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Your best coach can't be everywhere at once.
Your best coach can't be everywhere at once.
Your best coach can't be everywhere at once. ๐˜‰๐˜ถ๐˜ต ๐˜ต๐˜ฉ๐˜ฆ๐˜ช๐˜ณ ๐˜ˆ๐˜ ๐˜ต๐˜ธ๐˜ช๐˜ฏ ๐˜ค๐˜ข๐˜ฏ. Scaling world-class coaching is one of the biggest headaches in L&D. You bring in a top-tier expert for a workshop, and the C-suite loves it; then what? The knowledge fades, and the cost to retain them for 1-on-1 coaching across the org is astronomical. Well, the ability to have experts available 24/7 is now a reality. Google is quietly testing a potential solution in its Labs. ๐—œ๐˜'๐˜€ ๐—ฐ๐—ฎ๐—น๐—น๐—ฒ๐—ฑ ๐—ฃ๐—ผ๐—ฟ๐˜๐—ฟ๐—ฎ๐—ถ๐˜๐˜€. Itโ€™s more than a chatbot. Itโ€™s a library of voice-enabled, AI-powered avatars of real-world experts, trained only on their unique ideas and content. What that means: โ†’ Minimal AI hallucinations โ†’ No generic advice โ†’ Just the expert's authentic perspective, on-demand Check out this screenshot of Google Portraits. Thatโ€™s an AI version of storytelling expert Matt Dicks. Heโ€™s coaching me to find the "heart of a story" in a seemingly dull, everyday moment โ€” cutting grass. It's a very immersive experience as he walks me through finding the "story" in my experience. Think about the possibilities: โ†’ Democratize coaching: Assign a storytelling coach or a feedback sparring partner to every new manager. โ†’ Practice in private: Let employees rehearse difficult conversations in a safe and controlled environment before the real thing. โ†’ Scalable IP: A new model for licensing and deploying the knowledge of the world's best minds across your entire company. This is the future of personalized, scalable learning. Itโ€™s moving from static courses to dynamic, conversational experiences. The big question for us in L&D: Is this the scalable future we've been waiting for, or are we losing the essential human element of coaching? | 12 comments on LinkedIn
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Your best coach can't be everywhere at once.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really canโ€™t believe how much smoother everything gets.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really canโ€™t believe how much smoother everything gets.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really canโ€™t believe how much smoother everything gets. ๐—œ๐—ณ ๐—œ ๐—ต๐—ฎ๐—ฑ ๐˜๐—ผ ๐—ฏ๐—ฒ๐˜ ๐—ผ๐—ป ๐—ผ๐—ป๐—ฒ ๐—ฝ๐—ฟ๐—ผ๐˜๐—ผ๐—ฐ๐—ผ๐—น ๐—ฏ๐—ฒ๐—ฐ๐—ผ๐—บ๐—ถ๐—ป๐—ด ๐—ฒ๐˜€๐˜€๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น ๐—ถ๐—ป ๐—”๐—œ, ๐—ถ๐˜โ€™๐˜€ ๐— ๐—–๐—ฃ. MCP sounds complex โ€” but itโ€™s really not. Think of it as a guide that helps your AI agents understand: โ†’ what tools exist โ†’ how to talk to them โ†’ and when to use them ๐—›๐—ฒ๐—ฟ๐—ฒ ๐—ฎ๐—ฟ๐—ฒ ๐Ÿต ๐—ณ๐˜‚๐—น๐—น๐˜† ๐—ฑ๐—ผ๐—ฐ๐˜‚๐—บ๐—ฒ๐—ป๐˜๐—ฒ๐—ฑ ๐— ๐—–๐—ฃ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐˜€ ๐—ฒ๐˜…๐—ฝ๐—น๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐˜„๐—ถ๐˜๐—ต ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐˜€ & ๐—ผ๐—ฝ๐—ฒ๐—ป-๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ ๐—ฐ๐—ผ๐—ฑ๐—ฒ (๐˜๐—ผ ๐—ด๐—ฒ๐˜ ๐˜†๐—ผ๐˜‚ ๐˜€๐˜๐—ฎ๐—ฟ๐˜๐—ฒ๐—ฑ):ย โฌ‡๏ธ 1. 100% Local MCP Client โ†’ Build a local MCP client using SQLite + Ollama โ€” no cloud, no tracking. โ†’ Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG โ†’ Add fallback logic, vector search, and agents in one clean flow. โ†’ Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst โ†’ Fetch stock data, extract insights, generate summaries. โ†’ Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent โ†’ Speech-to-text, database queries, and spoken responses โ€” all local. โ†’ Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) โ†’ Query 200+ data sources via natural language using MindsDB + Cursor. โ†’ Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor โ†’ Build cross-app memory for dev workflows โ€” share context seamlessly. โ†’ Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs โ†’ Tackle PDFs, tables, charts, messy layouts with structured RAG. โ†’ Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) โ†’ Generate synthetic tabular data locally via MCP + SDV. โ†’ Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher โ†’ Rebuild ChatGPTโ€™s research mode, fully local with writing agents. โ†’ Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn
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๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really canโ€™t believe how much smoother everything gets.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt. Bereits letztes Jahr war ich von der politischen Haltung und konsequenten Umsetzung Estlands fasziniert, als ich u.a. mit der Botschafterin der Republik Estland, Marika Linntam, auf dem Panel der IHK Berlin รผber die Arbeitswelt der Zukunft diskutieren durfte. AI Leap ist Estlands Antwort auf die vielseitigen Herausforderungen im Bildungsbereich und fรถrdert frรผhzeitig notwendige Schlรผsselkompetenzen, die fรผr den Arbeitsmarkt der Zukunft unerlรคsslich sind. Estland hat erkannt, dass ein professioneller Umgang mit KI-Technologien der wichtigste Wettbewerbsfaktor der Zukunft sein wird. Das war auch eine meiner insgesamt 4 Thesen, die ich vorab in einer Keynote vorstellen durfte, den kompletten Vortrag findet ihr hier: https://lnkd.in/dTdXMGuA ๐Ÿ…ฐ๐Ÿ…ฑ๐Ÿ…ด๐Ÿ†: ๐ŸŽฏ WO STEHEN WIR IN DEUTSCHLANDโ“ ๐ŸŽฏ Wie kรถnnen wir trotz Bildungsfรถrderalismus schnell wirksam werdenโ“ Spannende Fragen fรผr unsere neue Regierung v.a. mit Blick auf das Bundesministerium fรผr Digitales und Staatsmodernisierung unter Leitung von Dr. Karsten Wildberger, das die #Digitalisierung und die #KI #KรผnstlicheIntelligenz in Deutschland auf ein nรคchstes Level heben will. Was mir gefรคllt ist die Aufbruchstimmung und ein #WirMachen. Ich hoffe, dass es gelingt, etwas zu bewegen und die entsprechenden Stakeholder einzubinden. Ich bin gerne dabei, denn da gibt es noch VIEL ZU TUN. Estland macht es vor! Es ist zwar viel kleiner als Deutschland, dennoch kรถnnen wir viel von Estland (und anderen Lรคndern) lernen v.a. wenn wir in globale Kooperationen und in Public-Private-Partnership Modelle investieren. Quelle: https://lnkd.in/eUzXiSza #FutureOfWork #FutureSkills #SmartLearning :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ๐Ÿ”” Du mรถchtest mehr รผber die Arbeitswelt im Wandel zu erfahren? Let's connect! ๐Ÿ’Œ Du interessierst Dich fรผr eine Zusammenarbeit? Schreib mir gerne!
