Understanding Complex Concepts

What most people think AI looks like vs. what AI actually is 🔹 Many people have misconceptions about AI, often influenced by media portrayals and popular beliefs! Let's talk about some common myths and the reality of AI: 1️⃣ Data:  What most people think: They believe that AI is primarily about collecting and analyzing massive amounts of data. ✅ What AI actually is: Data is indeed crucial for AI, but it's not just about quantity. Quality, relevance, and diversity of data, along with effective data management practices, are essential for accurate and meaningful AI-driven insights 2️⃣ Data Science: What most people think: They perceive AI as a field dominated solely by data scientists who crunch numbers and make predictions ✅ What AI actually is: Data science is vital to AI, but it's not the sole focus. AI encompasses a range of disciplines, including machine learning, natural language processing, and computer vision, working together to extract value from data 3️⃣ Value: What most people think: They expect AI to deliver tangible business value and maximize profits effortlessly instantly ✅ What AI actually is: While AI has the potential to generate significant value, it requires a strategic approach and careful implementation. Realizing the benefits of AI often involves incremental progress, continuous improvement, and aligning AI initiatives with specific goals 4️⃣ Data Engineering:  What most people think: They consider data engineering as a secondary concern compared to developing AI models ✅ What AI actually is: Data engineering plays a critical role in the AI journey. It involves collecting, storing, and preprocessing data, ensuring its quality and accessibility. Without proper data engineering practices, AI models may suffer from poor performance or biases 5️⃣ Modeling and Operationalizing:  What most people think: They see building AI models as the ultimate goal, often overlooking the challenges of deployment ✅ What AI actually is: Model development is just one aspect. Operationalizing AI models in real-world scenarios involves integrating them into existing systems, monitoring their performance, and ensuring ongoing maintenance and updates. To truly understand the potential of AI, it's crucial for most people to move beyond misconceptions and buzzwords. By recognizing the importance of data, data science, value generation, data engineering, modeling, and operationalizing, individuals can gain a deeper understanding of AI's true capabilities!

As we move from LLM-powered chatbots to truly 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, understanding 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 becomes non-negotiable. Agentic AI isn’t just about plugging an LLM into a prompt—it’s about designing systems that can 𝗽𝗲𝗿𝗰𝗲𝗶𝘃𝗲, 𝗽𝗹𝗮𝗻, 𝗮𝗰𝘁, 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻 in dynamic environments. Here’s where most teams struggle:  They underestimate the 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 required to support agent behavior. To build effective AI agents, you need to think across four critical dimensions: 1. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Agents should break down goals into executable steps and act without constant human input. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Agents need long-term and episodic memory. Vector databases, context windows, and frameworks like Redis/Postgres are foundational. 3. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Real-world agents must invoke APIs, search tools, code execution engines, and more to complete complex tasks. 4. 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 – Single-agent systems are powerful, but multi-agent orchestration (planner-executor models, role-based agents) is where scalability emerges. The ecosystem is evolving fast—with frameworks like 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, 𝗔𝘂𝘁𝗼𝗚𝗲𝗻, 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, and 𝗖𝗿𝗲𝘄𝗔𝗜 making it easier to move from prototypes to production. But tools are only part of the story. If you don’t understand concepts like 𝘁𝗮𝘀𝗸 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹𝗻𝗲𝘀𝘀, 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, your agents will remain shallow, brittle, and unscalable. The future belongs to those who can 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗟𝗟𝗠 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗿𝗼𝗯𝘂𝘀𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻. That’s where real innovation happens. 2025 will be the year we go from prompting to architecting.

The rise of agentic reasoning promises to transform how we work by creating AI systems that can autonomously pursue goals and complete complex tasks. Glean is at the forefront of this shift, pioneering a new agentic reasoning architecture that expands AI’s potential to get work done: resolve support tickets, help engineers debug code, and adapt tone of voice for corporate communications. With our new architecture, agents break down complex work into multi-step plans. Steps are executed by AI agents that are trained on their tasks and equipped with the tools to achieve their goals. Early research shows a 24% increase in relevance with our new agentic reasoning architecture. Here’s a preview of the new architecture: 🔷 Search: Evaluate the query and, using heuristics, determine whether it can be answered using search or agentic reasoning. 🔷 Reflect: Reflect on the initial search results, gauge confidence in the result, and decide whether to return a result or keep going down the agentic reasoning path. Search → fast and accurate answers Agentic reasoning → complex multi-step queries 🔷 Plan: Formulate the strategy, deeply understanding the goal and breaking down the steps to achieve it. Figure out the specialized sub-agents and tools to achieve each step of the work. 🔷 Execute: Sub-agents reason about the tools to use-search, data analysis, email, calendar, employee search, expert search, etc.- and how to stitch them together to achieve individual goals. 🔷 Respond: Respond in natural language via chat or by taking an action like creating a Jira ticket. Reimagining Work AI is an ongoing journey that builds on our foundational technologies. We began with search and advanced to RAG; now we’re progressing from RAG to agentic reasoning. We remain committed to pushing the boundaries of what AI can achieve in the workplace. This is the AI journey we envision for all our customers, where continuous innovation and practical application go hand in hand to transform the future of work. https://bit.ly/3ZdIWvg

