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🀖 Interesting insights from Anthropicnes recent study on how university students are leveraging AI! 📈
🀖 Interesting insights from Anthropicnes recent study on how university students are leveraging AI! 📈
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
·linkedin.com·
🀖 Interesting insights from Anthropicnes recent study on how university students are leveraging AI! 📈
𝗧𝗌𝗜 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗌𝘂 𝗊𝗵𝗌𝘂𝗹𝗱 𝗞𝗻𝗌𝘄
𝗧𝗌𝗜 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗌𝘂 𝗊𝗵𝗌𝘂𝗹𝗱 𝗞𝗻𝗌𝘄
𝗧𝗌𝗜 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗌𝘂 𝗊𝗵𝗌𝘂𝗹𝗱 𝗞𝗻𝗌𝘄 — 𝗘𝘅𝗜𝗹𝗮𝗶𝗻𝗲𝗱 𝗊𝗶𝗺𝗜𝗹𝘆 𝟭. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗎𝗲 𝗟𝗮𝗻𝗎𝘂𝗮𝗎𝗲 𝗠𝗌𝗱𝗲𝗹) → Helps computers understand and write human-like text → Examples: GPT-4, Claude, Gemini → Used in: Chatbots, coding tools, content generation 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗳𝗌𝗿𝗺𝗲𝗿𝘀 → The tech behind all modern AI models → Let models understand meaning, context, and order of words → Examples: BERT, GPT 𝟯. 𝗣𝗿𝗌𝗺𝗜𝘁 𝗘𝗻𝗎𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗎 → Writing better instructions to get better AI answers → Includes system prompts, step-by-step prompts, and safety rules 𝟰. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗎 → Training an AI model on your data → Helps tailor it for specific tasks like legal, medical, or financial use cases 𝟱. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗎𝘀 → A way for AI to understand meaning and relationships between words or documents → Used in search engines and recommendation systems 𝟲. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗎𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗌𝗻) → Combines AI with a database or document store → Helps AI give more accurate, fact-based answers 𝟳. 𝗧𝗌𝗞𝗲𝗻𝘀 → The chunks of text AI reads and writes → Managing them controls cost and performance 𝟎. 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗌𝗻 → When AI gives wrong or made-up answers → Can be fixed with fact-checking and better prompts 𝟵. 𝗭𝗲𝗿𝗌-𝗊𝗵𝗌𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗎 → When AI can perform a task without being trained on it → Saves time on training 𝟭𝟬. 𝗖𝗵𝗮𝗶𝗻-𝗌𝗳-𝗧𝗵𝗌𝘂𝗎𝗵𝘁 → AI explains its answer step-by-step → Helps with complex reasoning tasks 𝟭𝟭. 𝗖𝗌𝗻𝘁𝗲𝘅𝘁 𝗪𝗶𝗻𝗱𝗌𝘄 → The amount of info AI can see at once → Larger windows help with longer documents or conversations 𝟭𝟮. 𝗧𝗲𝗺𝗜𝗲𝗿𝗮𝘁𝘂𝗿𝗲 → Controls how creative or predictable AI is → Lower values = more accurate; higher values = more creative 𝗪𝗵𝗮𝘁’𝘀 𝗖𝗌𝗺𝗶𝗻𝗎 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 𝘶𝘎𝘊 them to build better solutions. | 51 comments on LinkedIn
·linkedin.com·
𝗧𝗌𝗜 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗲𝗿𝗺𝘀 𝗬𝗌𝘂 𝗊𝗵𝗌𝘂𝗹𝗱 𝗞𝗻𝗌𝘄
𝗢𝗜𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗜𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗌𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗣𝗧-4.1 𝗜𝗿𝗌𝗺𝗜𝘁𝗶𝗻𝗎 𝗎𝘂𝗶𝗱𝗲! It provides a detailed guide on how to steer GPT-4.1 with precision, including examples, tips, and advanced techniques.
𝗢𝗜𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗜𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗌𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗣𝗧-4.1 𝗜𝗿𝗌𝗺𝗜𝘁𝗶𝗻𝗎 𝗎𝘂𝗶𝗱𝗲! It provides a detailed guide on how to steer GPT-4.1 with precision, including examples, tips, and advanced techniques.
