David Karam

Connecting LLMs to Multiple Systems 🔗 The real strength of AI isn’t just in generating text - it’s in how well it can work with the systems and data you already have. That’s where the Model Context Protocol (MCP) comes in. MCP allows you to connect an LLM to multiple, diverse systems - ERP, MES, IoT platforms, or custom tools - without rebuilding your entire architecture. This means the LLM can combine data from different sources, add context, and deliver insights that are actually useful. Because MCP is modular, you can integrate new systems or update existing ones without disrupting the whole pipeline. It’s a flexible, scalable approach that makes experimentation and innovation much easier. This is how we move from isolated AI models to connected, context-aware systems. Have you seen LLMs used as an “integrator” for different systems? Which use cases do you find most promising? #AI #MCP #SystemIntegration #LanguageModels #FutureOfAI

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RIaaP-aligned world model evolution in AI: Dec 26, 2025 article discusses “world models” that enable robots to understand and plan in physical settings, predicting that models designed for real-world understanding will be a key frontier beyond LLMs and digital agents. After LLMs and agents, the next AI frontier: video language models #RIaaP #worldmodels #ai #llm #videolanguagemodels https://lnkd.in/g62D9qug

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𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 #𝐀𝐈 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞 𝐈𝐬 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥—𝐚𝐧𝐝 𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 We’ve long known that Moore’s Law is slowing, and the von Neumann bottleneck is choking AI scalability. Now, a new architecture breaks through both. The 𝐌𝟑𝐃-𝐋𝐈𝐌𝐄 𝐜𝐡𝐢𝐩, published in Nature Communications, integrates logic, compute-in-memory, and content-addressable memory in a monolithic 3D stack. It achieves one-shot learning with 𝟗𝟔% 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲—matching GPU performance—while consuming 𝟏𝟖× 𝐥𝐞𝐬𝐬 𝐞𝐧𝐞𝐫𝐠𝐲 and running 𝟐.𝟕× 𝐟𝐚𝐬𝐭𝐞𝐫. This isn’t incremental. It’s foundational. 𝐀𝐧𝐚𝐥𝐨𝐠 𝐑𝐑𝐀𝐌 enables neuromorphic efficiency. 𝐓𝐂𝐀𝐌 𝐚𝐫𝐫𝐚𝐲𝐬 perform real-time Hamming distance calculations. 𝐁𝐄𝐎𝐋-𝐜𝐨𝐦𝐩𝐚𝐭𝐢𝐛𝐥𝐞 𝐟𝐚𝐛𝐫𝐢𝐜𝐚𝐭𝐢𝐨𝐧 ensures manufacturability and sovereignty. + 𝐅𝐨𝐫 𝐄𝐮𝐫𝐨𝐩𝐞, this means we can build 𝐞𝐧𝐞𝐫𝐠𝐲-𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭, 𝐬𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 without relying on legacy architectures. + 𝐅𝐨𝐫 𝐩𝐨𝐥𝐢𝐜𝐲𝐦𝐚𝐤𝐞𝐫𝐬, it’s a call to rethink digital strategy from the silicon up. 𝐖𝐞 𝐦𝐮𝐬𝐭 𝐬𝐭𝐨𝐩 𝐜𝐡𝐚𝐬𝐢𝐧𝐠 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐛𝐫𝐮𝐭𝐞 𝐟𝐨𝐫𝐜𝐞 𝐚𝐧𝐝 𝐬𝐭𝐚𝐫𝐭 𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐭𝐡𝐚𝐭 𝐫𝐞𝐬𝐩𝐞𝐜𝐭𝐬 𝐞𝐧𝐞𝐫𝐠𝐲, 𝐩𝐫𝐢𝐯𝐚𝐜𝐲, 𝐚𝐧𝐝 𝐥𝐨𝐜𝐚𝐥𝐢𝐭𝐲. https://lnkd.in/dg_8XVj8 𝘛𝘩𝘪𝘴 𝘤𝘰𝘯𝘵𝘦𝘯𝘵 𝘩𝘢𝘴 𝘣𝘦𝘦𝘯 𝘤𝘳𝘦𝘢𝘵𝘦𝘥 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘩𝘦𝘭𝘱 𝘰𝘧 𝘋𝘰𝘯𝘯𝘢, 𝘮𝘺 𝘈𝘐.

