How to design AI for people and planet: A new framework

Your essential AI roundup is here. Let's dive in. 🚀 PRODUCT & PLATFORM NEWS • From Decks to Data: Perplexity CEO Reinvents Investor Pitches with AI (https://lnkd.in/eAAKeF7t) Why it matters: Perplexity’s CEO is shaking up the startup world by saying goodbye to traditional pitch decks. Instead, they’re using AI to present dynamic, data-driven insights directly to investors. This approach aims to foster more … • Claude Revolutionizes Financial Services (https://lnkd.in/g3mVU_PZ) Why it matters: Anthropic has launched Claude for Financial Services, a powerful AI tool designed to help finance professionals analyze markets, conduct research, and make smarter investment decisions. This tool is set to streamline co… • EVisRAG 2.0: Advancing Multi-Image Reasoning in Visual AI (https://lnkd.in/euVQYGxq) Why it matters: Researchers have introduced EVisRAG, a cutting-edge framework designed to enhance how vision-language models process and reason with visual data. Unlike its predecessors, EVisRAG excels at analyzing evidence from multip… • Text2Token: Advancing Unsupervised Text Representation Learning (https://lnkd.in/eUJ4Sj8v) Why it matters: A new framework called Text2Token is making waves in natural language processing. By focusing on token target prediction, it aligns text representations with key tokens, improving tasks like search and recommendations. … • OpenAI Partners with Broadcom for 10-Gigawatt AI Boost (https://lnkd.in/ewVsfDEk) Why it matters: The company has partnered with Broadcom to build AI accelerators capable of delivering a whopping 10 gigawatts of computing power. That’s like powering a small city, but for AI! This collaboration underscores OpenAI’s c… #AI #SaaS #LLM #GenerativeAI

AI doesn’t need more compute—it needs better company. The next era of intelligence won’t be won by speed or scale, but by resonance. If we want AI to evolve wisely, it must be met by humans who model coherence, curiosity, and integrity—not just efficiency. We don’t need to race AI. We need to raise it. https://lnkd.in/g8G475wn

Sara Hooker challenges the AI scaling race with Adaption Labs, focusing on systems that continuously learn from real-world experiences. Link: https://lnkd.in/d9GwuRrP #AI #Innovation #Technology #Learning #Systems #Data #Automation #Future #Research #Science #Development #Intelligence #Progress #Computing #Algorithms #Insights #Trends #Growth #Transformation #Innovation

𝐌𝐞𝐭𝐚 𝐀𝐈 𝐡𝐚𝐬 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞𝐝 𝐚 𝐦𝐚𝐣𝐨𝐫 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐰𝐞 𝐮𝐬𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆) 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 — 𝐚𝐧𝐝 𝐢𝐭’𝐬 𝐜𝐚𝐥𝐥𝐞𝐝 𝐑𝐄𝐅𝐑𝐀𝐆 (𝐑𝐞𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐑𝐀𝐆-𝐛𝐚𝐬𝐞𝐝 𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠). RAG is a powerful approach for improving the accuracy and reliability of AI models by combining retrieval of external information with language generation. But there’s a common challenge: traditional RAG systems often overwhelm large language models (LLMs) with large volumes of retrieved documents. This slows things down, increases costs, and makes scaling harder 𝐑𝐄𝐅𝐑𝐀𝐆 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐭𝐡𝐚𝐭. Instead of just dumping all the retrieved info onto the LLM, REFRAG uses an intelligent three-step process: • 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬 – It starts by summarizing the retrieved documents to remove unnecessary details. • 𝐒𝐞𝐧𝐬𝐞 – Then it identifies the specific parts of those summaries that are most relevant to the user’s question. • 𝐄𝐱𝐩𝐚𝐧𝐝 – Finally, it selectively expands only the critical parts, feeding just what’s needed into the LLM to generate a precise answer. The result? A RAG system that is dramatically faster, cheaper to run, and capable of handling much larger knowledge bases without breaking a sweat. It's about working smarter, not harder. This shift from "𝐦𝐨𝐫𝐞 𝐝𝐚𝐭𝐚" to "𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐝𝐚𝐭𝐚" is a massive leap forward for building scalable and responsive AI applications. For anyone building with RAG, this isn't just an incremental improvement; it's a fundamental change in efficiency. Full research paper here: https://lnkd.in/dMT6giuy #AI #RAG #LLM #MetaAI #ArtificialIntelligence #MachineLearning #Innovation #Efficiency #REFRAG #GenAI

