Open New Learning Lab Resources

We’re at a turning point. AI can generate faster, broader, cheaper content than ever. So the real differentiator isn’t knowledge, it’s experience design that actually influences behaviour and builds people's capability. And that’s tough. Because designing for experience means starting with the challenges people face, not topics or content. It means accepting that 'learning' doesn’t always feel good, in fact, it’s often the sting of experience that actually drives change. It’s layered too. Like an onion. The micro layer: our senses and emotions The meso layer: the interactions and activities we’re part of The macro layer: the strategic shifts in thinking and behaviour Some of the best experiences are completely invisible. Others stop us in our tracks and change us forever. But they’re rarely a one-off event. The most powerful ones are embedded in how we work, not added on after the fact. So let’s stop trying to make learning cute or entertaining (ie the Disneyfication effect). Let’s stop pretending every experience needs to feel good. Be honest. Build what’s real. And what actually makes a difference. Because if AI owns the content, then we’ve got to own the context. This means suba diving (see previous post..it will make sense, i promise), not snorkelling. If you’re interested in learning design that genuinely supports performance and growth, feel free to get in touch. As many know, I love talking about it.

Berufswahl im Zeitalter der lernenden Maschinen – Offener Brief an meine Nichte (Abi-Jahrgang 2025) Liebe Anna, als du mich fragtest, ob „Informatik, Medienwissenschaft oder Politik“ noch zukunftssicher sind, merkte ich, wie löchrig die alte Landkarte der Arbeit geworden ist. Code wird von KI vervollständigt, Diagnosen von Algorithmen unterstützt, Routineverträge von Bots geprüft. Laut Weltwirtschaftsforum wird bis 2030 fast jede zweite Kompetenz umgeschrieben. Was also studieren? Meine Empfehlung: Drei Felder, die weniger vom Titel als vom Skill-Mix leben. Warum? Weil sie Eigenschaften bieten, die KI kaum kopieren kann: direkten Menschenkontakt, interdisziplinäres Denken und sinnliche Materialerfahrung. Sie bilden zusammen einen „Human Moat“ – einen Schutzwall gegen reine Automatisierung. 1 | HEALTH & HUMAN SERVICES – BERUFE MIT EMPATHIE-FAKTOR Das ist erwartbar: Pflege, Sozialarbeit, Therapie oder Pädagogik bleiben knapp, weil Demografie und Krisen Resilienz verlangen. Typische Rollen: Pflegefachfrau+, Physician Assistant, Tele-Coach Mental Health. Schlüssel-Skills: evidenzbasierte Pflege, interkulturelle Kommunikation, Basiswissen Medizinrecht und Datenschutz. 2 | TWIN-TRANSITION CAREERS – KLIMA × TECHNOLOGIE Smarte Mash-Ups: Unternehmen brauchen Talente, die CO₂-Reduktion mit Datenkompetenz verbinden. Typische Rollen: Nachhaltigkeits-Data-Analyst, Circular-Economy-Ingenieurin, KI-Policy-Analyst, Energy-Systems-Modeler. Schlüssel-Skills: Life-Cycle-Assessment, Python/R, EU-Regulatorik (CSRD, AI Act), Systemdenken. 3 | CRAFT & EXPERIENCE DESIGN – WERT DES EINZIGARTIGEN Je perfekter Massenware KI-optimiert ist, desto höher steigt der Wert des Nicht-Skalierbaren. Typische Rollen: Produktdesigner*in für Bio-Materialien, Restaurator, Schreinerin mit CNC-Know-how, UX-Designer für phygitale Erlebnisse. Schlüssel-Skills: Materialkunde, CAD/CAM & 3-D-Druck, Storytelling, Customer-Journey-Mapping. Das ist natürlich nur ein Ausschnitt. Aber ich denke, die Muster dahinter sind klar, um es selbst weiterzudenken. WAS VERSCHWINDET? Alles, was rein repetitiv ist: Standard-Reporting, einfache Software-Tests, seitenlange Vertragsprüfungen. Die Maschine erledigt es schneller und billiger – doch jemand muss die Systeme entwerfen, mit Daten füttern und ethisch beaufsichtigen. MEIN RAT IN DREI SÄTZEN >> Suche kein Joblabel, sondern ein Problem, das dich elektrisiert. << Kombiniere digitale Grundfitness, empathische Kommunikation und moralischen Kompass. Dann arbeitest du nicht gegen Maschinen, sondern mit ihnen – und kannst dir jederzeit einen neuen Beruf erfinden. Vielleicht startest du als Pflege-Informatikerin, wirst später KI-Ethikerin und eröffnest irgendwann eine Bäckerei, in der Roboter den Teig kneten, während du den Sauerteig fütterst und Kund:innen berätst. Zukunftssicherheit entsteht nicht aus einem Studium, sondern aus lebenslanger Lernlust. Die Welt bleibt turbulent, doch wer Richtung Sinn steuert, hat immer Rückenwind. Dein Onkel Stefan | 59 Kommentare auf LinkedIn