A brilliant idea isn’t a fact—until it is. Many groundbreaking discoveries seem obvious only in hindsight, once they unify a web of seemingly isolated facts into a general principle. Before we connected the dots between evolution, genetics & material science, silk was just a thread, proteins were just biological molecules, & genes were just codes. But once we saw their relationships, we unlocked deep truths about how nature builds materials at every scale. What If AI Could Think in Relationships Instead of Just Memorizing? Most AI today doesn’t work this way. It merely predicts the next token, unaware of whether its own output is meaningful, correct, or groundbreaking. They: ❌ Lack true reasoning—they do not verify if their responses make sense. ❌ Cannot correct themselves—once they generate something, they have no mechanism to reflect and refine their own ideas. ❌ Do not connect ideas deeply—they retrieve, not discover. 💡 SciAgents does something different. Rather than treating knowledge as isolated facts, it builds a massive relational graph, connecting every concept and idea to others. Then, a team of AI agents explores this graph, not just by taking the shortest path between ideas, but by wandering through unexpected links. How SciAgents Reasons over Graphs ▶️Instead of taking the shortest path between two ideas (which can be too direct & limiting), SciAgents samples diverse paths through a powerful algorithm that explores ever-growing sets of diverse waypoints. This allows it to natively explore broader, richer relationships—leading to unexpected discoveries. ▶️For example, to explore the connection between silk and energy efficiency, SciAgents didn’t just look at direct links. It uncovered intermediate concepts like biocompatibility, multifunctionality & structural coloration, revealing new ways to design bioinspired materials that human researchers might have overlooked. Why does this matter for building better AI for science and beyond? 1⃣Generalization is the key to intelligence. Memorization alone won’t get AI to true reasoning—but structuring knowledge in a relational way can. 2⃣SciAgents goes beyond predicting words. It constructs maps of ideas by conceptual blueprints, from genes encoding proteins to evolutionarily refined materials like silk, and extrapolates new designs. 3⃣It refines its own outputs. Rather than passively generating text, SciAgents’ multi-agent system debates, critiques, and improves hypotheses, making its discoveries deeper and more reliable. Graph-based reasoning plus multi-agent collaboration is not just a better way for AI to think—it’s likely on a critical path towards AGI. The ability to form deep, structured insights from sparse information is what separates mere computation from true intelligence. A. Ghafarollahi, M.J. Buehler, SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning, Adv. Materials, DOI: 10.1002/adma.202413523, 2025

Correlation ≠ Causation: Here’s why seeing a trend doesn’t always mean you’ve found a direct cause. Did you know that there is correlation between arcade revenue and the number of computer science doctorates awarded in the U.S.? The two lines move in unison, but does that mean arcades are driving more people to pursue computer science degrees? Uh, no. When two variables are correlated, it doesn't automatically indicate causality. In fact, there could be several other explanations for this trend: 1. Reverse Causation: Instead of arcades influencing an increase in computer science PhDs, it could be the other way around. Perhaps those with a passion for gaming and programming are bringing back arcades! (Although this scenario is improbable, it's fun to consider.) 2. Consequence of Common Cause: Another possibility is that a third variable — such as advancements in technology or a rise in interest for digital entertainment — is simultaneously impacting both arcade revenue and computer science graduates. Therefore, both are increasing due to a shared underlying factor. 3. Indirect Causation: There may be indirect effects at play, where one variable affects another through a chain of factors. For instance, the growing tech industry could be fueling interest in both computer science and gaming culture, indirectly connecting these two variables. 4. Pure Coincidence: Sometimes, it's purely coincidental. With so many events occurring in the world, there will inevitably be instances where unrelated trends seem to align. In this case, the rise in arcade revenue and PhDs could simply be coincidental. Correlation is a useful starting point when analyzing data, but always remember: just because two lines appear to move together doesn't necessarily mean one is causing the other. Dive deeper into the data before assuming causality. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Researchers from Oxford University just achieved a 14% performance boost in mathematical reasoning by making LLMs work together like specialists in a company. In their new MALT (Multi-Agent LLM Training) paper, they introduced a novel approach where three specialized LLMs - a generator, verifier, and refinement model - collaborate to solve complex problems, similar to how a programmer, tester, and supervisor work together. The breakthrough lies in their training method: (1) Tree-based exploration - generating thousands of reasoning trajectories by having models interact (2) Credit attribution - identifying which model is responsible for successes or failures (3) Specialized training - using both correct and incorrect examples to train each model for its specific role Using this approach on 8B parameter models, MALT achieved relative improvements of 14% on the MATH dataset, 9% on CommonsenseQA, and 7% on GSM8K. This represents a significant step toward more efficient and capable AI systems, showing that well-coordinated smaller models can match the performance of much larger ones. Paper https://lnkd.in/g6ag9rP4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai