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 𝘀𝗵𝗌𝗿𝘁: 𝗧𝗵𝗲 𝗞𝗲𝘆 𝘁𝗌 𝘂𝘀𝗶𝗻𝗎 𝗚𝗣𝗧-4.1 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗜𝗿𝗌𝗺𝗜𝘁𝘀 𝘁𝗵𝗮𝘁 𝗎𝘂𝗶𝗱𝗲 𝗶𝘁 𝘁𝗌𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗿𝗶𝗎𝗵𝘁 𝗮𝗻𝘀𝘄𝗲𝗿. 𝗜𝘁’𝘀 𝗮𝗹𝗹 𝗮𝗯𝗌𝘂𝘁 𝗮𝘀𝗞𝗶𝗻𝗎 𝘁𝗵𝗲 𝗿𝗶𝗎𝗵𝘁 𝗟𝘂𝗲𝘀𝘁𝗶𝗌𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗎𝗵𝘁 𝘄𝗮𝘆! Access it here or download it below: https://lnkd.in/dCm6DeFW | 59 comments on LinkedIn
·linkedin.com·
𝗢𝗜𝗲𝗻𝗔𝗜 𝗷𝘂𝘀𝘁 𝗜𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝗌𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗣𝗧-4.1 𝗜𝗿𝗌𝗺𝗜𝘁𝗶𝗻𝗎 𝗎𝘂𝗶𝗱𝗲! It provides a detailed guide on how to steer GPT-4.1 with precision, including examples, tips, and advanced techniques.
97% of you are probably blissfully unaware of AI agents. However, they’re here and evolving fast!
97% of you are probably blissfully unaware of AI agents. However, they’re here and evolving fast!
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: 1⃣ 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. 2⃣ 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. 3⃣ 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. 4⃣ 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. 5⃣ Hierarchical This is like an org chart. Big boss on top, middle managers below, then the doers. Neat, structured, scalable. 6⃣ 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
·linkedin.com·
97% of you are probably blissfully unaware of AI agents. However, they’re here and evolving fast!
2023: „AI wird meinen Job ersetzen.“ 2024: „AI ist mein Copilot.“ 2025: „AI ersetzt keine Freundschaft.
2023: „AI wird meinen Job ersetzen.“ 2024: „AI ist mein Copilot.“ 2025: „AI ersetzt keine Freundschaft.
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: _FÃŒhrungskrÀfte nutzen GPTs zur Strategieentwicklung. _Manager bauen sich ihre eigenen Helferlings. _Entwickler sparen 56 % Zeit beim Coden. _Teams automatisieren repetitive Tasks – und schaffen Raum fÃŒr KreativitÀt. Die Grenze zwischen „Business Use Case“ und „Private Use Case“ verschwimmt zunehmend. Workflows, die am KÃŒchentisch anfangen und in der Vorstandsetage landen. Die spannendste Erkenntnis: Der meistgenutzte GenAI-Use-Case 2025 ist - Nicht Coding. Nicht PrÀsentationen. Nicht Strategie. Sondern: Therapie. 🧠 GesprÀche mit Chatbots ÃŒber Stress, Selbstzweifel, Sinnfragen. 📓 Journaling mit KI als stillem GegenÃŒber. 🪞Selbstreflexion – strukturiert, aber menschlich. Laut HBR ist „mentale Gesundheit“ als Use Case fÃŒr 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 geschÃŒtzter Raum zum Denken, Reden, FÃŒhlen. Und dass KI vielleicht nicht nur Arbeit, sondern auch Zugang demokratisiert – zu Support, der vorher unerschwinglich war. 🌀 Vielleicht ist das die eigentliche Disruption.
·linkedin.com·
2023: „AI wird meinen Job ersetzen.“ 2024: „AI ist mein Copilot.“ 2025: „AI ersetzt keine Freundschaft.
KI-Agenten werden zum Bildungscoach der Zukunft
KI-Agenten werden zum Bildungscoach der Zukunft
Bei KÃŒnstlicher 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 PrÃŒfungsformen noch zeitgemÀß? Sollen Dozenten zeigen, wie KI benutzt werden kann und s
·linkedin.com·
KI-Agenten werden zum Bildungscoach der Zukunft
I ve rolled out AI to 30+ companies and 25,000+ employees. Here is what I learned.
I ve rolled out AI to 30+ companies and 25,000+ employees. Here is what I learned.
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
·linkedin.com·