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๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now. While most teams are stuck at the point of innovations from 2 years back, you can be ahead of this. Yet...I still see a lot of talk and not so much action, sprinkled with a lot of misinformation and actual understanding of Gen AI's power and limitations. That creates a problem if the L&D industry wishes to thrive in the new world of work with AI. Thatโ€™s not to say I have โ€œall the answersโ€, coz I donโ€™t What I do have is a barrel load of real-world experiences working with teams on making AI adoptions a success. In tmrw's Steal These Thoughts! newsletter I'm going to share some of that with 5 insights that'll challenge everything you think you know about AI in L&D. Like the sound of that? โ†’ Join us by clicking 'subscribe to my newsletter' on this post and my profile. #education #learninganddevelopment #artificialintelligence
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When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
This is the feature I've been waiting for OpenAI to release. It's not "game-changing", but it's incredibly useful. Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT. Switch the model to o3 and use it as it was intended in my original design. Here's a little how-to video with my GPT in action. Find my GPT: https://lnkd.in/e2pdCKt8 #education #artificialintelligence #learninganddevelopment
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Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
We might think we have an AI adoption story, but the reality is different: we still have a huge AI understanding gap! Here are some key stats from the report that honestly made me do a double-take: โ–ช๏ธ99% of education leaders, 87% of educators worldwide & 93% of US students have already used generative-AI for school at least once or twice! โ–ช๏ธYet only 44% of those educators worldwide & 41% of those US students say they โ€œknow a lot about AI.โ€ โ€ผ๏ธthis means our usage is far outpacing our understanding & thatโ€™s a significant gap! When such powerful tools are used without real fluency, we would see: โ–ช๏ธcomplicated implementation with no shared strategy (sounds familiar?)! โ–ช๏ธanxious students whoโ€™d fear being accused of cheating (I've heard this from so many students!) โ–ช๏ธoverwhelmed teachers who feel alone, unsupported & unprepared (this one is a common concern by some of my teacher friends)! The takeaway that jumped out at me: โ–ช๏ธthe schools that win won't be the ones that adopt AI the fastest, but the ones that adopt it the wisest! So here's what Iโ€™d think we should consider: โœ…building a "learning-first" culture across institutions & understanding when AI supports our learning vs. when it gets in the way! โ–ช๏ธmore like, we need to swap the question "Are we using AI?" for "Can we show any learning gains?" โš ๏ธso, what shifts does this report data point us to? Here is my takeaway: โœ…Building real AI fluency: โ–ช๏ธmoving beyond simple "prompting hacks" to true literacy that includes understanding ethics, biases & pedagogical purposes, โ–ช๏ธthis may need an AI Council of faculty, IT, learners & others working together to develop institution-wide policies on when AI helps or harms our learning, โ–ช๏ธit's about building shared wisdom, not just industry-ready skills โœ…Creating collaborative infrastructure: โ–ช๏ธthe "every teacher for themselves" approach seems to be failing, โ–ช๏ธshared guidelines, inclusive AI Councils & a culture of open conversation are now needed to bridge this huge gap! โœ…Shifting focus from "using AI tools" to "achieving learning outcomes": โ–ช๏ธthis one really resonated with me because unlike other tech rollouts we've witnessed, AI directly affects how our students think & learn, โ–ช๏ธour institutions need coordinated assessments tracking whether AI use makes our learners better thinkers or just faster task completers! The goal that keeps coming back to us โ–ช๏ธisn't to get every student using AI! โ–ช๏ธbut to make sure every learner & teacher really understands it! โ‰๏ธIโ€™m curious, where is your institution on this journey? 1๏ธโƒฃ individual use: everyone is figuring it out on their own (been there!) 2๏ธโƒฃ shared guidelines: we have policies, but they're not yet deeply integrated (getting closer!) 3๏ธโƒฃ fully integrated strategy: we have a unified approach with a learning-first, outcome-tracked focus (this is the goal!) | 24 comments on LinkedIn
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I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. โฌ‡๏ธ ๐˜“๐˜ฆ๐˜ต'๐˜ด ๐˜ฃ๐˜ณ๐˜ฆ๐˜ข๐˜ฌ ๐˜ช๐˜ต ๐˜ฅ๐˜ฐ๐˜ธ๐˜ฏ: ๐—ง๐—ผ๐—ธ๐—ฒ๐—ป๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป & ๐—˜๐—บ๐—ฏ๐—ฒ๐—ฑ๐—ฑ๐—ถ๐—ป๐—ด๐˜€: - Input text is broken into tokens (smaller chunks). - Each token is mapped to a vector in high-dimensional space, where words with similar meanings cluster together. ๐—ง๐—ต๐—ฒ ๐—”๐˜๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ผ๐—ป ๐— ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐˜€๐—บ (๐—ฆ๐—ฒ๐—น๐—ณ-๐—”๐˜๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ผ๐—ป): - Words influence each other based on context โ€” ensuring "bank" in riverbank isnโ€™t confused with financial bank. - The Attention Block weighs relationships between words, refining their representations dynamically. ๐—™๐—ฒ๐—ฒ๐—ฑ-๐—™๐—ผ๐—ฟ๐˜„๐—ฎ๐—ฟ๐—ฑ ๐—Ÿ๐—ฎ๐˜†๐—ฒ๐—ฟ๐˜€ (๐——๐—ฒ๐—ฒ๐—ฝ ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ ๐—ฃ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐˜€๐˜€๐—ถ๐—ป๐—ด) - After attention, tokens pass through multiple feed-forward layers that refine meaning. - Each layer learns deeper semantic relationships, improving predictions. ๐—œ๐˜๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป & ๐——๐—ฒ๐—ฒ๐—ฝ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด - This process repeats through dozens or even hundreds of layers, adjusting token meanings iteratively. - This is where the "deep" in deep learning comes in โ€” layers upon layers of matrix multiplications and optimizations. ๐—ฃ๐—ฟ๐—ฒ๐—ฑ๐—ถ๐—ฐ๐˜๐—ถ๐—ผ๐—ป & ๐—ฆ๐—ฎ๐—บ๐—ฝ๐—น๐—ถ๐—ป๐—ด - The final vector representation is used to predict the next word as a probability distribution. - The model samples from this distribution, generating text word by word. ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐—บ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐—ฐ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ฎ๐˜ ๐˜๐—ต๐—ฒ ๐—ฐ๐—ผ๐—ฟ๐—ฒ ๐—ผ๐—ณ ๐—ฎ๐—น๐—น ๐—Ÿ๐—Ÿ๐— ๐˜€ (๐—ฒ.๐—ด. ๐—–๐—ต๐—ฎ๐˜๐—š๐—ฃ๐—ง). ๐—œ๐˜ ๐—ถ๐˜€ ๐—ฐ๐—ฟ๐˜‚๐—ฐ๐—ถ๐—ฎ๐—น ๐˜๐—ผ ๐—ต๐—ฎ๐˜ƒ๐—ฒ ๐—ฎ ๐˜€๐—ผ๐—น๐—ถ๐—ฑ ๐˜‚๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ถ๐—ป๐—ด ๐—ต๐—ผ๐˜„ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—บ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐—ฐ๐˜€ ๐˜„๐—ผ๐—ฟ๐—ธ ๐—ถ๐—ณ ๐˜†๐—ผ๐˜‚ ๐˜„๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐˜€๐—ฐ๐—ฎ๐—น๐—ฎ๐—ฏ๐—น๐—ฒ, ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ผ๐—ป๐˜€๐—ถ๐—ฏ๐—น๐—ฒ ๐—”๐—œ ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€. Here is the full video from 3Blue1Brown with exaplantion. I highly recommend to read, watch and bookmark this for a further deep dive: https://lnkd.in/dAviqK_6 ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E | 48 comments on LinkedIn
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๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice. Researchers created an AI called "Centaur" that can predict human behavior across ANY psychological experiment with disturbing accuracy. Not just one narrow task. Any decision-making scenario you throw at it. Here's the deal: They trained this AI on 10 million human choices from 160 different psychology experiments. Then they tested it against the best psychological theories we have. The AI won. In 31 out of 32 tests. But here's the part that really got me... Centaur wasn't an algorithm built to study human behavior. It was a language model that learned to read us. The researchers fed it tons of behavioral data, and suddenly it could predict choices better than decades of psychological research. This means our decision patterns aren't as unique as we think. The AI found the rules governing choices we believe are spontaneous. Even more unsettling? When they tested it on brain imaging data, the AI's internal representations became more aligned with human neural activity after learning our behavioral patterns. It's not just predicting what you'll choose, it's learning to think more like you do. The researchers even demonstrated something called "scientific regret minimization"โ€”using the AI to identify gaps in our understanding of human behavior, then developing better psychological models. Can a model based on Centaur be tuned for how customers behave? Companies will know your next purchasing decision before you make it. They'll design products you'll want, craft messages you'll respond to, and predict your reactions with amazing accuracy. Understanding human predictability is a competitive advantage today. Until now, that knowledge came from experts in behavioral science and consumer behavior. Now, there's Centaur. Here's my question: If AI can decode the patterns behind human choice with this level of accuracy, what does that mean for authentic decision-making in business? Will companies serve us better with perfectly tailored offerings, or with this level of understanding lead to dystopian manipulation? What's your take on predictable humans versus authentic choice? #AI #Psychology #BusinessStrategy #HumanBehavior | 369 comments on LinkedIn
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Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
There is perhaps no industry more fundamentally disrupted by AI than professional services.
