Exploring the Mindsets of Different Types of AI

👻👽The Magic of “Mixture-of-Experts” (MoE) 🧙♂️🧙♀️ Ever wondered how new AI models like GPT-5, Grok 4, or Gemini 2.5 Pro are becoming so powerful without costing a fortune to run? Meet Mixture-of-Experts (MoE) — a game-changing technique that makes Large Language Models (LLMs) more efficient, scalable, and specialized. 🧠✨ 🔹 What Is Mixture-of-Experts (MoE)? Think of MoE like a team of specialists. Instead of asking every team member to work on every task (like older AI models did), the system only calls the right experts for the job. 🧩 How it works: The model has many “experts” — small specialized sub-models (math, code, writing, etc.) A “gating network” acts like a manager, picking the best experts for the task Only those experts are activated → saving compute, boosting performance 👉 Result: The model becomes faster, cheaper, and smarter at focused tasks. 🔹 Real-World Examples (as of 2025) ModelDeveloperMoE?NotesGPT-5OpenAI✅ SpeculatedMassive scale, likely dynamic routingGrok 4xAI✅ ConfirmedMulti-agent MoE, very efficientGemini 2.5 ProGoogle✅ ConfirmedDesigned for efficient scalingClaude 4Anthropic❌Probably dense (no MoE yet)DeepSeek-V3DeepSeek✅ Confirmed671B total, 37B active per token 🔹 Why It Matters ✅ Efficiency: Uses less compute → faster and greener AI ✅ Scalability: Add more experts without slowing down ✅ Specialization: Experts learn unique skills → better accuracy ⚠️ Challenges Routing can sometimes misfire (wrong expert chosen) Requires more memory to store all experts Harder to interpret why a certain expert was picked 💡 TL;DR Mixture-of-Experts = Specialized AI teamwork. Instead of using the whole brain every time, the model just activates the smartest neurons for the task at hand. Smarter use of compute = better performance for less cost. That’s the future of AI — intelligent specialization. 🌍💻 👉 Curious takeaway: The next time you hear about “GPT-5” or “Grok 4,” know that there might be hundreds of tiny experts behind the scenes — working together to make your AI conversations faster and sharper than ever. Asharib Ali | Naeem H. | Ameen Alam | Daniyal Nagori | Muhammad Qasim | #AI #MachineLearning #Innovation #MoE #LLM #ArtificialIntelligence #GPT5 #DeepLearning #TechExplained #FutureOfAI

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Generative AI is the future of search, with AI-powered tools already making it easier than ever for owners to quickly access the information they need, no matter how disorganized their files are. But if all you’re doing is putting your documents into a generic AI tool, you’re probably not getting the quality responses that you need. At Gryps, we’ve developed a construction-specific knowledge graph that contextualizes all of your information, allowing our AI search tool to consistently provide relevant, reliable responses. Read our latest blog post to learn more: https://lnkd.in/ek3h3z9J

→ The Hidden Powerhouse Behind Next-Gen AI: The RAG Developer Stack 🔥 Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2 🔥 Access to all popular LLMs from a single platform: https://thealpha.dev What if I told you that the future of AI hinges on a little-known but unstoppable combination of technologies? This is not science fiction. It’s happening now. And if you’re not paying attention, you might get left behind. Here’s a quick dive into the core of Retrieval-Augmented Generation (RAG) - the secret sauce making AI smarter and faster every day: • Text Embeddings: The magic that turns words into math. Embeddings create rich vector representations of your data. Example: OpenAI’s text-embedding-ada-002, which distills context into usable numeric form. • Frameworks: Your toolkit for building RAG pipelines. Example: Haystack by deepset, which orchestrates search and generation for seamless AI workflows. • Vector Databases: The vault where embedded data lives and breathes, enabling lightning-fast retrieval. Example: Pinecone, optimized for scalable and low-latency vector search. • LLMs (Large Language Models): The brain that understands and generates language. Example: GPT-4, mastering everything from customer support to creative writing. • Data Extraction: Unlocking relevant insights from raw inputs. Example: spaCy for entity recognition and structure extraction. • Evaluation: Measuring AI’s precision and relevance to keep performance sharp. Example: datasets and metrics in Hugging Face for benchmarking RAG outputs. • Open LLMs: Democratizing access to powerful models with transparency. Example: LLaMA 2, offering customizable and open alternatives to closed AI giants. The RAG Stack is an ecosystem. Each part feeds the other, creating an AI feedback loop that’s both adaptive and powerful. Missing one link means losing the full impact.