👉 Prompt-Sammlung: https://simon-eckert.com/prompt-sammlung/👉 Bindungsstil-Selbsttest: https://simon-eckert.com/bindung-selbsttest/-- Notfallkontakte --Tel...

My site: https://natebjones.com/My links: https://linktr.ee/natebjonesMy substack: https://natesnewsletter.substack.com/Takeaways 1. Software 3.0 Paradigm Sh...

An eagerness to learn is essential for innovation. But the way we learn—and the order in which we partake in various learning activities—can make the difference between effective growth and potential missed opportunities. Jean-François Harvey, Johnathan Cromwell, Kevin J. Johnson, and I studied more than 160 innovation teams and found that the key to faster, clearer progress is: Structured learning 👷🏗️ Our research, published in the Administrative Science Quarterly Journal, highlights four distinct types of learning behaviors used by high-performing teams and examines variations in the sequence and blend of these types of team learning. Without a deliberate rhythm, teams risk becoming overwhelmed by continual information intake, leading to confusion and burnout. But by honing a team's ideal 'learning rhythm,' you can avoid overwhelm and instead focus on strategic decision-making and sustainable innovation. Read our research summary now in the Harvard Business Review: https://lnkd.in/e5nU-Kka | 90 comments on LinkedIn

The Learning Journey That Led to Nowhere The carefully curated “learning journey.” Polished decks. Inspiring speakers. Branded workbooks. The kickoff, the modules, the reflection points, the wrap-up. The capstone . It all looked beautiful. But no one changed. No one led differently. No one made a better decision, helped somebody else, solved a harder problem, or grew in any measurable way. They left as they came—only now with a certificate and a champagne toast. And we called it success. Why? Because the survey said they liked it. Because someone said it “landed well.” Because it fit the budget, the time box, and the LMS tracked completion. But deep down, we know better. We know that most learning programs don’t stick. They don’t demand enough. They don’t disturb the old habits. They don’t connect to the real pressures people actually face at work and in life. We’ve made learning comfortable when it’s supposed to be disruptive and difficult. We’ve made it a journey—when it should’ve been an expedition. As practitioners, we carry some of the blame. We built what would be approved, not what was required. We chased polish over power. And we told ourselves that “awareness” was enough. Because if they leave the same way they arrived—was it a journey at all. Or was it a scenic loop.? | 65 comments on LinkedIn

Technology often outpaces the ability of organizations to embrace it. We look at how businesses can overcome the growing challenge of gen AI adoption.

How AI Works 101. Brief morning rant, in case anyone is persistently bamboozled by AI discussions. #AI #learning #education

Your best coach can't be everywhere at once. 𝘉𝘶𝘵 𝘵𝘩𝘦𝘪𝘳 𝘈𝘐 𝘵𝘸𝘪𝘯 𝘤𝘢𝘯. 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. 𝗜𝘁'𝘀 𝗰𝗮𝗹𝗹𝗲𝗱 𝗣𝗼𝗿𝘁𝗿𝗮𝗶𝘁𝘀. It’s more than a chatbot. It’s a library of voice-enabled, AI-powered avatars of real-world experts, trained only on their unique ideas and content. What that means: → Minimal AI hallucinations → No generic advice → Just the expert's authentic perspective, on-demand Check out this screenshot of Google Portraits. That’s an AI version of storytelling expert Matt Dicks. He’s coaching me to find the "heart of a story" in a seemingly dull, everyday moment — cutting grass. It's a very immersive experience as he walks me through finding the "story" in my experience. Think about the possibilities: → Democratize coaching: Assign a storytelling coach or a feedback sparring partner to every new manager. → Practice in private: Let employees rehearse difficult conversations in a safe and controlled environment before the real thing. → 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. It’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