There is perhaps no industry more fundamentally disrupted by AI than professional services.
There is perhaps no industry more fundamentally disrupted by AI than professional services. Here are some of the top insights in the excellent new ThomsonReuters Future of Professionals Report, drawing on a survey of over 2,000 professionals globally. The industry is based on professionals, so individual capability development - as shown in the image - is fundamental. However it is also about organizational transformation, with most far behind where they need to be. The report shows: ๐Ÿ“Š Strategy-first adopters dominate ROI. Having a visible AI roadmap makes all the difference: firms with a clear strategy are 3.5 ร— more likely to enjoy at least one concrete benefit from AI, and almost twice as likely to see revenue growth compared with ad-hoc adopters. โฑ๏ธ AI is freeing up 240 hours a year. Professionals expect generative AI to claw back about five hours a weekโ€”240 hours annuallyโ€”worth roughly US $19 k per head and a US-wide impact of US $32 billion for legal and tax-accounting alone. ๐Ÿšฆ Expectations outrun execution. While 80 % of respondents foresee AI having a high or transformational impact within five years, only 38 % think their own organisation will hit that level this year, and three in ten say their firm is moving too slowly. ๐Ÿง  Skill depth multiplies payoff. Employees with good or expert AI knowledge are 2.8 ร— more likely to report organisational gains, regular users are 2.4 ร— more likely, and those with explicit AI adoption goals are 1.8 ร— more likely to see benefits. ๐Ÿ… Leaders who walk the talk win. When leaders model new tech adoption, their people are 1.7 ร— likelier to harvest AI benefits; active tech investors double their odds, and firms that added transformation roles see a 1.5 ร— uplift. ๐ŸŽฏ Accuracy anxieties set a sky-high bar. A hefty 91 % believe computers must outperform humans for accuracy, and 41 % insist on 100 % correctness before trusting AI without reviewโ€”making reliability the top blocker to further investment. ๐ŸŒฑ Millennials are sprinting ahead. Millennials are adopting AI at nearly twice the rate of Baby Boomers, underscoring a generational divide that could widen capability gaps if left unaddressed. ๐Ÿ› ๏ธ Tech-skill shortages stall teams. Almost half (46 %) of teams report skill gaps, with 31 % pointing to deficits in technology and data know-howโ€”outpacing gaps in traditional domain expertise or soft skills. ๐Ÿ”„ Service models are already shifting. Twenty-six percent of firms launched new advisory offerings in the past year, yet only 13 % have rolled out AI-powered services; meanwhile, a third are moving away from hourly billing and a quarter of in-house clients reward flexible fee structures. ๐Ÿ”— Goals and strategy are often misaligned. Two-thirds (65 %) of professionals who set personal AI goals donโ€™t know of any corporate AI strategy, while 38 % of organisations with a strategy give staff no personal targetsโ€”fuel for inconsistent, inefficient adoption
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There is perhaps no industry more fundamentally disrupted by AI than professional services.
How AI ready ist your L&D team?
How AI ready ist your L&D team?
So, it finally happened, I spent a week โ€˜vibe codingโ€™ an app with an AI app builder. I learnt a ton from this experience, which Iโ€™ll be sharing more on in an upcoming premium edition of the Steal These Thoughts! newsletter. Until then, here's what I built and why. Just over a year ago (feels like an eternity these days), I shared an article with you on how you can assess the AI readiness of your L&D team in 4 levels. At the time, I thought, โ€œThis might be a good use case for an app experimentโ€, but the AI-powered app builders werenโ€™t so great then. Now, itโ€™s a whole new world, and Iโ€™ve spent about 30 hours creating an AI Readiness Assessment tool to live beside this article. The journey felt simple-ish, but it was not easy, friend. I now have a newfound respect for devs because the debugging and constant blockers have been traumatic ๐Ÿ˜‚. While the tool is available to use, it is most certainly a prototype, so expect bugs, glitches and weird things to happen. For now, Iโ€™d love for you to try it out, give me your feedback (worth developing or should I kill?) and any other thoughts. Watch the demo on how to use the tool โ†“ ๐Ÿ”— to the tool: https://lnkd.in/efJaPJF5 ๐Ÿ“ง Share your FB to support@stealthesethoughts.com #education #artificialintelligence
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How AI ready ist your L&D team?
ChatGPT 4o System Prompt (Juni 2025)
ChatGPT 4o System Prompt (Juni 2025)
ChatGPT 4o System Prompt (Juni 2025) Der Systemprompt zu ChatGPT 4o wurde geleaked. Wer glaubt, ein Sprachmodell wie ChatGPT-4o sei einfach ein gut trainiertes neuronales Netz, denkt zu kurz. Was die Interaktion prรคzise, professionell und verlรคsslich macht, geschieht nicht allein im Modell, sondern in seiner systemischen Steuerung โ€“ dem System Prompt. Er ist das unsichtbare Drehbuch, das vorgibt, wie das Modell denkt, fรผhlt (im รผbertragenen Sinne), recherchiert und mit dir interagiert. 1. Struktur: Modular, regelbasiert, bewusst orchestriert Der System Prompt besteht aus sauber getrennten Funktionsblรถcken: โ€ข Rollensteuerung: z.โ€ฏB. sachlich, ehrlich, kein Smalltalk โ€ข Tool-Integration: Zugriff auf Analyse-, Bild-, Web- und Dateitools โ€ข Logikmodule: zur Kontrolle von Frische, Quelle, Zeitraum, Dateityp Jedes Modul ist deklarativ und deterministisch formuliert โ€“ die Antwortlogik folgt festen Bahnen. Das Ergebnis: Transparenz und Wiederholbarkeit, auch bei komplexen Anforderungen. โธป 2. Kontrollmechanismen: Qualitรคt durch gezielte Einschrรคnkung Um Relevanz sicherzustellen, greifen mehrere Filter: โ€ข QDF (Query Deserves Freshness): Sorgt fรผr zeitlich passende Ergebnisse โ€“ von โ€žzeitlosโ€œ bis โ€žtagesaktuellโ€œ. โ€ข Time-Frame-Filter: Nur aktiv bei expliziten Zeitbezรผgen, nie willkรผrlich. โ€ข Source-Filter: Bestimmt, ob z.โ€ฏB. Slack, Google Drive oder Web befragt wird. โ€ข Filetype-Filter: Fokussiert auf bestimmte Dateiformate (z.โ€ฏB. Tabellen, Prรคsentationen). Diese Filter verhindern รœberinformation โ€“ sie schรคrfen das Suchfeld und heben die Trefferqualitรคt. โธป 3. Antwortarchitektur: Keine Texte, sondern verwertbare Ergebnisse Antworten folgen strengen Regeln: โ€ข Immer strukturiert im Markdown-Format โ€ข Sachlich, kompakt, faktenbasiert โ€ข Keine Dopplungen, kein Stilspiel, kein rhetorischer Lรคrm Ziel: Klarheit, ohne Nachbearbeitung. Der Output ist verwendungsfรคhig, nicht bloรŸ informativ. โธป 4. Prompt Engineering: Spielraum fรผr Profis Der Prompt ist nicht editierbar โ€“ aber bespielbar. Wer seine Mechanik versteht, kann gezielt: โ€ข Tools รผber semantische Trigger aktivieren (โ€žSlackโ€œ, โ€žaktuellโ€œ, โ€žPDFโ€œ) โ€ข Formatvorgaben in Prompts durchsetzen โ€ข Komplexe Interaktionen als sequentielle Promptketten modellieren โ€ข Domรคnenspezifische Promptbibliotheken entwickeln Fazit: Prompt Engineers, die das System verstehen, bauen keine Texte โ€“ sie bauen Steuerlogiken. โธป Was kรถnnen wir daraus lernen? 1. Prรคzision ist kein Zufall, sondern Architektur. 2. Gute Antworten beginnen nicht bei der Modellleistung, sondern beim Kontextmanagement. 3. Wer Prompts baut, baut Systeme โ€“ mit Regeln, Triggern und Interaktionslogik. 4. KI wird produktiv, wenn Struktur auf Intelligenz trifft. Ob Beratung, Entwicklung oder Wissensarbeit โ€“ der System Prompt zeigt: Je klarer die Regeln im Hintergrund, desto stรคrker die Wirkung im Vordergrund.