I ve rolled out AI to 30+ companies and 25,000+ employees. Here is what I learned.
Friends, this is the MOST IMPORTANT study on AI in 2025. The brilliant Ethan Mollick and team studied how AI impacts individuals and teams across Procter & Gamble - the results are stunning.
Friends, this is the MOST IMPORTANT study on AI in 2025. The brilliant Ethan Mollick and team studied how AI impacts individuals and teams across Procter & Gamble - the results are stunning.
Friends, this is the MOST IMPORTANT study on AI in 2025. The brilliant Ethan Mollick and team studied how AI impacts individuals and teams across Procter & Gamble- the results are stunning. Here’s what you need to know: The "Cybernetic Teammate" study was conducted in Summer 2024 by a research from Harvard and Wharton, in partnership with Procter & Gamble.  ++++++++++++++++++++ WHO WAS TESTED: The study involved 776 P&G professionals and replicated P&G's product development process across four business units. The experiment featured four distinct conditions: - Individuals working alone without AI - Individuals working alone with AI - Teams of two specialists (one commercial expert, one technical R&D expert) working without AI - Teams of two specialists working with AI ++++++++++++++++++++ KEY FINDINGS: INDIVIDUAL PERFORMANCE: AI improved individual performance by 37% TEAM PERFORMANCE: AI improved team performance by 39% BREAKTHROUGH SOLUTIONS: Teams using AI were 3x more likely to produce solutions in top 10% of quality EFFICIENCY GAINS Individuals using AI completed tasks 16.4% faster than those without Teams with AI finished 12.7% faster than teams without AI OUTPUT QUALITY Despite working faster, AI-enabled groups produced substantially longer and more detailed solutions EXPERTISE AND COLLABORATION EFFECTS Breaking Down Silos!! Without AI: Clear professional silos existed — R&D specialists created technical solutions while Commercial specialists developed market-focused ideas With AI: Distinctions virtually disappeared — both types of specialists produced balanced solutions integrating technical and commercial perspectives EXPERIENCE LEVELING: Less experienced employees using AI performed at levels comparable to teams with experienced members EMOTIONAL EXPERIENCE Positive Emotions: AI users reported significantly higher levels of excitement, energy, and enthusiasm Negative Emotions: AI users experienced less anxiety and frustration during work Individual Experience: People working alone with AI reported emotional experiences comparable to or better than those in human teams TEAM DYNAMICS Solution Types: Teams without AI showed a bimodal distribution (either technically or commercially oriented solutions) Balanced Input: AI appeared to reduce dominance effects, allowing more equal contribution from team members Consistency: Teams with AI showed more uniform, high-quality outputs compared to the variable results of standard teams We'll be talking about this study for a while. +++++++++++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. Check out our website or shoot me a DM. | 133 comments on LinkedIn
·linkedin.com·
Friends, this is the MOST IMPORTANT study on AI in 2025. The brilliant Ethan Mollick and team studied how AI impacts individuals and teams across Procter & Gamble - the results are stunning.
Sehr spannende Studie von der Harvard Business School (03/2025) die aufzeigt, dass der Einsatz generativer KI (GenAI) die zentralen Aspekte von Teamarbeit

