Open New Learning Lab Resources

Didaktische Schieberegler der ISH Manufaktur

Vernünftige deutsche Version ist auch mit dabei 😁 Yeah! Die sehr praktische Audio-Zusammenfassung von NotebookLM ist jetzt in über 50 Sprachen verfügbar und deutsch ist auch dabei. Damit bist du in der Lage verschiedene Wissensquellen in vernünftiger deutsche Sprache zu konsumieren. Die Qualität der Aussprache und die Stabilität der Stimme sind auch richtig gut. Teilweise auch so gut, dass ich es nicht 100%ig von Menschen unterscheiden könnte. Google hat da einen guten Job gemacht. 👏🏻 🔵 Auch schon vorher möglich, aber nicht gut Ja, auch vorher war die Audio-Zusammenfassung auf Deutsch möglich, wenn man den Prompt angepasst hat. Gut war die Version aber nicht und die Aussprache teilweise nicht zu gebrauchen. Mit der offiziellen Unterstützung von anderen Sprachen klingt das Ganze schon wesentlich besser. 🔴 Wie findest du die deutsche Version? ------------------------------------------ 👉🏻 Mein KI-Newsletter: https://lnkd.in/gy42ujUE ------------------------------------------ #ki #ai #notebooklm #genai

In diesem Experiment reagiere ich auf Josh Bersins Impuls "The Revolution in Corporate Learning begins" und reflektiere, wie diese Themen auf unsere Realität...

Becoming an "AI-FIRST COMPANY" - nach dem viralen Post von Shopify über das Thema der AI-First Mentalität, legt Duolingo nach und ich kann nur empfehlen die Argumente bzgl "...employees can “focus on creative work and real problems, not repetitive tasks.” etc sich anzuschauen. Die mutigsten Unternehmen erkennen Wendepunkte, bevor sie offensichtlich werden. 2012 setzte Duolingo auf Mobile-First, als andere noch in Desktop-Denken gefangen waren. Heute stehen wir vor einem ähnlichen Moment – dem KI-Paradigmenwechsel. Duolingo sieht KI nicht nur als Produktivitätstool, sondern als Schlüssel zur Mission. KI skaliert Inhalte, die sonst wesentlich aufwändiger zu produzieren wären. Zum ersten Mal ist Unterricht auf dem Niveau der besten menschlichen Lehrer in Reichweite. Die klaren strukturellen Änderungen zeigen den echten Transformationswillen: _KI-Kompetenz als Einstellungs- und Leistungskriterium _Personalwachstum wo Automatisierung keine Option ist Am wichtigsten: Diese Transformation stellt Menschen in den Mittelpunkt. Es geht nicht darum, Mitarbeiter zu ersetzen, sondern sie von Routineaufgaben zu befreien und ihre Kreativität zu entfesseln – unterstützt durch Schulungen und Mentoring. In einer Zeit, in der noch so viele Unternehmen zögern, macht Duolingo bereits den nächsten Sprung. Eine kleine Erinnerung daran, dass wahre Innovatoren keine Angst vor Veränderung haben – sie sind diejenigen, die den Wandel willkommen heißen, bevor er zur Notwendigkeit wird. Ein paar wichtigen Stellen des offiziellen Announcements hier: "AI is already changing how work gets done. It’s not a question of if or when. It’s happening now. When there’s a shift this big, the worst thing you can do is wait. ... this time the platform shift is AI. AI isn’t just a productivity boost. It helps us get closer to our mission... Being AI-first means we will need to rethink much of how we work. Making minor tweaks to systems designed for humans won’t get us there. In many cases, we’ll need to start from scratch. We’re not going to rebuild everything overnight, and some things-like getting AI to understand our codebase-will take time. However, we can’t wait until the technology is 100% perfect. We’d rather move with urgency and take occasional small hits on quality than move slowly and miss the moment. ... Duolingo will remain a company that cares deeply about its employees. This isn’t about replacing Duos with AI. It’s about removing bottlenecks so we can do more with the outstanding Duos we already have. We want you to focus on creative work and real problems, not repetitive tasks. We’re going to support you with more training, mentorship, and tooling for AI in your function. Change can be scary, but I’m confident this will be a great step for Duolingo. It will help us better deliver on our mission — and for Duos, it means staying ahead of the curve in using this technology to get things done."