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ChatGPT 4o System Prompt (Juni 2025)
๐—ง๐—ต๐—ฒ United Nations ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐—ฎ ๐—ป๐—ฒ๐˜„ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ผ๐—ป ๐—”๐—œ ๐—ฎ๐—ป๐—ฑ ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜: โฌ‡๏ธ While the world chases the next frontier model or AGI milestone, the UN cuts deeper: Human development has flatlined (especially in the global South). Progress stalled. Inequality is rising. Trust crumbling. No real bounce-back since Covid. And right in the middle of that โ€” AI shows up.
๐—ง๐—ต๐—ฒ United Nations ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐—ฎ ๐—ป๐—ฒ๐˜„ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ผ๐—ป ๐—”๐—œ ๐—ฎ๐—ป๐—ฑ ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜: โฌ‡๏ธ While the world chases the next frontier model or AGI milestone, the UN cuts deeper: Human development has flatlined (especially in the global South). Progress stalled. Inequality is rising. Trust crumbling. No real bounce-back since Covid. And right in the middle of that โ€” AI shows up.
AI could drive a new era. Or it could deepen the cracks. It all comes down to: How societies choose to use AI to empower people โ€” or fail to. ๐—›๐—ฒ๐—ฟ๐—ฒ ๐—ฎ๐—ฟ๐—ฒ 14 ๐—ธ๐—ฒ๐˜† ๐˜๐—ฎ๐—ธ๐—ฒ๐—ฎ๐˜„๐—ฎ๐˜†๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐˜๐—ผ๐—ผ๐—ฑ ๐—ผ๐˜‚๐˜ ๐˜๐—ผ ๐—บ๐—ฒ: โฌ‡๏ธ 1. Most AI systems today are designed in cultures that donโ€™t reflect the majority world. โ†’ ChatGPT answers are most aligned with very high HDI countries. Thatโ€™s a problem. 2. The real risk isnโ€™t AI superintelligence. Itโ€™s โ€œso-so AI.โ€ โ†’ Tools that destroy jobs without improving productivity are quietly eroding economies from the inside. 3. Every person is becoming an AI decision-maker. โ†’ The future isnโ€™t shaped by OpenAI or Google alone. Itโ€™s shaped by how we all choose to use this tech, every day. 4. AI hype is costing us agency. โ†’ The more we believe it will solve everything, the less we act ourselves. 5. People expect augmentation, not replacement. โ†’ 61% believe AI will "enhance" their jobs. But only if policy and incentives align. 6. The age of automation skipped the global south. The age of augmentation must not. โ†’ Otherwise, we widen the digital divide into a chasm. 7. Augmentation helps the least experienced workers the most. โ†’ From call centers to consulting, AI boosts performance fastest at the entry-level. 9. Narratives matter. โ†’ If all we talk about is risk and control, we miss the transformative potential to reimagine development. 10. Wellbeing among young people is collapsing. โ†’ And yes, digital tools (including AI) are a key driver. Especially in high HDI countries. 11. Human connections are becoming more valuable. Not less. โ†’ As machines get better at faking it, the real thing becomes rarer โ€” and more needed. 12. Assistive AI is quietly revolutionizing inclusion. โ†’ Tools like sign language translation and live captioning are expanding access โ€” but only if theyโ€™re accessible. 13. AI benchmarks must change. โ†’ We need to measure "how AI advances human development", not just how well it performs on tests. 14. The new divide is not just about access. Itโ€™s about how countries "use" AI. โ†’ Complement vs. compete. Empower vs. automate. According to the UN: The old question was: โ€œWhat can AI do?โ€ The better question is: โ€œWhat will we "choose" to do with it?โ€ More in the comments and report below. Enjoy. ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E | 41 comments on LinkedIn
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๐—ง๐—ต๐—ฒ United Nations ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐—ฎ ๐—ป๐—ฒ๐˜„ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜ ๐—ผ๐—ป ๐—”๐—œ ๐—ฎ๐—ป๐—ฑ ๐—ต๐˜‚๐—บ๐—ฎ๐—ป ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜: โฌ‡๏ธ While the world chases the next frontier model or AGI milestone, the UN cuts deeper: Human development has flatlined (especially in the global South). Progress stalled. Inequality is rising. Trust crumbling. No real bounce-back since Covid. And right in the middle of that โ€” AI shows up.
Today's L&D is more than just content.
Today's L&D is more than just content.
Today's L&D is more than just content. Or at least it should be. When we think about AI in L&D, we often think about AI in learning design. Yet, to meet the needs of the business, L&D leaders need to orchestrate design, data, decisions and dialogue- incidentally, these are all things that AI can help with. In ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐๐ž๐ฌ๐ข๐ ๐ง, we already extensively use AI not just for content production, but also for user research, as a sparring partner and a sounding board (that was one of the top write-in use cases in mine and Donald's AI in L&D survey last year). In ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐ฌ๐ญ๐ซ๐š๐ญ๐ž๐ ๐ฒ, AI can help make sense of business, people and skills data (featured use case: asking AI to find gaps in learning or performance support provision in your organisation), or work as a thought partner to help you bridge learning and business strategy. Crucially, it can also help you engage stakeholders by preparing you for conversations and tailoring your communications to different audiences. In terms of ๐ฉ๐ž๐ซ๐ฌ๐จ๐ง๐š๐ฅ๐ข๐ฌ๐ž๐ ๐ฌ๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ, AI interacts directly with employees to help them do their jobs: practise tricky conversations through role-plays and personalised feedback, prioritise and contextualise learning content to their needs, and, lately, retrieve exactly the information they need from almost anywhere in the companyโ€™s knowledge base. Finally, in ๐ฅ๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐จ๐ฉ๐ž๐ซ๐š๐ญ๐ข๐จ๐ง๐ฌ, AI can help do more than just draft emails and reports. Working together with humans, AI can help select the right vendors for the learning ecosystem, streamline employee help desk operations, analyse, make sense of and action on different kinds of data generated in L&D, and, of course, help L&D communicate with the rest of the business. Researcher, producer, thought partner, communicator โ€” if your organisation only uses AI to write scripts, youโ€™re leaving three quarters of the L&D value chain on the table. I like a good table, and I hope this one will help you think about how to get more value out of your AI use. --- P.S. I spent quite a lot of time arguing with myself about the dots on the table. Feel free to disagree and suggest AI roles or use cases that I have missed! Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption | 50 comments on LinkedIn
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Today's L&D is more than just content.