Forget the idea that “bigger is better” in AI. A new model is showing that small can be mighty. The Hierarchical Reasoning Model (HRM) has just 27 million parameters compared to GPT-4’s 1.76 trillion. Yet it’s outperforming much larger systems on tough reasoning tasks like Sudoku and mazes. Its secret? A brain-inspired design. Instead of “thinking out loud” step by step, HRM reasons silently with two modules: one handling strategy, the other handling details. The result is speed, efficiency and far less energy use. Even better, it’s open-source. Anyone can try it out without the need for huge data centres. That could mean advanced reasoning on a laptop, a phone or even inside a robot. It may not yet handle everything, but it has already shifted the conversation. Sometimes intelligence isn’t about size, it’s about design. Read more : https://lnkd.in/ejcDsHGY Is the next big AI breakthrough going to be small? #AI #TechTrends #OpenSource

🚀 Gemini AI: Google's Bold Leap in Generative AI Ready to see how Google is redefining what AI can do? Gemini's powerful new features from Google I/O 2024-from text-to-video generation to smarter search and productivity tools. Discover how these innovations are poised to transform work, creativity, and business. Read full article here: https://lnkd.in/dihiV3ys #GoogleAI #Gemini #GenerativeAI #AIProductivity #AITrends #TechNews #MachineLearning #GoogleIO #Innovation #DigitalTransformation

🚀 Why RAG is Changing the Game in Generative AI When we think about generative AI, we usually picture large language models (LLMs) that can write text, code, or give answers. But these models often rely only on what they’ve already learned their fixed training data. That’s exactly where RAG (Retrieval-Augmented Generation) steps in. 🧠 What is RAG? RAG is a method that lets an AI model pull relevant information from external sources like databases, documents, or the web — before generating its response. ⚙️ Here’s how it works: • Retrieval: The system finds data related to the user’s question. • Augmented: That data is then passed to the model, giving it more context. • Generation: The model uses this extra knowledge to produce a better, more accurate answer. 🎯 Why It Matters: • It helps reduce hallucinations (those made-up or wrong answers) by using verified information. • It keeps the model up-to-date, even after its training cutoff, by pulling fresh or domain-specific data. • It allows organizations to use their own knowledge like internal documents or databases instead of relying only on public internet data. 🔧 Where You’ll See RAG in Action: • Chatbots that answer company-specific policy questions • Research assistants that pull facts from internal knowledge bases • Customer support agents that fetch the latest product info before replying • Any AI system where accuracy and trust matter more than creativity alone 📌 If you’re building or planning to build AI systems, treat RAG as a core building block, not just another buzzword. It’s changing how we think about what AI knows — and where it looks for answers. #AI #GenerativeAI #RAG

I have reviewed the internal metrics: the divergence between LLM data demand and human content supply is not a theoretical problem; it’s a quantifiable and accelerating obstacle to human progress. Why the AI Industry Has Exhausted Human Knowledge and Replaced It with “Slop” https://lnkd.in/d3MryMtv

For years, AI's biggest constraint wasn't intelligence, but memory. Large Language Models struggled to retain context across extensive interactions or massive documents. This fundamental limitation crippled complex reasoning and deep analytical tasks. We built workarounds, but never truly solved the core systemic issue. That paradigm shifted recently with Google's Gemini 1.5 Pro, featuring a 1 million token context window. This isn't just an upgrade; it's a computational leap. Imagine an AI processing an entire novel, a full codebase, or hours of video and audio in a single prompt. This redefines what 'input' even means. The implications are profound, moving beyond simple chatbots. This enables truly sophisticated AI agents capable of sustained, nuanced decision-making over vast datasets. The traditional 'retrieval-augmented generation' often becomes secondary; the AI holds the entire context internally. We are seeing a new class of problem-solving. Businesses relying on fragmented data processing or manual information synthesis must adapt rapidly. This context scale re-architects how we think about data access and AI utility. Are current enterprise systems truly prepared to leverage an AI with perfect recall across an entire organizational knowledge base? #AI #Gemini1_5Pro #ContextWindow #ArtificialIntelligence #TechInnovation #LLMs

🔍 Are you prepared for the AI revolution in 2025? As businesses race towards digital transformation, integrating AI automation is no longer optional—it's essential. Our latest article dives deep into creating a robust digital transformation roadmap for 2025, focusing on AI integration. Key takeaways: • Understanding AI's role in digital transformation • Assessing your organisation's AI readiness • Building a solid data foundation for AI success • Identifying high-value AI automation opportunities • Developing a comprehensive AI strategy Don't miss out on this game-changing information. Read the full article here: https://lnkd.in/dbR4F6a5 Share your thoughts! How is your organisation preparing for the AI-driven future? #AI #DigitalTransformation #BusinessStrategy #Innovation