No, you're not a "visual learner." Or an "auditory learner." Or a "kinesthetic-scent-triggered-by-smooth-jazz" learner. You’re a brain — and brains don’t work like that. There’s zero scientific evidence that teaching people in their so-called “preferred learning style” helps them learn better. In fact, it often hurts by giving people permission to avoid the real work of processing knowledge deeply, by engaging in an objectively more appropriate exercise for the topic at hand. Don’t get me wrong — personalized learning is still awesome. But that just means adjusting the difficulty, pacing, or topic sequence to how well you know something — not whether you "like" podcasts better than diagrams. What most people call “learning styles” are really just learning preferences. Sure, someone might like to watch a video instead of doing flashcards — but that doesn’t mean it’s the better learning experience. In fact, the easier or more “comfortable” something feels… the less likely it is to stick. Real learning = effortful. Uncomfortable. Active. Often annoyingly repetitive. And unfortunately, not optimized around your vibes. So let’s keep pushing for personalized learning that works — based on science, not zodiac signs. #LearningScience #EdTech #CognitivePsychology #Brainscape | 110 comments on LinkedIn

We are excited to announce the general availability of People Skills, a powerful new data layer in Microsoft 365 Copilot, Microsoft 365 and the Viva portfolio of apps and services. People Skills is the evolution of the service previously known as Skills in Viva.   Built on this new data layer, we’re also introducing the new Skills agent that helps leaders create dynamic skill-based teams to tackle any project, and lets employees find and connect with people with the skills they need.  People Skills enables Copilot and the Skills agent to understand the backbone of your company – your people. This innovative new grounding source augments the critical work context already built into Copilot to produce the first generative AI experience to deeply understand both your business and your people.   The People Skills data layer will start general availability rollout to Microsoft 365 Copilot and Viva customers at Microsoft Build in May 2025.  The Skills agent will become available starting in June 2025.   Overview  People Skills infers individuals’ skillsets derived from user profile and activity mapped to a customizable built-in skill taxonomy. This data layer fuels the Skills agent, and enhances Copilot Chat, Microsoft 365, and Viva services with contextualized information about the people in your organization.   With this advanced AI-based skills inferencing methodology built into everyday work tools, paired with robust privacy and visibility controls for both admins and end users, People Skills can equip leaders with critical workforce skill insights to prepare and accelerate their AI transformation while empowering employees with personalized skill profiles and career growth tools.   Skills agent  The new Skills agent helps leaders and employees across the organization stay informed, agile, and ready to thrive.   Employees can use the agent to easily explore their own skills and how to develop them, see how they can best leverage Copilot, find experts in the organization, and understand colleague’s skillsets.   Leaders can use the Skills agent to inform strategic workforce planning decisions, such as staffing for high-priority projects, with an up-to-date view of talent landscape strengths, gaps, and opportunities.    People Skills for leaders and organizations  To empower business leaders and analysts with detailed skills-based organizational insights, we’re introducing the new Skills landscape report in Copilot Analytics. This report can be filtered by organizational data - such as HR attributes – to allow leaders and analysts to customize views for relevant groups. The Skills landscape report data can also be exported for custom analysis.   The Skills landscape report will be available in Viva Insights starting in June 2025 and include four report pages detailed below.   The Skills introduction page explains how skill inferences are generated, allows for customization of report parameters, and displays a snapshot of organization progress towards confirmed skills on individual profiles.   The Top skills page shows commonly used skills and identifies areas of skill specialization in your organization.  The Deep dive page allows you to deep dive into a specific skill – including subskills and adjacent skills – to get a more complete view of talent in a specific area.     This Deep dive page also delivers insights focused on the number of people using selected skills by group, trending growth over time, and a heatmapped view of skill concentration across teams.    The Skills hierarchy page allows you to view the connection between skills and explore how your company’s skill taxonomy is structured. This view further allows you to drill down into granular skills that are critically important to your business.   People Skills for employees  People Skills improves the employee experience by improving expertise discovery, showcasing hard-earned talents, and accelerating career growth. As a core principle of People Skills, every person will have the ability to edit, update, customize – and if desired – opt out of sharing their skills or having their skills inferred.   