Remember when learning something new meant endless Google searches and YouTube videos? Those days are gone. AI has completely transformed how we can learn anything - and I mean ANYTHING. Let me show you how. 1. The "Explain Like I'm New" method This works great when you're totally lost about something. AI breaks down complex concepts using everyday terms and examples. 📝 Prompt: "Explain [topic] to someone who has zero background in it. Use everyday examples and avoid technical terms. Focus on core concepts that make it click." 2. Examples that actually make sense Ask AI to explain things using real-life examples. This makes abstract concepts feel more concrete. 📝 Prompt: "Give me 3 real-world examples that explain [concept]. Use scenarios from daily life that would help a beginner understand this better." 3. Getting motivated when you're stuck When you're feeling "meh" about learning, you can get strategies from AI that actually work for your situation. 📝 Prompt: "I'm struggling to stay motivated while learning [topic]. Share 5 practical strategies to regain momentum, and explain how each one helps overcome common roadblocks." 4. Practice through role-play Have AI play different roles to help you learn. This is perfect for practicing conversations and scenarios. And it is less awkward than practicing with real humans, isn't it? 📝 Prompt: "Let's do a role-play where you are [role] and I am [role]. We'll practice [specific situation]. Give me feedback on my responses and suggest improvements." 5. Creating study plans that don't overwhelm Tell AI your goal and available time. Get a broken-down plan that's actually doable for you. 📝 Prompt: "Create a 30-day study plan for learning [topic]. I can dedicate [X] hours per week. Break it down into small, manageable daily tasks and include milestones to track progress." 6. Quick knowledge checks Ask AI to quiz you on what you've learned and get explanations for wrong answers. 📝 Prompt: "Create a mix of 5 easy, medium, and challenging questions about [topic]. Include explanations for each answer and point out common misconceptions." 7. Connecting the dots See how different ideas link together. It makes remembering what you've learned a lot easier. Here's how: 📝 Prompt: "Create a concept map showing how [topic] connects to other related ideas. Explain each connection and why it matters." Pro tips for better results: - Be specific about your current level - Mention your learning style - Ask for examples you can relate to - Request simpler explanations if needed Remember: AI is like having a patient friend who never gets tired of explaining things. The key is asking the right questions! Try these prompts and let me know how they work for you! 👇 🔁 Repost if this inspired you. 💻 And follow #AIwithAnurupa to stay updated with everything AI. #ai #artificialintelligence #learning #education

STOP USING BUZZWORDS, START SPEAKING ENGLISH If you can’t explain a concept in plain English or better yet, in a single sentence you probably don’t understand it yet. I’ve sat in too many meetings where everyone nods along at terms no one actually understands. Or, even worse, everyone THINKS they understand, but they all have a completely different definition and then wonder why the conversation is going nowhere. “Cross-screen convergence” "Holistic measurement framework" "Cloud native measurement stacks" Say enough buzzwords in a row ("Leveraging AI-powered DCO to drive scalable creative versioning across omnichannel touchpoints.") and people stop asking questions. But that’s the moment you should lean in. The best strategists aren’t fluent in jargon. They’re fluent in translation. Here’s what’s worked for me and why TVREV is known for our ability to express complex thoughts in plain English without dumbing it down. 1️⃣ Swap buzzwords for metaphors. “buy this thing now" vs "think good thoughts about us” beats “performance vs brand.” 2️⃣ Ask, “How would you explain this to your grandmother?” 3️⃣ When in doubt: simplify, then simplify again. If you want to be taken seriously, stop trying to sound smart. Start trying to be understood. 🤪

