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Today, I’m diving into the world of deep research tools to find out which platforms are truly the most helpful for academic work. ▼ ▽ Sign up for my FREE new...

There is a lot of talk about how L&D needs to be proactive, not reactive. But how do we do that when technology is moving so fast? It starts with having a point of view on where the world of work is headed, and then building a bridge to that future. Because if we only make incremental changes from where we are now, we'll likely be playing catch-up for a long time—and risk preparing people for the work of today, not tomorrow. Here are some of the forces I think about a lot these days: 🎓 AI seems to be denting the supply of entry level jobs. What does that mean for the talent pipeline later down the line? And how should we onboard the graduates that *do* get employed so they can add value on top of AI? 📈 AI gets lower performers closer to higher performers (HBS & BCG study), and individuals working with AI match the performance of *teams* without AI (HBS & P&G study). How do we evaluate, recognise and enhance expertise in such a world? 🏁 Vibe coding/marketing/learning/something else, single founder unicorns, service-as-a-software (not software-as-a-service!) and zero latency economy are just some of the predictions that would affect both the nature and pace of work. What support would our people and organisations need to adapt? L&D isn't short on AI tools. What we need is a vision—to imagine how AI will reshape performance, learning, and the world of work at large. And, ultimately, what L&D needs to 𝘣𝘦𝘤𝘰𝘮𝘦 to have a role in it. Nodes #AI #HR #Learning #Talent #FutureOfWork | 12 comments on LinkedIn

In my work with boards and exec teams one of the pointed questions is always the degree of focus in AI initiatives. Johnson & Johnson's divergent-convergent strategy is highly instructive. Some commentators have suggested that this means the use case proliferation was a mistake. J&J's CIO doesn't see it like that. "You had to take an iterative approach to say, ‘Where are these technologies useful and where are they not?’... We had the right plan three years ago, but we matured our plan based on three years of understanding,” Leaders cannot know in advance where the value will emerge. The challenge is to select the right scope of experimenation before selecting focus use cases. Another shift was from centralized AI by a board governance to function-specific ownership such as commercial, R&D, and supply chain, enabling better prioritization and faster iteration. Again, these models suit different phases of the AI adoption journey. Most organizations are far earlier than J&J, which has strong maturity. On metrics: "The company is tracking progress in three buckets: first, the ability to successfully deploy and implement use cases; second, how widely they are adopted; and third, the extent to which they deliver on business outcomes." I strongly suspect that they are not using a "win rate" on their use case success. There are similarities to VC portfolios, where a few big wins make all the investments worthwhile. | 12 comments on LinkedIn

3.000 KI-Assistenten integriert in alle Teams. Das ist die KI-Reise von Moderna: “It’s hard to convey—within the hype—how much AI is changing things and how much Moderna is using it across the board” Dieses Zitat von Wade Davis, Modernas Head of Digital for Business, zeigt sehr schön wie schwer der allumfassende Wandel von KI zu beschreiben ist. Es sind eben nicht 2 - 3 Use Cases ein ein paar Bereichen. Viel mehr geht es um eine Veränderung der Denk- und Arbeitsweise. Während viele Unternehmen noch zögern, hat Moderna bereits konkrete Schritte unternommen, um KI strategisch zu implementieren: 1. Zusammenlegung von HR und IT unter einer Führung 2. Systematische Analyse aller Arbeitsprozesse 3. Klare Entscheidung: Was macht Mensch & Maschine? 4. Entwicklung von 3.000 spezialisierten KI-Assistenten 5. Integration dieser Assistenten in komplexe Workflows Der taktische Ansatz dahinter ist bemerkenswert: ↳ Nicht einzelne KI-Projekte, sondern eine umfassende Transformation ↳ Keine isolierten Tools, sondern vernetzte Systeme ↳ Kein Fokus auf Stellenabbau, sondern auf Neugestaltung der Arbeit KI-Integration ist keine einmalige Initiative, sondern ein fortlaufender Prozess der Organisationsentwicklung. Moderna zeigt, dass der Erfolg nicht von einzelnen Tools abhängt, sondern von der strategischen Neugestaltung der Arbeit selbst. Genau das ist der Weg, den es zu gehen gilt. | 12 Kommentare auf LinkedIn