People Skills enables multiple employee scenarios across a variety of apps and services, including: Microsoft 365 Copilot, Skills agent, Microsoft 365 profile card, Microsoft 365 profile editor, Org Explorer, People companion, and Viva Learning.   Watch the video below to see how People Skills creates a rich, connected experience in the flow of work across these endpoints.    Inference engine  The People Skills inference engine uses Microsoft 365 profile and activity signals from the Microsoft Graph like documents, emails, chats, and meetings to create personalized skill profiles for individuals within your organization.         Under the hood, the inference engine is powered by the latest OpenAI LLM models, with a proprietary inferencing approach based on principles of game theory and multi-agent frameworks executing multi-directional inference runs across relevant Microsoft Graph data. This skill extraction ecosystem leverages simulated agent personas that operate on bespoke logic to capture the right signals, optimize for a diverse and specific pool of skills, and improve predictability between input signals and output skills. Combined with the richness of underlying Microsoft Graph data, this proprietary approach produces accurate skill profiles for each user.   In addition to generating accurate skill profiles, the People Skills inference engine:  Has a frequent refresh cadence so inferences are always up-to-date and relevant  Requires zero action by end users (note that users always have control over their skills inferences, profile display, and visibility settings)  Takes <5 minutes to set up from Microsoft 365 admin center when using our recommended configuration  Includes robust privacy and visibility controls at both the admin and user level  By building a skills inference engine that is accurate, up to date, easy to set up, requires zero end-user action, and is embedded into everyday work tools – we believe we may have solved the core issues traditionally preventing companies from accessing relevant skills information.    Flexible taxonomy approach  People Skills includes a flexible approach to skills taxonomy management, designed to meet you at any stage along your journey to a skills-enabled organization.   Option 1 – Use the built-in skills taxonomy  People Skills includes a built-in skills taxonomy of 16,000+ skills produced in partnership with LinkedIn. Each of the 16,000+ skills in this taxonomy are surrounded by a semantic description of embedded data including skill name, skill description, related skills, where the skill fits in a skill hierarchy, roles that tend to have this skill, and more contextual information on how the skill gets applied at work. This information helps the People Skills inference model determine when the skill is demonstrated.  You can use this taxonomy out of the box, or customize as needed, with admin editing capabilities supporting both removal and addition of skills.  Option 2 – Use your own custom skill taxonomy  You may choose not to use the built-in skills taxonomy, and instead import your own custom skill taxonomy.      Privacy and visibility controls  We take responsible AI and privacy seriously to help you deploy with trust. People Skills includes robust privacy and visibility controls both at the admin and user level. Admins can set these controls for users, groups, or for their entire tenant to meet their needs.   As noted below, users are always in control of their Microsoft 365 profile and may turn skills inferencing and/or skills visibility off at any time.   Skills inferencing controls:  Admins can turn skills inferencing auto-on (individual users can opt out)  Admins can turn skills inferencing auto-off (individual users can opt in)  Admins can disable skills inferencing for their tenant  Skills visibility controls - this refers to the ability for users to see their colleagues’ skills on surfaces like the people card or in Copilot:  Admins can turn skills visibility auto-on (individual users can opt out)  Admins can turn skills visibility auto-off (individual users can opt in)  Admins can disable skills visibility for their tenant  People Skills also provides a framework for tagging sensitive skills that admins do not want the inference engine to capture.   Customer feedback  People Skills has already been enabled for over 100,000 Microsoft employees and 10 customers participating in our private preview. We thank our preview customers for all the deep dives, feedback, and iteration as we fine-tuned our inference engine. In their words:  “We’ve partnered with Microsoft to help our people gain the skills they need for the future. As we undertake one of the largest transformations in UK financial services, gaining deeper skills insights will be instrumental in unlocking the potential of our colleagues and equipping them with the capabilities they need now and in the future. Making the process for colleagues to record their skills simple and efficient has been a priority, enabling them to focus on the skills they want to develop to better serve our customers. The AI-powered suggestions within People Skills are thoughtfully aligned with the work our colleagues do, helping us accelerate progress in our transformation journey”.    - Sharon Doherty, Chief People & Places Officer, Lloyds Banking Group    "The future of work is increasingly about sk...