Complexity used to be the cost of scale. Now, it’s the tax on speed. For leaders of my generation, complexity has been our conditioning because we were taught that it equals competence. Dense slide decks made me feel credible. Multilayered strategies made me feel indispensable. Overpacked calendars gave me the illusion of control. Over time, I saw what complexity actually does: it slows decisions, dilutes focus, and distances leaders from outcomes. What I once thought made me look smart was actually keeping me stuck. We are no longer rewarded for how much we manage, how long we work, or how complex we sound. We are rewarded for how clearly we lead, how quickly we decide, and how efficiently we execute. Yet, reduction is deceptively hard for senior executives because reduction challenges identity. It confronts ego. Senior leaders don’t need to do more. We need to do fewer things faster and better with tools and thinking that match the velocity of this new era. STRATEGIC COMPLEXITY 🚫 Long decks. Vague goals. Annual cycles that feel irrelevant after six weeks. 👉 The shift: Move to lean, AI-assisted strategy cycles. Think quarterly focus, not yearly sprawl. OPERATIONAL COMPLEXITY 🚫 Bloated workflows. Too many approvals. Manual check-ins across disconnected tools. 👉 The shift: Cut, automate, or reassign. Simpler systems lead to faster movement. COMMUNICATION COMPLEXITY 🚫 Email chaos. Unclear messaging. Meetings that go nowhere. 👉 The shift: Move to asynchronous clarity with AI-generated briefs. The next era will be led by those who simplify the fastest. That's the new currency of high-performance leadership. Outcomes improve not by layering more controls but by returning to the essential. As John Maeda says: “Simplicity is about subtracting the obvious and adding the meaningful.” Where are you mistaking complexity for value and what can you strip away? #leadership #transformation #change

Happy Friday, this week in #learnwithmz lets talk about 𝐀𝐈 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠. Most of the focus in AI has been on scaling up and out: more data, longer context windows, bigger models. But in my opinion one of the most exciting shifts is happening in a different direction: Reasoning. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐈 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠? Reasoning allows a model to: > Break problems into smaller steps > Compare options and evaluate outcomes > Combine facts logically > Review and improve its own outputs Language models are great with patterns, but they often struggle with logic, math, or planning. Reasoning techniques aim to make them smarter, not just bigger. 𝐊𝐞𝐲 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 > Chain of Thought (CoT) The model thinks out loud, step by step. Example: “Let’s solve this carefully, one step at a time.” > Tree of Thoughts (ToT) The model explores multiple possible answers in parallel, like different paths. Useful for puzzles, planning, and creative writing. Paper (https://lnkd.in/gbJhTS6q) | Code (https://lnkd.in/g9vdA4qm) > Graph of Thoughts (GoT) The model builds and navigates a reasoning graph to compare and revise ideas. Paper (https://lnkd.in/gW2QcBZU) | Repo (https://lnkd.in/gC_QSFcQ) > Self-Refinement The model reviews and edits its own output to improve accuracy or quality. Works well for writing, code, and structured tasks. 𝐖𝐢𝐭𝐡 𝐨𝐫 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞? Reasoning workflows can be used in both static and dynamic AI systems. > Without AI agents Reasoning happens in a single prompt or series of prompts. You ask the model to "think step by step" or use a CoT or ToT workflow manually. This works well for individual tasks like solving a math problem, drafting content, or analyzing a dataset. > With AI agents Reasoning becomes part of an ongoing process. Agents use tools, memory, and feedback loops to plan and adapt over time. They might use reasoning to decide which action to take next, evaluate outcomes, or retry when they fail. Reasoning becomes part of autonomous behavior. Simple way to think is reasoning is the brain. Agents are the body. You can use reasoning alone for smart responses or combine it with agents for end-to-end execution. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 Writing tools that plan, draft, and edit content. Data agents that walk through logic and insights. Tutoring tools that teach by showing reasoning. Business agents that plan tasks and retry failures. Copilots that reason about which tool or API to use next. Reasoning workflows are helping smaller models solve bigger problems. They make AI more reliable, interpretable, and useful. This is how we move from chatbots to actual collaborators. #AI #AIReasoning #ChainOfThought #LLMEngineering #AIAgents #ArtificialIntelligence #learnwithmz