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🚀 Making Models Pay Attention: Inside the Mind of an LLM If you caught my earlier post on LLM — you already know that a Large Language Model is, at its core, a sophisticated text predictor. But have you ever wondered how it decides which words to focus on when generating an answer? How does your prompt — “What’s the recipe for dosa?” — transform into a coherent, context-aware response? That’s where the Attention Mechanism comes in — the heart of the Transformer architecture and the reason LLMs revolutionized NLP. 🧩 Here’s the intuition: Attention helps the model decide which tokens matter most when predicting the next one. It’s like giving each word a spotlight based on its importance to the overall meaning. When you input a sentence, the model computes three vectors for each token — Query, Key, and Value — and determines how much each token should “attend” to others. This is how the model captures relationships like: ‘The staple food of Punjab is Paratha, and the staple food of South India is ___.’ the model pays attention to the region–food pattern and confidently serves you Dosa! 🥞😋” That’s how LLMs learn — by attending to patterns, not memorizing cookbooks! What’s fascinating is that modern LLMs like GPT-4, Llama 3, and Mistral use multi-head attention, meaning multiple “perspectives” analyze context in parallel — enabling richer understanding and creativity. ⚡ Fun fact: Researchers discovered something called the Attention Sink, where the first token often attracts disproportionate attention — a curious artifact that might actually help stabilize training! The attention mechanism is what allowed Transformers to surpass older architectures like RNNs and LSTMs — and scaling it up is what made models suddenly capable of reasoning, coding, and storytelling. Let me know if you’d like me to dive deeper into how the attention mechanism really works.

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𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 6 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬: ➤ Sequential Architecture Tasks are processed step-by-step using a defined flow. Great for predictable, structured automation. ➤ Tool-Enhanced Memory LLMs access external tools to recall, transform, and reason with long-term data. Think smarter context, better answers. ➤ Multi-Agent Collaboration Multiple agents work together - each with specific roles. From researchers to planners, they form a digital workforce. ➤ Shared Tool Interfaces Agents use common tools (e.g., browsers, code editors) to stay aligned. Enables coordination and parallel task handling. ➤ Hierarchical Agent Design Supervising agents delegate tasks to sub-agents. Allows scalable, modular execution. ➤ Human-in-the-Loop AI + Human judgment = best outcomes. Crucial for oversight, ethics, and decision quality. 🌟 Follow the AIKaDoctor (Free AI & Data Science Resources)  channel on WhatsApp: https://lnkd.in/dCTCEKKc 📌Follow Habib Shaikh For more such content.

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GPT-5.4 can now control your computer. Not APIs — actual mouse and keyboard. The implication nobody's grasping: we're moving from "AI helps me" to "AI acts for me." That changes the human role entirely. From orchestrator to conductor. #AI #AIAgents #FutureOfWork https://lnkd.in/gwYveVN5

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I think Sam Altman, in his recent interview, essentially agreed with my paper “Turing Test 2.0: The General Intelligence Threshold”! He proposed that if a future AI model could “…figure out quantum gravity and could tell you its story,” that would indicate that AI has achieved AGI, and that a truly creative AI would “…come up with new scientific knowledge”. This perfectly aligns with the framework I proposed for testing AGI. I define AGI as the ability to generate new functionality by applying new knowledge that was not previously introduced through training. My framework (Turing Test 2.0) generates tests that can determine if a model has achieved AGI in a simple fail-pass result. I even present some examples of applying these tests to popular LLMs! Having a precise and measurable definition for AGI can help us better understand how close we are to achieving this goal. If you want to learn more about my work, you can read my paper, or you can listen to me discuss my work with Prof. Robert J. Marks in his podcast MindMatters in a three-part series (links below). You can read my paper here: https://lnkd.in/gStXD_pF You can listen to me talk about my work here: Part 1: https://lnkd.in/g-_tzJZt Part 2: https://lnkd.in/gdnZX6pz Part 3: https://lnkd.in/gaCBzPvZ Sam Altman’s interview: https://lnkd.in/gtENirSu

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