(I didn't even know that was a thing! 🙃 ) Not able to make attend? Here is everything that was covered from theory to application is now available for free... ➡️ Day 1: Foundational Models & Prompt Engineering https://lnkd.in/d-_w3gXj ➡️ Day 2: Embeddings & Vector Stores / Databases https://lnkd.in/dkmfDUcp ➡️ Day 3: Generative AI Agents https://lnkd.in/dd3Zd2-F ➡️ Day 4: Domain-Specific LLMs https://lnkd.in/d6Z39yqt ➡️ Day 5: MLOps for Generative AI https://lnkd.in/dcXCTPVF And, be sure to check out the winners of the course's capstone project: building tools from Generative AI (classroom assistants, schedulers, mock interviewers and more.) https://lnkd.in/dPsXnrct Interested in putting all of those newly-developed AI skills to use? Here are some of the latest job openings here at Google: http://google.com/careers. Hope to see you around! 😊 #google #lifeatgoogle #training #ai #education | 21 comments on LinkedIn

I was interviewed in today's The Wall Street Journal on the impact of AI agents on customer behavior - here's how I believe our lives are about to change: ( ⬇️ From the article by the great Steve Rosenbush ⬇️ ) There is a flywheel effect at work here. The AI agent has access to an enormous amount of data about users that makes it possible to tailor recommendations, information, and insights to their needs. And once they reside in a messaging app, they can create a continuing presence in the user’s life, just like a person would. “Once an AI knows you and remembers your history, it stops feeling like a tool and starts to feel like a companion,” says Conor Grennan, chief AI architect at New York University Stern School of Business. “It starts to blur the line between an AI brand ambassador and just a friend who shares your taste.”" ⬆️ End of quote ⬆️ . The wild part of all this to me is that agents are coming to WhatsApp, where we hang out. It shows us a ton about Meta's strategy: My thoughts: WhatsApp already hosts most of our everyday conversations, so when a brand drops in an AI agent that greets me like the barista who knows my order, it doesn’t feel like marketing—it feels like service. What’s new is the compounding effect: every helpful, context-aware response deposits a little ‘trust capital’ in the relationship bank. Those micro-interactions can become a moat for a brand by helping establish lasting customer loyalty. So: Where do you see this all going? +++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset would love to help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website. | 56 comments on LinkedIn

Eine empirische Studie zeigt: Der wirtschaftliche Nutzen der generativen KI wird von Führungskräften mit praktischer Erfahrung deutlich positiver bewertet als von solchen ohne. Während 64 Prozent der Erfahrenen von einer schnellen Amortisation ausgehen, glauben dies nur 35 Prozent der Unerfahrenen. Die Wirtschaftlichkeit hängt stark vom Betriebsmodell, der Nutzungstiefe und den unternehmensspezifischen Bedingungen ab. Wer GenAI gezielt einsetzt, steigert Produktivität, Innovationskraft und Arbeitgeberattraktivität – ein realer betriebswirtschaftlicher Vorteil, schreibt Peter Buxmann in seinem Gastbeitrag für F.A.Z. PRO Digitalwirtschaft. 𝗪𝗲𝗶𝘁𝗲𝗿𝗹𝗲𝘀𝗲𝗻: ▶︎ https://lnkd.in/e3faARTd Der Text stammt aus unserem Digitalwirtschaft-Newsletter zur digitalen Ökonomie. Der Newsletter wird jeden Mittwoch um 8 Uhr an 230.000 Abonnenten versendet und erklärt die relevanten Digitalthemen der Woche, aufgeteilt auf die Themenbereiche Künstliche Intelligenz, Zukunft der Arbeit, Digitale Transformation, Plattformen und Digitale Mobilität. Interessenten können den Newsletter zwei Monate 𝗸𝗼𝘀𝘁𝗲𝗻𝗹𝗼𝘀 testen. ▶️ https://lnkd.in/eY_4zwbr Frankfurter Allgemeine Zeitung | 13 Kommentare auf LinkedIn

“𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯. 𝗛𝗲𝗰𝗸, 𝗶𝘁’𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗺𝗶𝗻𝗲 𝘁𝗼𝗼!" That’s not clickbait. That’s the CEO of Fiverr (Micha Kaufman) in his latest e-mail every employee received yesterday! And he’s not the first writing that: first Shopify, then Duolingo, now Fiverr. Top tech CEOs are one by one speaking out loud. BUT Fiverr did something different: They didn’t just warn their teams — they gave them a blueprint to survive. 𝗧𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲 𝘁𝗵𝗲 7 𝗔𝗜 𝗦𝘂𝗿𝘃𝗶𝘃𝗮𝗹 𝗧𝗶𝗽𝘀 𝗵𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝘀 𝗳𝗼𝗿 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝘀𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀: ⬇️ 1. 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁. ➜ Test every AI tool you can get your hands on. See which ones make you 10x faster. If you’re in coding? Use Cursor. Law? Lexis+. Learn what makes you dangerous. 2. 𝗙𝗶𝗻𝗱 𝘀𝗺𝗮𝗿𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝗰𝗹𝗼𝘀𝗲. ➜ Surround yourself with folks who already get AI. Ask questions. Watch what tools they use. Shortcut your learning curve. 3. 𝗨𝘀𝗲 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. ➜ If you're still working like it's 2024, you're doing it wrong (!!!). Speed and efficiency are the new currency. Cut the fluff, automate the rest. 4. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. ➜ Google is basic now. LLM'S are the new baseline. The better your prompts, the more powerful you become. Learn it like it’s your second language. 5. 𝗛𝗲𝗹𝗽 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗱𝗼 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝘀𝘀. ➜ Don’t just do your job — rethink how the whole org works. Automate stuff. Suggest improvements. Be the person who makes things smoother and smarter. 6. 𝗧𝗵𝗶𝗻𝗸 𝗹𝗶𝗸𝗲 𝗮𝗻 𝗼𝘄𝗻𝗲𝗿. Know what the company’s really trying to achieve. Don’t wait to be asked. Show up with ideas. Pitch improvements. You don’t need a permission slip to contribute. 7. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀. No one's coming to save you. If you want to grow, build your own path. Take initiative, start small, stay consistent. Those who help themselves get help too. 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝗿𝗲'𝘀 𝘀𝗼𝗺𝗲 𝘁𝗿𝘂𝘁𝗵 𝘁𝗼 𝘁𝗵𝗶𝘀. 𝗕𝘂𝘁 𝘄𝗲 𝗼𝗳𝘁𝗲𝗻 𝘁𝗲𝗻𝗱 𝘁𝗼 𝗲𝗶𝘁𝗵𝗲𝗿 𝗼𝘃𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗧𝗵𝗮𝘁 𝘀𝗮𝗶𝗱, 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲: 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗰𝗼𝗺𝗶𝗻𝗴 — 𝗶𝘁’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗿𝗲. 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝘀 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗻𝗼𝗿𝗺𝗮𝗹. 𝗦𝘁𝗶𝗹𝗹, 𝘁𝗵𝗲 𝘁𝗶𝗽𝘀 𝗮𝗯𝗼𝘃𝗲 𝗮𝗿𝗲 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲. 𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝗽𝗲𝗿𝗸. 𝗬𝗼𝘂’𝗿𝗲 𝗲𝗶𝘁𝗵𝗲𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 — 𝗼𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗽𝗮𝘀𝘀 𝘆𝗼𝘂 𝗯𝘆. You can read the full e-mail attached! ⬇️ | 152 comments on LinkedIn

*NEW PAPER*: GenAI investments will only pay off if employees adopt the technology and learn to use it effectively. Feelings of psychological threats are common and they are going to be a major obstacle. GenAI deployment therefore needs to be accompanied by a careful talent management strategy. In our new Trends in Cognitive Sciences article, we review the psychological threats that GenAI deployment can trigger in workers, focusing on three areas: competence, autonomy, and relatedness. Moreover, we sketch different types of reactions that such feelings of threats can trigger. The figure below summarizes five coping strategies (both adaptive and maladaptive) for the three types of psychological threat. Link to the article in comment (open access for 50 days). With Erik Hermann and Carey Morewedge The Wharton School Wharton AI & Analytics Initiative Wharton Executive Education | 24 comments on LinkedIn