→ The Hidden Powerhouse Behind Next-Gen AI: The RAG Developer Stack 🔥 Join Our community for latest AI updates: https://lnkd.in/gNbAeJG2 🔥 Access to all popular LLMs from a single platform: https://thealpha.dev What if I told you that the future of AI hinges on a little-known but unstoppable combination of technologies? This is not science fiction. It’s happening now. And if you’re not paying attention, you might get left behind. Here’s a quick dive into the core of Retrieval-Augmented Generation (RAG) - the secret sauce making AI smarter and faster every day: • Text Embeddings: The magic that turns words into math. Embeddings create rich vector representations of your data. Example: OpenAI’s text-embedding-ada-002, which distills context into usable numeric form. • Frameworks: Your toolkit for building RAG pipelines. Example: Haystack by deepset, which orchestrates search and generation for seamless AI workflows. • Vector Databases: The vault where embedded data lives and breathes, enabling lightning-fast retrieval. Example: Pinecone, optimized for scalable and low-latency vector search. • LLMs (Large Language Models): The brain that understands and generates language. Example: GPT-4, mastering everything from customer support to creative writing. • Data Extraction: Unlocking relevant insights from raw inputs. Example: spaCy for entity recognition and structure extraction. • Evaluation: Measuring AI’s precision and relevance to keep performance sharp. Example: datasets and metrics in Hugging Face for benchmarking RAG outputs. • Open LLMs: Democratizing access to powerful models with transparency. Example: LLaMA 2, offering customizable and open alternatives to closed AI giants. The RAG Stack is an ecosystem. Each part feeds the other, creating an AI feedback loop that’s both adaptive and powerful. Missing one link means losing the full impact.

The Agentic RAG Tech Stack: Blueprint for building AI Agents We are moving from LLMs and chatbots. They were the starting point but now the future is Agentic AI. Systems that reason, act, and learn across multiple layers of intelligence. I will breakdown the different layers that compose an AI Agent so it is easy to understand and some tools that can be used. 1. Deployment (The Power Source): This is the infrastructure layer - the engine that makes everything run. Platforms like AWS, GCP or cloud providers keep our agent alive and online. 2. Large Language Models (The Brain): This is the thinking and reasoning center. LLMs for example ChatGPT, Gemini, Claude have incorporated reasoning capabilities. They have the ability to understand, plan, and converse. 3. Frameworks (The Nervous System): Our Agent needs to connect the brain to its body. Frameworks like LangChain, LlamaIndex or DSPy act as the neural network that connect different sensors, memory or knowledge systems. They make sure all the layers are communicating to each other properly. 4. Vector Databases (The Memory Archive): This is like a library for our agent. Databases like Pinecone, Chroma, and Weaviate store information as vector embeddings (compressed memories) the agent can instantly recall when needed. It is like giving it a photographic memory of everything it's read. 5. Embeddings (The Translator): How will our Agent understand what is in the library? Embeddings from OpenAI, Nomic or Voyage AI convert words, text, or images into numbers which the agent's brain can actually process. They act as the translator between human input and machine input. 6. Data Extraction (The Explorer): Before our agent can think, it needs to gather information. Tools like Firecrawl, Docling or Scrapy act as the eyes and ears that crawl the web, read documents and pull in fresh data. They keep the agent up to date and informed. 7. Memory (The Experience Layer): Now the agent learns from experience.  Some examples like Mem0, Zep, or Letta give the agent short term and long term memory, so it remembers the information given previously and act in the present. Otherwise it would forget everything after each chat. 8. Alignment (The Moral Compass): We have given all the data, reasoning and thinking powers to our agent. It needs to know when to stop or what boundaries it should not cross. We need to guard it. Tools like Guardrails AI, Helicone or Arize make sure it behaves safely, ethically and reliably. They are like values and rules for the agent. It makes sure the agent does not go rogue or hallucinate. 9. Evaluation (The Teacher): In the end, how do we know our agent is learning properly? Some frameworks or benchmarks like LangSmith, Phoenix or DeepEval act as coaches, constantly testing performances, spotting errors and guiding improvements. Without this our agent will remain mediocre. 

