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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

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 #...

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

KI-Anwendungen aufnehmen und klassifizieren: Aber wie? Der AI Act verlangt eine Risikoklassifizierung von KI-Anwendungen und Systemen im Unternehmen. Dafür…

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

In dieser Episode berichtet Dr. Bettina Laugwitz, die bei SAP das Thema KI-Ethik verantwortet, von SAPs Ansatz zur ethischen Entwicklung und Implementierung von künstlicher Intelligenz.

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

One of my favourite reads from the last six months is Sequoia Capital’s report exploring the evolution of generative AI and it’s implications for the messy…

"𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀!" 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗗𝗿𝗲𝗮𝗺: ➜ Deploy AI Agents ➜ Automate everything ➜ Enjoy efficiency 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 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

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Das KI-System "The AI Scientist-v2" von Sakana AI hat eine wissenschaftliche Publikation erstellt, die den Peer-Review-Prozess bei einem Workshop der wichtigen…

🌟 KI-generierte Inhalte und Urheberrecht: Wer haftet bei Rechtsverletzungen? Stell dir vor, du nutzt eine generative KI, um ein Marketing-Bild zu erstellen.… | 16 comments on LinkedIn

Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit wenigen Tagen geistert Manus AI durch meinen Feed. Warte noch sehnsüchtig 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 verknüpft. "Manus" als die KI "Hand", die einem tatkräftig im digitalen Alltag hilft. 💪

Thought is a multi-step process, but rarely linear. Early LLMs lacked structured reasoning and often struggled with logic. Chain-of-Thought introduced…

🚂 Erfahrt in der neuen AI 4 L&D Podcastfolge, wie DB Lingua die Kommunikation und Effizienz zwischen Fahrdienstleitern und Triebfahrzeugführern verbessert u...

2025 is the Year of LCMs and not LLMs.   Meta has announced a new architecture for the future of Large Language Models called Large Concept Models.   Building… | 103 comments on LinkedIn

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