Aladin El-Mafaalani, Soziologe und Professor für Migrations- und Bildungssoziologie, nimmt kein Blatt vor den Mund: Das deutsche Bildungssystem steckt tief i...

Learn about Anthropic's comprehensive framework for identifying, classifying, and mitigating potential harms from AI systems, ensuring responsible development of advanced AI technology.

Sammlung von Prompts für wissenschaftliches Arbeiten von Barbara Geyer

🔮 Where is the Future of AI-Powered Digital Adoption Foundational priorities By 2028, both executives and employees will prioritize security, efficiency, and proper infrastructure management over specific features. Evolution of DAPs Next-gen Digital Adoption Platforms are evolving to incorporate cross-application, contextual support that continuously improves through user interaction. Path to HyperProductivity Organizations that successfully implement AI while leveraging emerging technologies will achieve a state of HyperProductivity – where human capabilities and technology converge to achieve measurable gains in efficiency, innovation, and resilience. See some selected interesting pictures - or download the full report via the link in the comments. #DigitalAdoption #AITransformation #DigitalProductivity #FutureOfWork

Der große Vergleich von GPT-4o, o3, o4 mini, 4.1 und mehr ✨ Ein persönlicher Erfahrungsbericht und ein Deep Research – speziell für mein Netzwerk.

This week I detail the whole story: how the $360 billion corporate learning market is being blown up by AI.This tidal wave has now arrived, with every major ...

Key findings: - STEM students, particularly in Computer Science, are early adopters of AI tools like Claude, accounting for 36.8% of conversations despite representing only 5.4% of U.S. bachelor's degrees. - Students interact with AI in four primary ways: Direct Problem Solving, Direct Output Creation, Collaborative Problem Solving, and Collaborative Output Creation, each occurring at similar rates. - Claude is mainly used for creating and improving educational content (39.3%), technical explanations (33.5%), and higher-order cognitive functions like Creating (39.8%) and Analyzing (30.2%). Students are not just seeking quick answers; they're using AI as a collaborative tool to enhance their learning journey. This trend highlights the transformative potential of AI in higher education. And it shows: students are smarter than many teachers think or fear. I also liked this graphic which however is also nice marketing showing Claude being used for higher order thinking via creation something new… which is the strength of LLMs obviously. #AIinEducation #HigherEducation #STEM #Innovation #FutureofLearning