🚀 Top AI Newsletters to Master AI, ML & AI Agents in 2025: Want to stay ahead in AI? Here are the must-read newsletters: 📬 Daily Reads (3-5 min): 1️⃣ Superhuman AI - 1M+ subscribers. Business-focused AI news, tools & tutorials trusted by professionals at OpenAI, Tesla, and Microsoft.https://https://lnkd.in/dEpvinPR 2️⃣ The Rundown AI - 1M+ subscribers. No-nonsense daily updates explaining why AI developments matter and how to apply them.https://https://lnkd.in/dcXBFDW6 3️⃣ The Neuron - 240K+ subscribers. Engaging AI breakdowns with technical depth made accessible. Includes free video courses.https://https://lnkd.in/d8Beadiq 4️⃣ There's An AI For That - 1.7M+ subscribers. Discover new AI tools daily, human-curated by John Hayes.https://https://lnkd.in/dcXBFDW6 📚 Weekly Deep Dives: ✅ The Batch (DeepLearning.AI) - Founded by Andrew Ng. Authoritative AI coverage with jargon-free explanations for all ✅ Import AI - By Jack Clark (Anthropic co-founder). Cutting-edge research + ethical implications of AI. ✅ TheSequence - 165K+ specialists trust this for technical ML concepts, research papers, and frameworks. Premium tier offers deep analysis. ✅ Latent Space - Essential for AI engineers. Covers AI agents, LLM tooling, multimodal AI, and infrastructure. 🤖 AI Agents Specialists: ♾️ AI++ Newsletter - Latest news for developers on AI agents and Model Context Protocol. ♾️ Latent Space - Dedicated coverage of agentic AI systems and coding tools. Book recommendation: 1️⃣ Building Agentic AI Systems by Anjanava Biswas & Wrick Talukdar - focuses on tool integration and planning with frameworks like CrewAI, AutoGen, and LangGraph. 2️⃣ AI Agents in Action by Michael Lanham - project-based approach with practical walkthroughs. 🎨 Generative AI Focus: ♾️ Syntha AI Newsletter - 5-minute weekly reads on cutting-edge GenAI techniques by PhD researchers. ♾️ Ben's Bites - 120K+ subscribers. AI startups, investments, and real-world use cases. Book recommendation: 1️⃣ Generative AI with LangChain (2nd Ed.) - covers RAG, multi-agent workflows, and production deployment. 2️⃣ Hands-On Generative AI with Transformers and Diffusion Models - practical guide for text, image, and audio generation. 🎓 Research & Academic: ♾️ Berkeley AI Research (BAIR) - University-level insights on diffusion models, RL algorithms, and more. ♾️ Machine Learning Pills - Weekly fundamentals to advanced techniques like RAG systems. Free ML ebook for new subscribers. My recommendation: Start with 2-3 that match your goals: 👉 Business/Career: Superhuman AI + The Rundown AI 👉 Technical/Engineering: Latent Space + TheSequence 👉 Research: The Batch + Import AI 👉 AI Agents: Latent Space + AI++ All offer free versions. Which ones are you reading? Drop your favorites in the comments! 👇 #ArtificialIntelligence #MachineLearning #AIAgents #GenerativeAI #TechNews #ProfessionalDevelopment

🚀 From Prompting to Building Thinking Machines — The Real Journey of AI Mastery Most people use AI like a calculator. A few build with it like creators. But the real leap begins when you make AI think, reason, and act like a teammate. Welcome to the Agentic AI Era — where Large Language Models don’t just respond, they plan, execute, and evolve. If you want to actually learn how to build these systems — here’s your hands-on roadmap 👇 🧠 1️⃣ Fine-Tune Your First LLM Goal: Teach the model your domain knowledge or your tone. Workflow: • Pick a base model (like Llama, Mistral, or OpenAI’s gpt-4o-mini). • Collect 200–500 curated examples of your domain Q&A (e.g., “Startup finance FAQs” or “Cloud troubleshooting tips”). • Use tools like LoRA or OpenAI Fine-Tuning API. • Evaluate: Does your model answer more like you now? 🤖 2️⃣ Build a Simple Multi-Agent System Goal: Make two agents talk to solve a task. Workflow: • Use Microsoft’s AI Agents for Beginners or LangChain + AutoGen. • Create: 🧩 Research Agent → searches web/data 🧩 Reasoning Agent → summarizes findings and drafts insights • Watch them chat and collaborate. That’s your first “AI team” in action. 🧩 3️⃣ Give Your Agent Real-World Powers Goal: Make your agent do things — not just talk. Workflow: • Integrate APIs: email, Google Calendar, or Slack. • Example: “Schedule my meeting and draft the summary note.” • Use function calling or tool execution in LangChain. When the AI acts — you’ve entered the Agentic Zone. 📈 4️⃣ Observe, Log, and Improve Goal: Teach your model how to self-correct. Workflow: • Capture logs of wrong answers. • Use feedback datasets for iterative fine-tuning. • Add a “critic agent” to review decisions before execution. Now, your system starts learning from its own mistakes. 💡 Pro tip: Start small. A single agent that fetches data and writes an email is more powerful (and educational) than an over-engineered 10-agent swarm. 🌍 The future won’t belong to those who just use AI — It’ll belong to those who can engineer behavior and build intelligence from scratch. Let’s make AI your co-founder, not your tool. Let’s build the future — one agent at a time. #AI #LLM #AgenticAI #FineTuning #LangChain #MicrosoftAI #OpenAI #GenerativeAI #MachineLearning #AIEngineering #AIWorkflows

