weapon for forward-thinking hotel marketers and revenue managers. The days of static prices and broad-brush marketing are over. Today, AI algorithms can analyze vast datasets in…

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An Implementation to Build Dynamic AI Systems with the Model Context Protocol (MCP) for Real-Time Resource and Tool Integration

An Implementation to Build Dynamic AI Systems with the Model Context Protocol (MCP) for Real-Time Resource and Tool Integration

In this tutorial, we explore the Advanced Model Context Protocol (MCP) and demonstrate how to use it to address one of the most unique challenges in modern AI systems: enabling real-time interaction between AI models and external data or tools. Traditional models operate in isolation, limited to their training data, but through MCP, we create…

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