𝗧𝗼𝗽 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 — 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗦𝗶𝗺𝗽𝗹𝘆 𝟭. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) → Helps computers understand and write human-like text → Examples: GPT-4, Claude, Gemini → Used in: Chatbots, coding tools, content generation 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 → The tech behind all modern AI models → Let models understand meaning, context, and order of words → Examples: BERT, GPT 𝟯. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Writing better instructions to get better AI answers → Includes system prompts, step-by-step prompts, and safety rules 𝟰. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 → Training an AI model on your data → Helps tailor it for specific tasks like legal, medical, or financial use cases 𝟱. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 → A way for AI to understand meaning and relationships between words or documents → Used in search engines and recommendation systems 𝟲. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) → Combines AI with a database or document store → Helps AI give more accurate, fact-based answers 𝟳. 𝗧𝗼𝗸𝗲𝗻𝘀 → The chunks of text AI reads and writes → Managing them controls cost and performance 𝟴. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 → When AI gives wrong or made-up answers → Can be fixed with fact-checking and better prompts 𝟵. 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 → When AI can perform a task without being trained on it → Saves time on training 𝟭𝟬. 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 → AI explains its answer step-by-step → Helps with complex reasoning tasks 𝟭𝟭. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗼𝘄 → The amount of info AI can see at once → Larger windows help with longer documents or conversations 𝟭𝟮. 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 → Controls how creative or predictable AI is → Lower values = more accurate; higher values = more creative 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗼𝗺𝗶𝗻𝗴 𝗡𝗲𝘅𝘁? → Multimodal AI (text, images, audio together) → Smaller, faster models → Safer, ethical AI (Constitutional AI) → Agentic AI (autonomous, task-completing agents) Knowing the terms is just step one — what really matters is how you 𝘶𝘴𝘦 them to build better solutions. | 51 comments on LinkedIn

This week I discuss this massive shift toward “AI-Native” applications and systems which are radically different from traditional HR Tech, with a particular ...

You can access the full version for free below. ⬇️ 𝗜𝗻 𝘀𝘂𝗺𝗺𝗮𝗿𝘆, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀: ➜ Be Clear with Your Instructions: GPT-4.1 is really good at following directions, but only if you're specific. The more clear and direct your prompt, the better the response. ➜ Break Down Complex Tasks: If you're working on something complicated, ask GPT-4.1 to “think step by step.” It helps the model give more accurate and thoughtful answers. ➜ Use Structure: If you need to share a lot of info, use clear structure—like markdown or bullet points. This helps GPT-4.1 understand and organize the info better. ➜ Format Your Prompts with Clear Sections: Structure your prompts for easier comprehension:   - Role and Objective   - Instructions (with subcategories)   - Reasoning Steps   - Output Format   - Examples   - Final instructions ➜ Put Important Instructions at the Start and End: For longer prompts, put your key instructions both at the beginning and the end. This helps the model stay on track. ➜ Guide It with Reminders: If you're designing a workflow or solving a problem, include reminders like “keep going until it’s fully resolved” or “plan carefully before acting.” This keeps the model focused. ➜ Use the Token Window Wisely: GPT-4.1 can handle a huge amount of text, but too much at once can slow it down. Be strategic about how much context you provide. ➜ Balance Internal and External Knowledge: For factual questions, tell GPT-4.1 to either “only use the provided context” or to mix that context with general knowledge. This helps you get the most accurate results. 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: 𝗧𝗵𝗲 𝗸𝗲𝘆 𝘁𝗼 𝘂𝘀𝗶𝗻𝗴 𝗚𝗣𝗧-4.1 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝘁𝗵𝗮𝘁 𝗴𝘂𝗶𝗱𝗲 𝗶𝘁 𝘁𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗻𝘀𝘄𝗲𝗿. 𝗜𝘁’𝘀 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆! Access it here or download it below: https://lnkd.in/dCm6DeFW | 59 comments on LinkedIn