Sehr spannende Studie von der Harvard Business School (03/2025) die aufzeigt, dass der Einsatz generativer KI (GenAI) die zentralen Aspekte von Teamarbeit

Sehr spannende Studie von der Harvard Business School (03/2025) die aufzeigt, dass der Einsatz generativer KI (GenAI) die zentralen Aspekte von Teamarbeit

·linkedin.com·
Sehr spannende Studie von der Harvard Business School (03/2025) die aufzeigt, dass der Einsatz generativer KI (GenAI) die zentralen Aspekte von Teamarbeit

𝗔𝗜 𝗔𝗎𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗌𝗹𝘂𝘁𝗶𝗌𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝘄𝗌𝗿𝗞𝗳𝗹𝗌𝘄𝘀! Or Not? And what about the data?
𝗔𝗜 𝗔𝗎𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗌𝗹𝘂𝘁𝗶𝗌𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝘄𝗌𝗿𝗞𝗳𝗹𝗌𝘄𝘀! Or Not? And what about the data?
"𝗔𝗜 𝗔𝗎𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗌𝗹𝘂𝘁𝗶𝗌𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝘄𝗌𝗿𝗞𝗳𝗹𝗌𝘄𝘀!" 𝗘𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝗔𝗜 𝗗𝗿𝗲𝗮𝗺: ➜ Deploy AI Agents ➜ Automate everything ➜ Enjoy efficiency 𝗘𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝗔𝗜 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: ➜ Messy, siloed, unreliable data ➜ AI hallucinations & compliance nightmares ➜ Enterprise AI initiatives stall as organizations spend more time fixing data issues than realizing AI-driven value. The Hard Truth: AI (agents) aren't failing—data strategies are. AI Agents are only as effective as the data beneath them. Without governed, high-quality data, AI adoption becomes an expensive experiment instead of a strategic advantage. Important to fix the data first. Kudos for this image to Armand Ruiz! | 258 comments on LinkedIn
·linkedin.com·
𝗔𝗜 𝗔𝗎𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗌𝗹𝘂𝘁𝗶𝗌𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗜𝗿𝗶𝘀𝗲 𝘄𝗌𝗿𝗞𝗳𝗹𝗌𝘄𝘀! Or Not? And what about the data?
Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit
 | Maks Giordano
Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit
 | Maks Giordano
Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit wenigen Tagen geistert Manus AI durch meinen Feed. Warte noch sehnsÃŒchtig auf den Access, aber was man bereits sehen kann in diversen Demos macht richtig Lust drauf: General AI Agent als quasi Mischung aus Claude Computer Use, Chat GPT Operator, Deep Research etc und das ganze extrem intelligent miteinander verknÃŒpft. "Manus" als die KI "Hand", die einem tatkrÀftig im digitalen Alltag hilft. 💪
·linkedin.com·
Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit
 | Maks Giordano
Felix Schlenther auf LinkedIn: 17x interne KI Chatbots / CompanyGPTs eingefÃŒhrt. Diese 11 Lektionen habe

Felix Schlenther auf LinkedIn: 17x interne KI Chatbots / CompanyGPTs eingefÃŒhrt. Diese 11 Lektionen habe

17x interne KI Chatbots / CompanyGPTs eingefÃŒhrt. Diese 11 Lektionen habe ich gelernt: 𝗗𝗮𝘀 "𝗪𝗮𝗿𝘂𝗺" 𝘇𝗮̈𝗵𝗹𝘁. Was ist das Ziel der KI-Initiative?
 | 54 Kommentare auf LinkedIn
·linkedin.com·
Felix Schlenther auf LinkedIn: 17x interne KI Chatbots / CompanyGPTs eingefÃŒhrt. Diese 11 Lektionen habe

Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele
 | Matthias Kindt
Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele
 | Matthias Kindt
Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele Beteiligte mit Tech-Hintergrund, die super Abbildungen erstellen und damit nicht selten sehr hohe Reichweiten erzielen. Genau diese Art der Wissenschaftskommunikation kommt besonders gut an. Zum Linkedin-Post https://lnkd.in/eq4mWQFd
·linkedin.com·
Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele
 | Matthias Kindt