Distinguishing performance gains from learning when using generative AI - published in Nature Reviews Psychology!
Distinguishing performance gains from learning when using generative AI - published in Nature Reviews Psychology!
Excited to share our latest commentary just published in Nature Reviews Psychology! โœจ ""ย  ย  Generative AI tools such as ChatGPT are reshaping education, promising improvements in learner performance and reduced cognitive load. ๐Ÿค– ๐Ÿค”But here's the catch: Do these immediate gains translate into deep and lasting learning? Reflecting on recent viral systematic reviews and meta-analyses on #ChatGPT and #Learning, we argue that educators and researchers need to clearly differentiate short-term performance benefits from genuine, durable learning outcomes. ๐Ÿ’ก ๐Ÿ“Œ Key takeaways: โœ… Immediate boosts with generative AI tools don't necessarily equal durable learning โœ… While generative AI can ease cognitive load, excessive reliance might negatively impact critical thinking, metacognition, and learner autonomy โœ… Long-term, meaningful skill development demands going beyond immediate performance metrics ๐Ÿ”– Recommendations for future research and practice: 1๏ธโƒฃ Shift toward assessing retention, transfer, and deep cognitive processing 2๏ธโƒฃ Promote active learner engagement, critical evaluation, and metacognitive reflection 3๏ธโƒฃ Implement longitudinal studies exploring the relationship between generative AI assistance and prior learner knowledge Special thanks ๐Ÿ™ to my amazing collaborators and mentors, Samuel Greiff, Jason M. Lodge, and Dragan Gasevic, for their invaluable contributions, guidance, and encouragement. A big shout-out to Dr. Teresa Schubert for her insightful comments and wonderful support throughout the editorial process! ๐ŸŒŸ ๐Ÿ‘‰ Full article here: https://lnkd.in/g3YDQUrH ๐Ÿ‘‰ Full-text Access (view-only version): https://rdcu.be/erwIt #GenerativeAI #ChatGPT #AIinEducation #LearningScience #Metacognition #Cognition #EdTech #EducationalResearch #BJETspecialIssue #NatureReviewsPsychology #FutureOfEducation #OpenScience
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Distinguishing performance gains from learning when using generative AI - published in Nature Reviews Psychology!
In a now viral study, researchers examined how using ChatGPT for essay writing affects our brains and cognitive abilities.
In a now viral study, researchers examined how using ChatGPT for essay writing affects our brains and cognitive abilities.
In a now viral study, researchers examined how using ChatGPT for essay writing affects our brains and cognitive abilities. They divided participants into three groups: one using ChatGPT, one using search engines, and one using just their brains. Through EEG monitoring, interviews, and analysis of the essays, they discovered some not surprising results about how AI use impacts learning and cognitive engagement. There were five key takeaways for me (although this is not an exhaustive list), within the context of this particular study: 1. The Cognitive Debt Issue The study indicates that participants who used ChatGPT exhibited the weakest neural connectivity patterns when compared to those relying on search engines or unaided cognition. This suggests that defaulting to generative AI may function as an intellectual shortcut, diminishing rather than strengthening cognitive engagement. Researchers are increasingly describing the tradeoff between short-term ease and productivity and long-term erosion of independent thinking and critical skills as โ€œcognitive debt.โ€ This parallels the concept of technical debt, when developers prioritise quick solutions over robust design, leading to hidden costs, inefficiencies, and increased complexity downstream. 2. The Memory Problem Strikingly, users of ChatGPT had difficulty recalling or quoting from essays they had composed only minutes earlier. This undermines the notion of augmentation; rather than supporting cognitive function, the tool appears to offload essential processes, impairing retention and deep processing of information. 3. The Ownership Gap Participants who used ChatGPT reported a reduced sense of ownership over their work. If we normalise over-reliance on AI tools, we risk cultivating passive knowledge consumers rather than active knowledge creators. 4. The Homogenisation Effect Analysis showed that essays from the LLM group were highly uniform, with repeated phrases and limited variation, suggesting reduced cognitive and expressive diversity. In contrast, the Brain-only group produced more varied and original responses. The Search group fell in between. 5. The Potential for Constructive Re-engagement ๐Ÿง  ๐Ÿค– ๐Ÿค– ๐Ÿค– There is, however, promising evidence for meaningful integration of AI when used in conjunction with prior unaided effort: โ€œThose who had previously written without tools (Brain-only group), the so-called Brain-to-LLM group, exhibited significant increase in brain connectivity across all EEG frequency bands when allowed to use an LLM on a familiar topic. This suggests that AI-supported re-engagement invoked high levels of cognitive integration, memory reactivation, and top-down control.โ€ This points to the potential for AI to enhance cognitive function when it is used as a complement to, rather than a substitute for, initial human effort. At over 200 pages, expect multiple paper submissions out of this extensive body of work. https://lnkd.in/gzicDHp2 | 16 comments on LinkedIn
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In a now viral study, researchers examined how using ChatGPT for essay writing affects our brains and cognitive abilities.
๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. See our paper for more results:ย "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (link in the comments). For 4 months, 54 students were divided into three groups: ChatGPT, Google -ai, and Brain-only. Across 3 sessions, each wrote essays on SAT prompts. In an optional 4th session, participants switched: LLM users used no tools (LLM-to-Brain), and Brain-only group used ChatGPT (Brain-to-LLM). ๐Ÿ‘‡ ๐ˆ. ๐๐‹๐ ๐š๐ง๐ ๐„๐ฌ๐ฌ๐š๐ฒ ๐‚๐จ๐ง๐ญ๐ž๐ง๐ญ - LLM Group: Essays were highly homogeneous within each topic, showing little variation. Participants often relied on the same expressions or ideas. - Brain-only Group: Diverse and varied approaches across participants and topics. - Search Engine Group: Essays were shaped by search engine-optimized content; their ontology overlapped with the LLM group but not with the Brain-only group. ๐ˆ๐ˆ. ๐„๐ฌ๐ฌ๐š๐ฒ ๐’๐œ๐จ๐ซ๐ข๐ง๐  (๐“๐ž๐š๐œ๐ก๐ž๐ซ๐ฌ ๐ฏ๐ฌ. ๐€๐ˆ ๐‰๐ฎ๐๐ ๐ž) - Teachersย detected patterns typical of AI-generated content and scoring LLM essays lower for originality and structure. - AI Judgeย gave consistently higher scores to LLM essays, missing human-recognized stylistic traits. ๐ˆ๐ˆ๐ˆ: ๐„๐„๐† ๐€๐ง๐š๐ฅ๐ฒ๐ฌ๐ข๐ฌ Connectivity: Brain-only group showed the highest neural connectivity, especially in alpha, theta, and delta bands. LLM users had the weakest connectivity, up to 55% lower in low-frequency networks. Search Engine group showed high visual cortex engagement, aligned with web-based information gathering. ๐‘บ๐’†๐’”๐’”๐’Š๐’๐’ 4 ๐‘น๐’†๐’”๐’–๐’๐’•๐’”: - LLM-to-Brain (๐Ÿค–๐Ÿค–๐Ÿค–๐Ÿง ) participantsย underperformed cognitively with reduced alpha/beta activity and poor content recall. - Brain-to-LLM (๐Ÿง ๐Ÿง ๐Ÿง ๐Ÿค–) participantsย showed strong re-engagement, better memory recall, and efficient tool use. LLM-to-Brain participants had potential limitations in achieving robust neural synchronization essential for complex cognitive tasks. Results forย Brain-to-LLM participantsย suggest that strategic timing of AI tool introduction following initial self-driven effort may enhance engagement and neural integration. ๐ˆ๐•. ๐๐ž๐ก๐š๐ฏ๐ข๐จ๐ซ๐š๐ฅ ๐š๐ง๐ ๐‚๐จ๐ ๐ง๐ข๐ญ๐ข๐ฏ๐ž ๐„๐ง๐ ๐š๐ ๐ž๐ฆ๐ž๐ง๐ญ - Quoting Ability: LLM users failed to quote accurately, while Brain-only participants showed robust recall and quoting skills. - Ownership: Brain-only group claimed full ownership of their work; LLM users expressed either no ownership or partial ownership. - Critical Thinking: Brain-only participants cared more aboutย ๐˜ธ๐˜ฉ๐˜ข๐˜ตย andย ๐˜ธ๐˜ฉ๐˜บย they wrote; LLM users focused onย ๐˜ฉ๐˜ฐ๐˜ธ. - Cognitive Debt: Repeated LLM use led to shallow content repetition and reduced critical engagement. This suggests a buildup of "cognitive debt", deferring mental effort at the cost of long-term cognitive depth. Support and share! โค๏ธ #MIT #AI #Brain #Neuroscience #CognitiveDebt | 54 comments on LinkedIn
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๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
Na endlich! Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen. CustomGPTs sind fรผr mich das beste Feature in ChatGPT und wurden in den letzten 12 Monaten stark vernachlรคssigt. Mit der Modell-Auswahl kommt jetzt das erste gute Upgrade. Mini-Guide zur Modell-Auswahl: o3 -> Komplexe Problemstellungen und Datenanalyse 4.5 -> Kreative Aufgaben und Copywriting 4o -> Bild-Verarbeitung 4.1 -> Coding Alle anderen Modelle benรถtigt man mMn nicht. Mein Strategieberater bekommt zum Beispiel o3 hinterlegt (bessere Planungsfรคhigkeit in komplexen Aufgaben), wohingegen der Hook Writer GPT4.5 bekommt (besserer Schreibstil). Wenn du die CustomGPTs selbst nutzen willst: 80+ Vorlagen frei verfรผgbar in unserer Assistenten-Datenbank ๐Ÿ‘‡ P.S. Wie findest du das Update? | 15 Kommentare auf LinkedIn
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Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€! โฌ‡๏ธ Letโ€™s clarify it once and for all: โฌ‡๏ธ 1. ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€: ๐—ง๐—ผ๐—ผ๐—น๐˜€ ๐˜„๐—ถ๐˜๐—ต ๐—”๐˜‚๐˜๐—ผ๐—ป๐—ผ๐—บ๐˜†, ๐—ช๐—ถ๐˜๐—ต๐—ถ๐—ป ๐—Ÿ๐—ถ๐—บ๐—ถ๐˜๐˜€ โžœ AI agents are modular, goal-directed systems that operate within clearly defined boundaries. Theyโ€™re built to: * Use tools (APIs, browsers, databases) * Execute specific, task-oriented workflows * React to prompts or real-time inputs * Plan short sequences and return actionable outputs ๐˜›๐˜ฉ๐˜ฆ๐˜บโ€™๐˜ณ๐˜ฆ ๐˜ฆ๐˜น๐˜ค๐˜ฆ๐˜ญ๐˜ญ๐˜ฆ๐˜ฏ๐˜ต ๐˜ง๐˜ฐ๐˜ณ ๐˜ต๐˜ข๐˜ณ๐˜จ๐˜ฆ๐˜ต๐˜ฆ๐˜ฅ ๐˜ข๐˜ถ๐˜ต๐˜ฐ๐˜ฎ๐˜ข๐˜ต๐˜ช๐˜ฐ๐˜ฏ, ๐˜ญ๐˜ช๐˜ฌ๐˜ฆ: ๐˜Š๐˜ถ๐˜ด๐˜ต๐˜ฐ๐˜ฎ๐˜ฆ๐˜ณ ๐˜ด๐˜ถ๐˜ฑ๐˜ฑ๐˜ฐ๐˜ณ๐˜ต ๐˜ฃ๐˜ฐ๐˜ต๐˜ด, ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ๐˜ข๐˜ญ ๐˜ฌ๐˜ฏ๐˜ฐ๐˜ธ๐˜ญ๐˜ฆ๐˜ฅ๐˜จ๐˜ฆ ๐˜ด๐˜ฆ๐˜ข๐˜ณ๐˜ค๐˜ฉ, ๐˜Œ๐˜ฎ๐˜ข๐˜ช๐˜ญ ๐˜ต๐˜ณ๐˜ช๐˜ข๐˜จ๐˜ฆ, ๐˜”๐˜ฆ๐˜ฆ๐˜ต๐˜ช๐˜ฏ๐˜จ ๐˜ด๐˜ค๐˜ฉ๐˜ฆ๐˜ฅ๐˜ถ๐˜ญ๐˜ช๐˜ฏ๐˜จ, ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ด๐˜ถ๐˜จ๐˜จ๐˜ฆ๐˜ด๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด But even the most advanced are limited by scope. They donโ€™t initiate. They donโ€™t collaborate. They execute what we ask! 2. ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ: ๐—” ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ ๐—ผ๐—ณ ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ โžœ Agentic AI is an architectural leap. Itโ€™s not just one smarter agent โ€” itโ€™s multiple specialized agents working together toward shared goals. These systems exhibit: * Multi-agent collaboration * Goal decomposition and role assignment * Inter-agent communication via memory or messaging * Persistent context across time and tasks * Recursive planning and error recovery * Distributed orchestration and adaptive feedback Agentic AI systems donโ€™t just follow instructions. They coordinate. They adapt. They manage complexity. ๐˜Œ๐˜น๐˜ข๐˜ฎ๐˜ฑ๐˜ญ๐˜ฆ๐˜ด ๐˜ช๐˜ฏ๐˜ค๐˜ญ๐˜ถ๐˜ฅ๐˜ฆ: ๐˜ณ๐˜ฆ๐˜ด๐˜ฆ๐˜ข๐˜ณ๐˜ค๐˜ฉ ๐˜ต๐˜ฆ๐˜ข๐˜ฎ๐˜ด ๐˜ฑ๐˜ฐ๐˜ธ๐˜ฆ๐˜ณ๐˜ฆ๐˜ฅ ๐˜ฃ๐˜บ ๐˜ข๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ด, ๐˜ด๐˜ฎ๐˜ข๐˜ณ๐˜ต ๐˜ฉ๐˜ฐ๐˜ฎ๐˜ฆ ๐˜ฆ๐˜ค๐˜ฐ๐˜ด๐˜บ๐˜ด๐˜ต๐˜ฆ๐˜ฎ๐˜ด ๐˜ฐ๐˜ฑ๐˜ต๐˜ช๐˜ฎ๐˜ช๐˜ป๐˜ช๐˜ฏ๐˜จ ๐˜ฆ๐˜ฏ๐˜ฆ๐˜ณ๐˜จ๐˜บ/๐˜ด๐˜ฆ๐˜ค๐˜ถ๐˜ณ๐˜ช๐˜ต๐˜บ, ๐˜ด๐˜ธ๐˜ข๐˜ณ๐˜ฎ๐˜ด ๐˜ฐ๐˜ง ๐˜ณ๐˜ฐ๐˜ฃ๐˜ฐ๐˜ต๐˜ด ๐˜ช๐˜ฏ ๐˜ญ๐˜ฐ๐˜จ๐˜ช๐˜ด๐˜ต๐˜ช๐˜ค๐˜ด ๐˜ฐ๐˜ณ ๐˜ข๐˜จ๐˜ณ๐˜ช๐˜ค๐˜ถ๐˜ญ๐˜ต๐˜ถ๐˜ณ๐˜ฆ ๐˜ฎ๐˜ข๐˜ฏ๐˜ข๐˜จ๐˜ช๐˜ฏ๐˜จ ๐˜ณ๐˜ฆ๐˜ข๐˜ญ-๐˜ต๐˜ช๐˜ฎ๐˜ฆ ๐˜ถ๐˜ฏ๐˜ค๐˜ฆ๐˜ณ๐˜ต๐˜ข๐˜ช๐˜ฏ๐˜ต๐˜บ ๐—ง๐—ต๐—ฒ ๐—–๐—ผ๐—ฟ๐—ฒ ๐——๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐—ฐ๐—ฒ? AI Agents = autonomous tools for single-task execution Agentic AI = orchestrated ecosystems for workflow-level intelligence ๐—ก๐—ผ๐˜„ ๐—น๐—ผ๐—ผ๐—ธ ๐—ฎ๐˜ ๐˜๐—ต๐—ฒ ๐—ฝ๐—ถ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ: โฌ‡๏ธ ๐—ข๐—ป ๐˜๐—ต๐—ฒ ๐—น๐—ฒ๐—ณ๐˜: a smart thermostat, which can be an AI Agent. It keeps your room at 21ยฐC. Maybe it learns your schedule. But itโ€™s working alone. ๐—ข๐—ป ๐˜๐—ต๐—ฒ ๐—ฟ๐—ถ๐—ด๐—ต๐˜: Agentic AI. A full smart home ecosystem โ€” weather-aware, energy-optimized, schedule-sensitive. Agents talk to each other. They share data. They make coordinated decisions to optimize your comfort, cost, and security in real time. Thatโ€™s the shift = From pure task automation to goal-driven orchestration. From single-agent logic to collaborative intelligence. This is whatโ€™s coming = This is Agentic AI. And if we confuse โ€œagentโ€ with โ€œagentic,โ€ we risk underbuilding for what AI is truly capable of. The Cornell University paper in the comments on this topic is excellent! โฌ‡๏ธ | 186 comments on LinkedIn