I've covered an explainer of AI agents for non-techies before, see the comments for a link to that. For most non-techies, AI is viewed as one entity doing every thing on its own. With agents, we can create a team of specialists. That’s the idea behind multi-agent AI systems This image (from the brilliant folks at LangGraph) shows different ways you can set up teams of AI “agents.” Think of each agent like a little digital worker with a specific role - one plans, another checks facts, one executes tasks, and another reviews the results. Like any good team, they talk to each other, share ideas, and back each other up. Now, let's explain that image: 1️⃣ Single Agent This is your classic setup with one AI model doing all the work. It can use tools, but it’s working solo. Smart, but overworked. 2️⃣ Network Here, agents all talk to each other like a group chat. Everyone’s sharing, checking, and helping out. Great for collaboration, but can get noisy. 3️⃣ Supervisor This is the manager model where one central AI supervises others. It gives instructions and checks in. A bit like a project lead guiding a team. 4️⃣ Supervisor as Tools Flip it around: the main AI treats the others as tools. It doesn’t chat with them it just uses them to get stuff done. Efficient, but not very democratic. 5️⃣ Hierarchical This is like an org chart. Big boss on top, middle managers below, then the doers. Neat, structured, scalable. 6️⃣ Custom Everything everywhere all at once. No strict structure—just doing what works to get the job done. It can look a bit messy, but it’s great for handling tricky tasks that don’t fit in a neat box. → So why does this matter? Traditional AI is like having one brain trying to do everything. But now, we can build teams of AIs, each focused on a task—planning, checking, executing, or reviewing. Multi-agent systems might sound like Sci-Fi but they're already at work today. ↳ Image Credit: Google Agents Companion & LangGraph Multi-agent systems 📔 Source: Agents Companion Report 2025 by Google #education #artificialintelligence #learninganddevelopment

2023: „AI wird meinen Job ersetzen.“ 2024: „AI ist mein Copilot.“ 2025: „AI ersetzt keine Freundschaft. Aber manchmal hilft sie beim Denken.“ Super spannendes Lesefutter von der Harvard Business Review. Marc Zao-Sanders hatte sich nach 12 Monaten seinen Artikel aus 2024 wieder angeschaut und ein Update veröffentlicht, wie viele von uns inzwischen wirklich mit Gen AI Tools arbeiten. Spoiler: Der Hype ist vorbei – und das ist gut so. Denn es wird super konkret: _Führungskräfte nutzen GPTs zur Strategieentwicklung. _Manager bauen sich ihre eigenen Helferlings. _Entwickler sparen 56 % Zeit beim Coden. _Teams automatisieren repetitive Tasks – und schaffen Raum für Kreativität. Die Grenze zwischen „Business Use Case“ und „Private Use Case“ verschwimmt zunehmend. Workflows, die am Küchentisch anfangen und in der Vorstandsetage landen. Die spannendste Erkenntnis: Der meistgenutzte GenAI-Use-Case 2025 ist - Nicht Coding. Nicht Präsentationen. Nicht Strategie. Sondern: Therapie. 🧠 Gespräche mit Chatbots über Stress, Selbstzweifel, Sinnfragen. 📓 Journaling mit KI als stillem Gegenüber. 🪞Selbstreflexion – strukturiert, aber menschlich. Laut HBR ist „mentale Gesundheit“ als Use Case für GenAI noch vor Business Productivity. Tools wie ChatGPT weniger Roboter als Spiegel. Dass wir in einer Welt leben, in der vielen genau das fehlt: ein geschützter Raum zum Denken, Reden, Fühlen. Und dass KI vielleicht nicht nur Arbeit, sondern auch Zugang demokratisiert – zu Support, der vorher unerschwinglich war. 🌀 Vielleicht ist das die eigentliche Disruption.

💡 Wie viel Power steckt im Google AI Studio? Live-Test der „Share Screen“-Funktion als Performance Support! 🖥️In diesem Video nehmen wir die Share Screen-F...

A research-backed AI scenario forecast.

Catch the top moments from the Google Cloud Next keynote presentation, featuring CEO Thomas Kurian on AI breakthroughs, along with key announcements and real...