When I say a ‘traditional course’, I mean learning content (instructor-led or self-service) delivered online or face-to-face either, going from beginning to end with little variation in content or delivery. These courses have been the mainstay of training at work since I started in the classroom in the 1980s. For some, the idea that the traditional course is doomed come as a shock. For others, it’s self-evident. Listening to researchers, experts and practitioners like Dani Johnson, Dr Philippa Hardman and Gregg Collins, I am convinced that within 5 years most organisational training will abandon these traditional courses. Why? It is now easy to personalise content, even in a simple fashion, with AI. With extra effort, you can deliver content via adaptive delivery that understands where you are succeeding and failing and changes what you learn, and how you learn it, to ensure you reach competency faster. It’s more effective, more enjoyable, and faster. All of this is already happening, and it's only going to get easier and more wide-spread. But the real drive will come not from the technology, but from the learners. The technology enables the change. The learners will demand it. Once enough people have experienced content delivered with this flexibility – probably initially in their private lives, as consumers – they will start to ask why their employers aren’t delivering content the same way. So I have three questions for you this Friday: · Do you agree that the traditional course doomed? · If so, is the timescale of 2-5 years reasonable? · What are the implications of all this? I’d love to hear your thoughts. | 77 comments on LinkedIn

New Research: AI vs. Human Coaches - Building Effective Working Relationships This study explores a fascinating question: Can AI coaches of the future build effective working relationships with clients comparable to human coaches? Surprisingly, the answer is yes. Part of my dissertation research study at Teachers College, Columbia University was recently published in an Advancing Coaching Scholarship special issue alongside other prominent scholars. With AI increasingly entering human-centered spaces like coaching, this research offers early insight into its impact. Through a randomized controlled experiment, I found that people could establish strong connections with both simulated autonomous AI and human coaches in just a single hour-long session. The data showed comparable relationship quality metrics across both conditions, with individuals specifically valuing the collaborative, goal-oriented conversation regardless of coach type. Read the full study here to explore what this means for the future of coaching. #AICoaching https://lnkd.in/g4W7i8dx | 31 comments on LinkedIn

it's also reducing the wage premium of a college degree. This is why the #Superworker strategy is so urgent. https://lnkd.in/g8V9aHxN “Law firms lean on AI for paralegal work as consulting firms find that five 22-year-olds with ChatGPT can do the work of 20 recent grads." "Tech firms are turning over their software programming to a handful of superstars working with AI co-pilots." "The share of jobs posted on Indeed in software programming has declined by more than 50 percent since 2022." "And even if employers aren’t directly substituting AI for human workers, spending on AI infrastructure is crowding out spending on new hires.” | 172 comments on LinkedIn

𝗢𝗽𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗕𝗜𝗚 — 𝗮𝗻𝗱 𝗯𝗮𝗿𝗲𝗹𝘆 𝗮𝗻𝘆𝗼𝗻𝗲 𝗶𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁! Yesterday, I spent a few hours diving into the newly launched "𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆". And it's an absolute goldmine of FREE AI education, packed with tutorials, live workshops, labs and real-world case studies. Whether you're just starting or already building with GPTs — there’s definitely something here for you. And it’s all 100% FREE and beginner-friendly tracks (no code needed). Here is some stuff to have an eye on: 𝗨𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – Introduction to ChatGPT: https://lnkd.in/e4dgUbWj – AI in Action: Uses for Work, Learning & Life: https://lnkd.in/efXpXY_9 𝗔𝗿𝗰𝗵𝗶𝘃𝗲𝗱 𝗪𝗲𝗯𝗶𝗻𝗮𝗿𝘀: – ChatGPT 101: A Guide to Your Super Assistant: https://lnkd.in/e6RJMcEC – ChatGPT 102: Using AI to Do Your Best Work: https://lnkd.in/eF4iQfFz – Advanced Prompt Engineering: https://lnkd.in/eb9JGYkY 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗮𝘁 𝗪𝗼𝗿𝗸 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – ChatGPT Search: https://lnkd.in/e8fRSkPT – ChatGPT for Data Analysis: https://lnkd.in/ezssYnGk – Introduction to GPTs: https://lnkd.in/eiUCDF9u 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗼𝗻 𝗖𝗮𝗺𝗽𝘂𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – AI for Academic Success: https://lnkd.in/e9hPwRsF – AI for Career Prep: Resumes & Interviews: https://lnkd.in/ezK62jzQ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗕𝘂𝗶𝗹𝗱 𝗛𝗼𝘂𝗿𝘀 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: – Fine-Tuning: https://lnkd.in/e2iqWD7J – Assistants & Agents: https://lnkd.in/em6FBu2Q Link to the academy: https://lnkd.in/d8GK4sC4 Definitely very interesting to see that OpenAI is now also building their own learning ecosystem. ENJOY! | 58 comments on LinkedIn

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

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."

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.