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗖𝗣 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗿𝗮𝗿𝗲 “𝗼𝗵 𝗱𝗮𝗺𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴” 𝗺𝗼𝗺𝗲𝗻𝘁𝘀! I’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 can’t believe how much smoother everything gets. 𝗜𝗳 𝗜 𝗵𝗮𝗱 𝘁𝗼 𝗯𝗲𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗔𝗜, 𝗶𝘁’𝘀 𝗠𝗖𝗣. MCP sounds complex — but it’s really not. Think of it as a guide that helps your AI agents understand: → what tools exist → how to talk to them → and when to use them 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟵 𝗳𝘂𝗹𝗹𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗖𝗣 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 & 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 (𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱): ⬇️ 1. 100% Local MCP Client → Build a local MCP client using SQLite + Ollama — no cloud, no tracking. → Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG → Add fallback logic, vector search, and agents in one clean flow. → Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst → Fetch stock data, extract insights, generate summaries. → Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent → Speech-to-text, database queries, and spoken responses — all local. → Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) → Query 200+ data sources via natural language using MindsDB + Cursor. → Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor → Build cross-app memory for dev workflows — share context seamlessly. → Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs → Tackle PDFs, tables, charts, messy layouts with structured RAG. → Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) → Generate synthetic tabular data locally via MCP + SDV. → Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher → Rebuild ChatGPT’s research mode, fully local with writing agents. → Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn

AI can help, or hurt, our thinking

𝙃𝙖𝙗𝙩 𝙞𝙝𝙧 𝙨𝙘𝙝𝙤𝙣 𝙫𝙤𝙣 𝘼𝙄 𝙇𝙚𝙖𝙥 2025 𝙜𝙚𝙝ö𝙧𝙩? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schüler:innen der 10. und 11. Klasse sowie 3.000 Lehrkräften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewährt. 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 über die Arbeitswelt der Zukunft diskutieren durfte. AI Leap ist Estlands Antwort auf die vielseitigen Herausforderungen im Bildungsbereich und fördert frühzeitig notwendige Schlüsselkompetenzen, die für den Arbeitsmarkt der Zukunft unerlässlich 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 🅰🅱🅴🆁: 🎯 WO STEHEN WIR IN DEUTSCHLAND❓ 🎯 Wie können wir trotz Bildungsförderalismus schnell wirksam werden❓ Spannende Fragen für unsere neue Regierung v.a. mit Blick auf das Bundesministerium für Digitales und Staatsmodernisierung unter Leitung von Dr. Karsten Wildberger, das die #Digitalisierung und die #KI #KünstlicheIntelligenz in Deutschland auf ein nächstes Level heben will. Was mir gefällt 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 können wir viel von Estland (und anderen Ländern) lernen v.a. wenn wir in globale Kooperationen und in Public-Private-Partnership Modelle investieren. Quelle: https://lnkd.in/eUzXiSza #FutureOfWork #FutureSkills #SmartLearning :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 🔔 Du möchtest mehr über die Arbeitswelt im Wandel zu erfahren? Let's connect! 💌 Du interessierst Dich für eine Zusammenarbeit? Schreib mir gerne!

When I think about the future of learning with AI, I don’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. That’s not to say I have “all the answers”, coz I don’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? → Join us by clicking 'subscribe to my newsletter' on this post and my profile. #education #learninganddevelopment #artificialintelligence

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

For a long time I felt that if we wanted to answer questions such as ‘how do we design learning experiences?’, ‘how do we measure learning?’, ‘what is our pedagogy based on?’ - or even just explain to stakeholders what it is that we do - then it would help to have an understanding of learning. Thanks again to Ben Gallacher and the #Inrehearsal team for creating this series. #learning #pedagogy #learningdesign #education #training

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: ▪️99% of education leaders, 87% of educators worldwide & 93% of US students have already used generative-AI for school at least once or twice! ▪️Yet only 44% of those educators worldwide & 41% of those US students say they “know a lot about AI.” ‼️this means our usage is far outpacing our understanding & that’s a significant gap! When such powerful tools are used without real fluency, we would see: ▪️complicated implementation with no shared strategy (sounds familiar?)! ▪️anxious students who’d fear being accused of cheating (I've heard this from so many students!) ▪️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: ▪️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 I’d think we should consider: ✅building a "learning-first" culture across institutions & understanding when AI supports our learning vs. when it gets in the way! ▪️more like, we need to swap the question "Are we using AI?" for "Can we show any learning gains?" ⚠️so, what shifts does this report data point us to? Here is my takeaway: ✅Building real AI fluency: ▪️moving beyond simple "prompting hacks" to true literacy that includes understanding ethics, biases & pedagogical purposes, ▪️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, ▪️it's about building shared wisdom, not just industry-ready skills ✅Creating collaborative infrastructure: ▪️the "every teacher for themselves" approach seems to be failing, ▪️shared guidelines, inclusive AI Councils & a culture of open conversation are now needed to bridge this huge gap! ✅Shifting focus from "using AI tools" to "achieving learning outcomes": ▪️this one really resonated with me because unlike other tech rollouts we've witnessed, AI directly affects how our students think & learn, ▪️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 ▪️isn't to get every student using AI! ▪️but to make sure every learner & teacher really understands it! ⁉️I’m curious, where is your institution on this journey? 1️⃣ individual use: everyone is figuring it out on their own (been there!) 2️⃣ shared guidelines: we have policies, but they're not yet deeply integrated (getting closer!) 3️⃣ fully integrated strategy: we have a unified approach with a learning-first, outcome-tracked focus (this is the goal!) | 24 comments on LinkedIn