Bei Künstlicher Intelligenz an der Hochschule denken die meisten an KI in der Lehre. Sollen Studierende KI nutzen und falls ja, im Unterricht, bei den Übungen oder zu Hause bei den Ausarbeitungen? Sind bestehende Prüfungsformen noch zeitgemäß? Sollen Dozenten zeigen, wie KI benutzt werden kann und s

Very few companies I've encountered have been so direct and clear on "How we expect you to use AI at x company". I think this move empowers and allays the fears of those who are already using AI on the side. That's a big hurdle already cleared. One which can really amplify adoption. There's some bits I'm not sold on (looking at you point 5), yet, what I really liked here is the focus on learning together. What's clear for Shopify employees is: - They have access to AI tools - They have the endorsement to experiment and explore - They're actively being given places to share ideas and lessons - They know leveraging is a key skill for today and the future If you want to build these mythical learning cultures we all talk abt, you need more action like this. Often, people know what to do but until you open the door to that path as a company, you'll probably stagnate. Anyway, that's my two cents. → How are you approaching the mandate of AI at work? Let's chat abt that in the comments ↓ #education #learninganddevelopment #artificialintelligence

In dem AI Adoption in L&D Circle des New Learning Lab lernt ihr, die KI Transformation bei euch aktiv mitzugestalten.created by Jan Foelsing#aiadoption #ai #...

I got a message last week that stopped me mid doom-scroll. "I just saw a video of an AI agent taking a test for someone. Aren't you worried about that?" Honestly? No. But probably not for the reason you'd expect. I've spent nearly 20 years in learning and education. I've seen trends, fads, and enough terrible multiple-choice quizzes to last a lifetime. The AI agent didn't scare me. The fact that this is still how we assess skills? That did. If your entire measurement of understanding relies on picking answers from lists, of course an AI can game it. So could a cheat sheet. So could your colleague who's got a memory like an elephant. This isn't about AI cheating the system. It's about the system being easy to cheat because it's broken. ❌ We don't need better tests. → We need a better approach. If we confirm 'skills' and 'understanding' through quizzes, then it's all just a game of who has the best memory, not who understands how to apply knowledge. I know millions of institutions and corporate learning experiences worship at the altar of the almighty multiple choice exam as the measurement stick for human intelligence, skills and expertise. ↳ Doesn't mean it's right. That's where AI can actually help. Not as a cheat code, but as a coach. To challenge, to question, to uncover the "how" behind your answer. In tomorrow's Steal These Thoughts! newsletter, I'll show you an AI-powered approach that could transform how we validate skills and knowledge, and help you build more meaningful assessment experiences. Join us by clicking 'subscribe to my newsletter' on this post and my profile. #education #learninganddevelopment #aritificialintelligence | 27 comments on LinkedIn

Gratis und das BESTE LLM auf zig Benchmarks. Wie hat Google das geschafft? Und vor allem, was bedeutet das für uns?Schnappt euch hier Incogni mit meinem Code...

Implementing AI isn't just about integrating new technology. It's about transforming the way people work. Without a structured change management approach, AI initiatives can face resistance, underperformance, and missed opportunities. Here are the key success factors for good AI change management: ✅ Clear vision and purpose: Define the goals and benefits of AI in your company. Make it aspiring. ✅ Strong leadership: Leaders must champion the change and guide their teams. Don't forget to communicate clear expectations. ✅ Effective communication: Regular updates and transparency to manage expectations and take away fear. ✅ Evangelists: Elect "Pro users" in each department to provide instant support. ✅ Training and development: Equip your team with the skills needed to leverage AI. ✅ AI platform: Ensure the platform has excellent UX to drive user adoption and engagement. UX is not UI! ✅ AI agents: Yes, this specific feature is key. Create and share AI agents that employees can use off the shelf. Significantly improves adoption! At Zive, it's our mission to make work efficient and enjoyable for everyone. We provide the best platform for it. But it doesn't work without good change management. Thankfully, we have amazing partners who support our customers with it. #AI #ChangeManagement #Leadership #Innovation #EmployeeEngagement #BusinessTransformation #UserExperience #AIagents | 18 comments on LinkedIn

Watching long videos but not learning much? Here’s my system: 1) Upload a video you watched/are… | 61 comments on LinkedIn