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One of the most interesting things about the original research is that it provides a clear metric for measuring AI performance improvement that is not tied to benchmarks that keep on being superceded, needing new benchmarks. We can now rank AI models and agents by their half-life - the time for human tasks for which they achieve 50% success rate. Of course we are usually more interested in models that can achieve 99+% success rates - depending on the task - but the relative consistency of the half life decay means the T50 threshold predicts whatever success rate we aim for, both today, and at future dates if the original trend holds Generally the decay is due to cumulative errors or going off course. But the decay is not always consistent, as there can be subtasks of uneven difficulty, or agents can recover from early mistakes. Interestingly, humans don't follow pure exponential decay curves. Our success rate falls off more slowly over very long tasks, suggesting we have broader context, allowing us to recover from early mistakes. The research was applied to tasks in research or software engineering. The dynamics of this performance evolution may or may not apply to other domains. Certainly, this reframing of assessing the development of AI capabilities and its comparison to human work is a very useful advance to the benchmarking approach.

Here’s what I take away: 🚫 HR is no longer just about people. 🚫 IT is no longer just about systems. ✅ The real value lies in how people and systems interact—seamlessly, intelligently, adaptively. Let’s be honest, most organizations still operate in silos: - HR builds talent and culture - IT builds systems and infrastructure But the future of work is all about integration. What if you make that happen? Think about it: Can you redesign work itself? Not roles. Not org charts. But the actual FLOW of work. Because that’s what Moderna’s doing. They are reimagining how humans and machines co-create value. IBM is doing the same. They use HR AI agents that handle questions, routes issues, and manage HR processes. This isn’t about cutting costs. It’s about building a business that adapts faster to the next disruption. They are building resilience. I recognize that HR and IT both have unique complexities, and in many companies are simply too far apart or too large merge shortly. Still, it still got me thinking. As an HR leader: -> How comfortable are you with data, automation, and AI? -> Could you confidently lead both people strategy and digital infrastructure? -> What would need to change for that answer to be yes? This isn’t a tech conversation. It’s an organization and leadership revolution. The next era of HR won’t be like today's HR at all. It will be integrated, tech-savvy, and central to how business gets done. Time to level up. Are you ready? #futureofwork #hrtech #ai Picture and story credits: Isabelle Bousquette 🙏 | 34 comments on LinkedIn

switching to non-augmented tasks. It is definitely interesting research, but I am very cautious about the conclusions reached by the authors, partly since they are to a degree contradictory, and also not necessarily generalizable. The authors implicitly criticize AI for removing the “most cognitively demanding parts” of work, implying that this reduces fulfillment. But the outputs and productivity are clearly improved. Are they suggesting workers create inferior output for the sake of engagement? It is worth noting that other recent research points to improved emotion and engaement with genAI collaboration. The emotional impact of genAI collaboration will vary substantially across use cases, especially with the nature of the task, and certainly with the cultural context. It appears the use case here was performance reviews, which is not representative of many other types of cognitive work. The authors also say that AI-assisted tasks reduce users’ sense of control, thus lowering motivation. But they say this sense of control is restored during subsequent solo tasks, even though those are when boredom and disengagement rise. Having said that, for some tasks and work design the issues they raise could be real and substantial. These are the sound remedies they suggest: ➡️Blend AI and Human Contributions: Use gen AI as a foundation for tasks while encouraging humans to personalize, expand, and refine outputs to retain creativity and ownership. ➡️Design Engaging Solo Tasks: Follow AI-supported work with autonomous, creative tasks to help employees stay motivated and exercise their own skills. ➡️Make AI Collaboration Transparent: Clearly communicate AI’s supporting role to preserve employees’ sense of control and fulfillment in their contributions. ➡️Rotate Between Tasks: Alternate between independent and AI-assisted tasks to maintain engagement and productivity throughout the workday. ➡️Train Employees to Use AI Mindfully: Provide training that helps employees critically and strategically integrate AI, strengthening their autonomy and judgment.

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

Norwegen, Schweden und Finnland haben viele Dinge gemeinsam: Lange Küsten, viele Wälder und eine ausgeprägte soziale Tradition. Alle drei nordischen Länder s...

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

We used to rely on each other, but then it went a bit sour. Or did it? AI has helped ramp that up too. The context of work ie hybrid and remote has also driven the need for providing digital learning solutions. So what happened to face to face training? It was the staple of L&D pre-Covid but now it is hardly talked about. Have you seen articles or conference sessions on the topic? Latest research from Hemsley Fraser suggests the in-person training is far from forgotten. Indeed, it is the number one learning approach, according to its survey of 822 L&D, HR and talent practitioners, followed by coaching and virtual instructor-led training. The research shows that there has been a 17% year on year growth in in-person training in the US. According to the research, the learning approaches used by L&D are: ⦿ In-person training 87% ⦿ Coaching 62% ⦿ Virtual instructor-led training 60% ⦿ Free online resources 56% ⦿ Learning hub/portal 54% ⦿ Blended learning 51% The report authors say, "When L&D is expected to not only grow skills but boost engagement and experiences, coming together, when structured effectively, can simply make people feel good. It becomes a strategic tool with benefits beyond training alone." This sentiment is reflected in the latest Fosway Group 9-Grid for Digital Learning. One of its trends is titled "The desire to learn together gains momentum". The report, says: "Amongst all this talk of AI, the desire to actually learn together (with other people) remains, arguably, valued more highly. The need for learning solutions for high-value or large-scale transformation programmes is enduring. The best solutions bring cohorts to learn and work together over time. And maybe unsurprisingly, live events are still an important part of the desired blend, as well as virtual and hybrid approaches, led by experts that can interact in real time with their audience. In-group facilitation and collaboration is another core capability." So what is the reality for L&D teams? Is in-person training evolving to find its place alongside learning technology? And is the in-person experience evolving away from content delivery to focus on the value of people being together, sharing and working and learning collaboratively? 🔗 Read the Hemsley Fraser Learning & Development Impact Survey 2025 https://lnkd.in/e33fuTp2 🔗 Read the 2025 Fosway 9-Grid for Digital Learning https://lnkd.in/ebAbqd_x Picture credit: ElisaRiva https://lnkd.in/epFrUtXd #learninganddevelopment #research | 23 comments on 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

Progress is happening all over the world. I couldn’t fit the entire world into my studio though, so built a model. See how I did that with a Nebula subscript...

STREAM MY MUSIC: https://linktr.ee/nomadicamusicINSTA: / xnomadicax EMAIL: emswildworld@gmail.com

Werbung: Am Mittwoch, 14.05.2025 moderiere ich eine Talk-Runde mit Akteuren aus der Wissenschaft und Changemakern rund um das Thema Greenovations - also Inno...

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

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...