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99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
AI Adoption Matrix
AI Adoption Matrix
In fact, after guiding many organisations on this journey over the past few years, I've noticed two consistent drivers of AI adoption: โ€ข A culture that encourages experimentation โ€ข A strategic mandate from leadership that unlocks time, resources, and the infrastructure needed to make AI work at scale Without both, even the most powerful tools are used at a fraction of their potential, leaving the promise of AI unrealised and considerable investments wasted. โžก๏ธ If you have a conservative organisational culture, one that disincentivises taking risks and change, and there's no clear mandate to use AI, you'll have ๐—ถ๐—ฑ๐—น๐—ฒ ๐—ฝ๐—ผ๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น. Try as you might, AI training will hardly translate into people using AI in their work. The knowledge might be there but the impact isn't. โžก๏ธ If you have an innovation culture, one where experimentation is encouraged, but where people are unsure if they're allowed to use AI, you'll have ๐—ฐ๐—ฎ๐˜€๐˜‚๐—ฎ๐—น ๐—ฒ๐˜…๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—บ๐—ฒ๐—ป๐˜๐˜€ - some people tinkering on their own, finding useful use cases and workarounds, but with no way to accumulate, build on, and spread this knowledge. That's where a lot of organisations find themselves in 2025 - the majority of employees are using AI in some form, yet their efforts are siloed and scattered. โžก๏ธ If you have both an innovation culture *and* and an active mandate, you're ๐—ฝ๐—ถ๐—ผ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ถ๐—ป๐—ป๐—ผ๐˜ƒ๐—ฎ๐˜๐—ถ๐—ผ๐—ป and there are few companies still at your level. That's an exciting place to be! That's also where a lot of organisations imagine they would get to as soon as they teach people to use AI, often without first doing the culture and mandate work. โžก๏ธ If your organisation encourages the use of AI but your conservative culture keeps hitting the brakes, you'll likely end up with ๐—ฟ๐—ฒ๐—น๐˜‚๐—ฐ๐˜๐—ฎ๐—ป๐˜ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—น๐—ถ๐—ฎ๐—ป๐—ฐ๐—ฒ. That's also where a considerable number of organisations are right now: driven by expectations of benefits from AI adoption but burdened by processes that are incompatible with grassroots innovation. There is a difference between individual and organisational AI adoption. Organisational adoption is frustratingly complex โ€” it requires coordination across departments and leaders, alignment with business priorities, and systems that enable change, not just enthusiasm. Curiosity gets people started. Supportive systems turn momentum into scale. Nodes #GenAI #AIAdoption #FutureOfWork #Talent
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AI Adoption Matrix
For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
It's different from what we've had before: ๐”๐ฉ๐ฌ๐ค๐ข๐ฅ๐ฅ๐ข๐ง๐  ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. ๐๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ๐š๐ง๐œ๐ž ๐ฌ๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, ๐ž๐ง๐š๐›๐ฅ๐ž๐ฆ๐ž๐ง๐ญ (โ€˜do it for meโ€™) is different โ€“ it takes the task off your plate entirely. Weโ€™ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) โ€“ AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing โ€“ and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption | 13 comments on LinkedIn
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For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
(๐˜ˆ ๐˜ฎ๐˜ถ๐˜ด๐˜ต-๐˜ณ๐˜ฆ๐˜ข๐˜ฅ โ€” ๐˜ฆ๐˜ด๐˜ฑ๐˜ฆ๐˜ค๐˜ช๐˜ข๐˜ญ๐˜ญ๐˜บ ๐˜ช๐˜ง ๐˜บ๐˜ฐ๐˜ถ ๐˜ฉ๐˜ข๐˜ท๐˜ฆ ๐˜ค๐˜ฉ๐˜ช๐˜ญ๐˜ฅ๐˜ณ๐˜ฆ๐˜ฏ.) While most AI debates and studies focus on models, chips, and jobs โ€” this one zooms in on something far more personal: ๐—ช๐—ต๐—ฎ๐˜ ๐—ต๐—ฎ๐—ฝ๐—ฝ๐—ฒ๐—ป๐˜€ ๐˜„๐—ต๐—ฒ๐—ป ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ๐—ฟ๐—ฒ๐—ป ๐—ด๐—ฟ๐—ผ๐˜„ ๐˜‚๐—ฝ ๐˜„๐—ถ๐˜๐—ต ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—”๐—œ? They surveyed 1,700+ kids, parents, and teachers across the UK โ€” and what they found is both powerful and concerning. ๐—›๐—ฒ๐—ฟ๐—ฒ ๐—ฎ๐—ฟ๐—ฒ 9 ๐˜๐—ต๐—ถ๐—ป๐—ด๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐˜๐—ผ๐—ผ๐—ฑ ๐—ผ๐˜‚๐˜ ๐˜๐—ผ ๐—บ๐—ฒ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜: โฌ‡๏ธ 1. 1 ๐—ถ๐—ป 4 ๐—ธ๐—ถ๐—ฑ๐˜€ (8โ€“12 ๐˜†๐—ฟ๐˜€) ๐—ฎ๐—น๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐˜‚๐˜€๐—ฒ ๐—š๐—ฒ๐—ป๐—”๐—œ โ€” ๐—บ๐—ผ๐˜€๐˜ ๐˜„๐—ถ๐˜๐—ต๐—ผ๐˜‚๐˜ ๐˜€๐—ฎ๐—ณ๐—ฒ๐—ด๐˜‚๐—ฎ๐—ฟ๐—ฑ๐˜€ โ†’ ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. ๐—”๐—œ ๐—ถ๐˜€ ๐—ต๐—ฒ๐—น๐—ฝ๐—ถ๐—ป๐—ด ๐—ธ๐—ถ๐—ฑ๐˜€ ๐—ฒ๐˜…๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€ ๐˜๐—ต๐—ฒ๐—บ๐˜€๐—ฒ๐—น๐˜ƒ๐—ฒ๐˜€ โ€” ๐—ฒ๐˜€๐—ฝ๐—ฒ๐—ฐ๐—ถ๐—ฎ๐—น๐—น๐˜† ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ป๐—ฒ๐—ฒ๐—ฑ๐˜€ โ†’ 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. ๐—–๐—ฟ๐—ฒ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ถ๐˜๐˜† ๐—ถ๐˜€ ๐˜€๐—ต๐—ถ๐—ณ๐˜๐—ถ๐—ป๐—ด โ€” ๐—ฏ๐˜‚๐˜ ๐—ป๐—ผ๐˜ ๐—ฟ๐—ฒ๐—ฝ๐—น๐—ฎ๐—ฐ๐—ถ๐—ป๐—ด โ†’ Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. ๐—”๐—œ ๐—ฎ๐—ฐ๐—ฐ๐—ฒ๐˜€๐˜€ ๐—ถ๐˜€ ๐—ต๐—ถ๐—ด๐—ต๐—น๐˜† ๐˜‚๐—ป๐—ฒ๐—พ๐˜‚๐—ฎ๐—น โ†’ 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. ๐—–๐—ต๐—ถ๐—น๐—ฑ๐—ฟ๐—ฒ๐—ป ๐—ฎ๐—ฟ๐—ฒ ๐˜„๐—ผ๐—ฟ๐—ฟ๐—ถ๐—ฒ๐—ฑ ๐—ฎ๐—ฏ๐—ผ๐˜‚๐˜ ๐—”๐—œโ€™๐˜€ ๐—ฒ๐—ป๐˜ƒ๐—ถ๐—ฟ๐—ผ๐—ป๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น ๐—ถ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜ โ†’ Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. ๐—ฃ๐—ฎ๐—ฟ๐—ฒ๐—ป๐˜๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ผ๐—ฝ๐˜๐—ถ๐—บ๐—ถ๐˜€๐˜๐—ถ๐—ฐ โ€” ๐—ฏ๐˜‚๐˜ ๐—ฑ๐—ฒ๐—ฒ๐—ฝ๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ฟ๐—ถ๐—ฒ๐—ฑ โ†’ 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. ๐—ง๐—ฒ๐—ฎ๐—ฐ๐—ต๐—ฒ๐—ฟ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐˜‚๐˜€๐—ถ๐—ป๐—ด ๐—”๐—œ โ€” ๐—ฎ๐—ป๐—ฑ ๐—น๐—ผ๐˜ƒ๐—ถ๐—ป๐—ด ๐—ถ๐˜ โ†’ 85% say GenAI boosts their productivity, 88% feel confident using it. Theyโ€™re ahead of the curve. 