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀 𝗱𝗼𝘄𝗻 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗕𝗘𝗦𝗧 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗟𝗟𝗠𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸. ⬇️ 𝘓𝘦𝘵'𝘴 𝘣𝘳𝘦𝘢𝘬 𝘪𝘵 𝘥𝘰𝘸𝘯: 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: - 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. 𝗧𝗵𝗲 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺 (𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻): - Words influence each other based on context — ensuring "bank" in riverbank isn’t confused with financial bank. - The Attention Block weighs relationships between words, refining their representations dynamically. 𝗙𝗲𝗲𝗱-𝗙𝗼𝗿𝘄𝗮𝗿𝗱 𝗟𝗮𝘆𝗲𝗿𝘀 (𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴) - After attention, tokens pass through multiple feed-forward layers that refine meaning. - Each layer learns deeper semantic relationships, improving predictions. 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - This process repeats through dozens or even hundreds of layers, adjusting token meanings iteratively. - This is where the "deep" in deep learning comes in — layers upon layers of matrix multiplications and optimizations. 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 & 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 - 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. 𝗧𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝗮𝗿𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗼𝗳 𝗮𝗹𝗹 𝗟𝗟𝗠𝘀 (𝗲.𝗴. 𝗖𝗵𝗮𝘁𝗚𝗣𝗧). 𝗜𝘁 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 𝘁𝗼 𝗵𝗮𝘃𝗲 𝗮 𝘀𝗼𝗹𝗶𝗱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝘄𝗼𝗿𝗸 𝗶𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀. 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 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 48 comments on LinkedIn

A visual introduction to transformers. This chapter focuses on the overall structure, and word embeddings

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"—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

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: 📊 Strategy-first adopters dominate ROI. Having a visible AI roadmap makes all the difference: firms with a clear strategy are 3.5 × 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. ⏱️ AI is freeing up 240 hours a year. Professionals expect generative AI to claw back about five hours a week—240 hours annually—worth roughly US $19 k per head and a US-wide impact of US $32 billion for legal and tax-accounting alone. 🚦 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. 🧠 Skill depth multiplies payoff. Employees with good or expert AI knowledge are 2.8 × more likely to report organisational gains, regular users are 2.4 × more likely, and those with explicit AI adoption goals are 1.8 × more likely to see benefits. 🏅 Leaders who walk the talk win. When leaders model new tech adoption, their people are 1.7 × likelier to harvest AI benefits; active tech investors double their odds, and firms that added transformation roles see a 1.5 × uplift. 🎯 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 review—making reliability the top blocker to further investment. 🌱 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. 🛠️ Tech-skill shortages stall teams. Almost half (46 %) of teams report skill gaps, with 31 % pointing to deficits in technology and data know-how—outpacing gaps in traditional domain expertise or soft skills. 🔄 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. 🔗 Goals and strategy are often misaligned. Two-thirds (65 %) of professionals who set personal AI goals don’t know of any corporate AI strategy, while 38 % of organisations with a strategy give staff no personal targets—fuel for inconsistent, inefficient adoption

Die Lernplattform bietet interaktive Online-Kurse für den effektiven Umgang mit generativer KI. Das Angebot steht allen offen und ist kostenlos.