🤖 From Prediction to Autonomy — Where Are We on the AI Curve? I recently came across this brilliant breakdown on the evolution of AI — from traditional machine learning all the way to Agentic AI. And as someone deeply curious about the intersection of AI, product thinking, and ethical tech, this resonates a lot with what I’m exploring during my MBA journey. 🔄 My key takeaway? AI’s journey isn’t just technical — it’s also philosophical. As we move from prompt-driven GenAI to autonomous AI agents, the questions Product Managers (and humans in general) must ask shift too: • Are we designing AI that aligns with human values? • Can our systems self-reflect and learn responsibly? • How do we balance autonomy with governance? ✨ I believe the future belongs to intelligent systems that act — but also understand. Would love to hear your thoughts — where do you see us headed next? #AgenticAI #ProductManagement #AIProductStrategy #MachineLearning #GenerativeAI #ResponsibleAI #WomenInTech #TechEthics #WittenborgMBA #MaedehZare #DigitalTransformation #AIinBusiness #FutureOfTech

AI Can’t Extrapolate — But Humans Can. That’s the Advantage. Artificial intelligence has made enormous progress in language, vision, coding, and automation. But there’s a fundamental capability it still lacks: extrapolation : the ability to reason beyond existing data and imagine what doesn’t yet exist. This limitation is not a bug. It’s a direct outcome of how today’s AI systems are built and trained. AI is Optimized for Interpolation, Not Invention Modern models, from GPT to diffusion systems learn statistical patterns from vast datasets. They excel at: - Summarizing existing knowledge - Remixing familiar structures - Predicting the “most likely” continuation That is interpolation. But extrapolation ie: generating correct, coherent ideas outside the known distribution, requires abstract reasoning and symbolic generalization that these models do not inherently possess. Give AI something radically new, and it will try to approximate it using familiar patterns, and very often unsuccessfully. Why Humans Still Matter ? Humans can do what AI cannot: - Invent new directions - Form abstractions from limited examples - Ask “what if?” without precedent - Judge novelty and utility in context This isn’t just creativity, it’s strategic generalisation grounded in understanding and intent. Humans extrapolate naturally. AI imitates what it has seen. The Winning Model: Human Extrapolation and AI Execution Instead of waiting for AGI to emerge, the most powerful approach right now is collaborative intelligence: - Humans define the leap : a hypothesis, design, strategy, product, or scenario that doesn’t yet exist. - AI expands : tests, drafts, models, simulates, refines, or implements across large search spaces. - Humans steer direction and evaluate what’s actually new or valuable. - AI handles iteration, translation, and execution at scale. In this partnership: The human sets the vector (direction). The AI provides the acceleration (speed and scale). This Isn’t Speculation — It’s Emerging Practice Research in areas like: Human-in-the-loop machine learning World models Neurosymbolic reasoning Interactive concept development Collaborative prompting is converging on the same insight: AI doesn’t need to replace human reasoning but just needs to amplify it. The future isn’t fully automated intelligence. It is co-intelligence. The Takeaway AI will not become extrapolative just by scaling more parameters. But that’s not a roadblock, it’s a design opportunity. The teams and individuals who win will be those who use AI to operationalize their ideas, not rely on AI to originate them. Humans extrapolate. AI executes. Together, that combination is a superpower.