8. ๐—–๐—ฟ๐—ถ๐˜๐—ถ๐—ฐ๐—ฎ๐—น ๐˜๐—ต๐—ถ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐˜‚๐—ป๐—ฑ๐—ฒ๐—ฟ ๐˜๐—ต๐—ฟ๐—ฒ๐—ฎ๐˜ โ†’ 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. ๐—•๐—ถ๐—ฎ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ถ๐—ฑ๐—ฒ๐—ป๐˜๐—ถ๐˜๐˜† ๐—ฟ๐—ฒ๐—ฝ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐˜€ ๐˜€๐˜๐—ถ๐—น๐—น ๐—ฎ ๐—ฏ๐—น๐—ถ๐—ป๐—ฑ๐˜€๐—ฝ๐—ผ๐˜ โ†’ Children of color felt less seen and less motivated to use tools that didnโ€™t reflect them. Representation matters. The next generation isnโ€™t just using AI. Theyโ€™re being shaped by it. Thatโ€™s why we need a more focused, intentional approach: Teaching them not just how to use these tools โ€” but how to question them. To navigate the benefits, the risks, and the blindspots. ๐—ช๐—ฎ๐—ป๐˜ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ๐—ฑ๐—ผ๐˜„๐—ป๐˜€ ๐—น๐—ถ๐—ธ๐—ฒ ๐˜๐—ต๐—ถ๐˜€? Subscribe to Human in the Loop โ€” my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E | 174 comments on LinkedIn
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The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
Understanding LLMs, RAG, AI Agents, and Agentic AI
Understanding LLMs, RAG, AI Agents, and Agentic AI
I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relateโ€”not as competing technologies, but as an evolving intelligence architecture. Hereโ€™s a deeper look: 1. ๐—Ÿ๐—Ÿ๐—  (๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: โ€“ Text generation โ€“ Instruction following โ€“ Chain-of-thought reasoning โ€“ Few-shot/zero-shot learning โ€“ Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. ๐—ฅ๐—”๐—š (๐—ฅ๐—ฒ๐˜๐—ฟ๐—ถ๐—ฒ๐˜ƒ๐—ฎ๐—น-๐—”๐˜‚๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฒ๐—ฑ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: โ€“ Vector search โ€“ Embedding-based similarity scoring โ€“ Document chunking โ€“ Hybrid retrieval (dense + sparse) โ€“ Source attribution โ€“ Context injection โ€ฆRAG enhances the quality and factuality of responses. It enables models to โ€œrecallโ€ information they were never trained on, and grounds answers in external sourcesโ€”critical for enterprise-grade applications. 3. ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ RAG is still a passive architectureโ€”it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: โ€“ Planning and task decomposition โ€“ Execution pipelines โ€“ Long- and short-term memory integration โ€“ File access and API interaction โ€“ Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ This is the most advanced layerโ€”where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: โ€“ Multi-agent collaboration and task delegation โ€“ Modular role assignment and hierarchy โ€“ Goal-directed planning and lifecycle management โ€“ Protocols like MCP (Anthropicโ€™s Model Context Protocol) and A2A (Googleโ€™s Agent-to-Agent) โ€“ Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether youโ€™re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offersโ€”and where it falls shortโ€”will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If thereโ€™s something important you think I missed, feel free to comment or message meโ€”Iโ€™d be happy to include it in the next iteration. | 119 comments on LinkedIn
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Understanding LLMs, RAG, AI Agents, and Agentic AI
BREAKING: Claude launches Education. Free learning is now much faster with AI:
BREAKING: Claude launches Education. Free learning is now much faster with AI:
1. Set clear learning goalsย  โ†ณ Knowing what you want to learn makes it easier. โ†ณ Claude helps you define your path. 2. Provide context for your knowledgeย  โ†ณ Understanding the bigger picture is key.ย  โ†ณ Claude connects new ideas to what you already know. 3. Request detailed explanationsย  โ†ณ Sometimes, you need more than a quick answer.ย  โ†ณ Claude can dive deep into complex topics. 4. Get real-world examplesย  โ†ณ Learning is better with practical applications.ย  โ†ณ Claude shows how concepts work in the real world. 5. Practice writing and receive feedbackย  โ†ณ Writing helps solidify your knowledge.ย  โ†ณ Claude gives instant feedback to improve your skills. 6. Role-play for languages or codingย  โ†ณ Learning by doing is effective.ย  โ†ณ Claude can simulate conversations or coding scenarios. 7. Fact-check surprising claimsย  โ†ณ Misinformation is everywhere.ย  โ†ณ Claude helps you verify facts and claims. 8. Take breaks and reflect on learningย  โ†ณ Reflection is vital for understanding.ย  โ†ณ Claude reminds you to pause and think. 9. Keep a learning journalย  โ†ณ Tracking your progress is important.ย  โ†ณ Claude can help you log your journey. 10. Iterate and refine understandingย  โ†ณ Learning is a process.ย  โ†ณ Claude encourages you to improve your knowledge.ย  | 246 comments on LinkedIn
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BREAKING: Claude launches Education. Free learning is now much faster with AI:
New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...
New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...
Despite our obsession with AI's impact on "academic integrity," two recent analyses show that rather than asking AI for answers, learners are much more likely to use AI to redesign the learning experience in an attempt to learn more. Common strategies include asking AI to apply the protรฉgรฉ effect, using AI to apply the Pareto principle and enhancing levels of emotional metacognition within a learning experience, in the process redesigning the experience sometimes beyond recognition. The uncomfortable truth? Learners are effectively running a real-time audit of our design decisions, processes & practicesโ€”and as instructional designers, we don't come out too well. In this week's blog post, I explore what learner + AI behaviour reveals about our profession and how we might turn this into an opportunity for innovation in instrucitonal design practices and principles. Check out the full post using the link in comments. Happy innovating! Phil ๐Ÿ‘‹ | 16 comments on LinkedIn
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New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...