'You can’t accomplish anything if your stakeholders aren’t on board.' After delivering hundreds of Human Centred Design (5Di©) workshops, ‘Stakeholders’ is one of the topics that comes up time & time again: L&D want to do one thing, the business another. What’s required is a consultative approach rather than an adversarial one - partnering not pushback. In response, I added new resources into the latest version version of the 5Di© toolkit released a few months back (https://lnkd.in/eacUSAbc) - here are a couple of them: #learning #education #consulting #learninganddevelopment #HR #stakeholders | 11 comments on LinkedIn

𝗙𝘂𝘁𝘂𝗿𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗪𝗮𝘀 𝘄𝗶𝗿𝗱 𝗮𝘂𝘀 𝗱𝗲𝗿 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗲𝗻𝘁𝘄𝗶𝗰𝗸𝗹𝘂𝗻𝗴? In der aktuellen Wirtschaftswoche (1) plädieren Julian Kirchherr und Cawa Younosi für „NO HR“, d. h. die Abschaffung des gesamten Personalbereiches mithilfe Generativer KI und die Rückverlagerung von HR-Aufgaben ins Management. Alles, was besonders viel Fingerspitzengefühl verlangt, insbesondere die Entwicklung ihrer Mitarbeitenden, gehört danach in die Verantwortung der Führungskräfte. Routinetätigkeiten wie das Erstellen von Arbeitsverträgen, Schulungszertifikaten oder die erste Sichtung von Bewerbungen übernimmt die Maschine. Ihre Hauptargumente sind:  • Die Führungskräfte sollen Personalaufgaben direkt verantworten, um eine engere Verknüpfung der strategischen Unternehmensziele mit dem Personalmanagement zu erreichen.   • Traditionelle HR-Abteilungen sollten in ihren Strukturen überdacht oder sogar abgeschafft werden, um mehr Agilität zu ermöglichen. Nicht nur administrative Aufgaben wie Arbeitsverträge, werden automatisiert, auch Arbeitszeugnisse oder Umfragen werden mit Hilfe der KI bearbeitet. Was auf den ersten Blick sehr radikal wirkt, beinhaltet aus meiner Sicht diskussionswürdige Aspekte. Mit diesem Vorschlag rücken viele Aufgaben wieder dorthin, wo sie eigentlich schon immer hingehörten, zur Führungskraft. Zu ihrer Verantwortung rechnen danach Onboarding, Nachfolgeplanung oder die gezielte Skillsentwicklung im Team sowie Fragen von Kultur und Transformation. Sie entwickeln sich zu „𝗣𝗲𝗼𝗽𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿*𝗶𝗻𝗻𝗲𝗻“. Ich bin der Überzeugung, dass weiterhin eine HR-Funktion benötigt wird, aber mit relativ wenigen, hoch spezialisierten Expert*innen, z. B. für die mittelfristige Personalbedarfsplanung und insbesondere für 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁. Im Future Learning stehen nämlich die Mitarbeitenden im Mittelpunkt, die mit Unterstützung der KI selbst ihre Lernpfade planen und im Arbeitsprozess umsetzen. Ihre Führungskräfte werden deshalb zu ihren Entwicklungspartner*innen, die ihre personalisierten Lernpfade ermöglichen. Die Personalentwicklung wandelt sich folglich zum 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝘀𝗰𝗵𝗲𝗻 𝗟𝗲𝗿𝗻𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗸𝘁𝗲𝗻, zu 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁. Diese baut und optimiert laufend das "Lernhaus" als Ermöglichungsraum für selbstorganisiertes Lernen im Dialog mit der generativen KI , begleitet Lernprojekte sowie die notwendigen Veränderungsprozesse und evaluiert den Lernerfolg. 𝗟&𝗗 𝘄𝗶𝗿𝗱 𝗱𝗮𝗺𝗶𝘁 𝘇𝘂𝗿 𝘇𝗲𝗻𝘁𝗿𝗮𝗹𝗲𝗻 𝗨𝗺𝘀𝗲𝘁𝘇𝗲𝗿𝗶𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝘀𝗰𝗵𝗲𝗿 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. Daraus ergibt sich folgende Rollenverteilung im Future Learning. (1) https://lnkd.in/eiupSJae

Nichts vergessen und alles schnell finden: Seit einigen Monaten nutze ich ein einfaches System, um wichtige Daten und Informationen aus meinem Leben besser zu speichern und zu organisieren. So funktioniert es.

Künstliche Intelligenz

So, it finally happened, I spent a week ‘vibe coding’ an app with an AI app builder. I learnt a ton from this experience, which I’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, “This might be a good use case for an app experiment”, but the AI-powered app builders weren’t so great then. Now, it’s a whole new world, and I’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 😂. While the tool is available to use, it is most certainly a prototype, so expect bugs, glitches and weird things to happen. For now, I’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 ↓ 🔗 to the tool: https://lnkd.in/efJaPJF5 📧 Share your FB to support@stealthesethoughts.com #education #artificialintelligence

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