Open New Learning Lab Resources

Open New Learning Lab Resources

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๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. See our paper for more results:ย "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (link in the comments). For 4 months, 54 students were divided into three groups: ChatGPT, Google -ai, and Brain-only. Across 3 sessions, each wrote essays on SAT prompts. In an optional 4th session, participants switched: LLM users used no tools (LLM-to-Brain), and Brain-only group used ChatGPT (Brain-to-LLM). ๐Ÿ‘‡ ๐ˆ. ๐๐‹๐ ๐š๐ง๐ ๐„๐ฌ๐ฌ๐š๐ฒ ๐‚๐จ๐ง๐ญ๐ž๐ง๐ญ - LLM Group: Essays were highly homogeneous within each topic, showing little variation. Participants often relied on the same expressions or ideas. - Brain-only Group: Diverse and varied approaches across participants and topics. - Search Engine Group: Essays were shaped by search engine-optimized content; their ontology overlapped with the LLM group but not with the Brain-only group. ๐ˆ๐ˆ. ๐„๐ฌ๐ฌ๐š๐ฒ ๐’๐œ๐จ๐ซ๐ข๐ง๐  (๐“๐ž๐š๐œ๐ก๐ž๐ซ๐ฌ ๐ฏ๐ฌ. ๐€๐ˆ ๐‰๐ฎ๐๐ ๐ž) - Teachersย detected patterns typical of AI-generated content and scoring LLM essays lower for originality and structure. - AI Judgeย gave consistently higher scores to LLM essays, missing human-recognized stylistic traits. ๐ˆ๐ˆ๐ˆ: ๐„๐„๐† ๐€๐ง๐š๐ฅ๐ฒ๐ฌ๐ข๐ฌ Connectivity: Brain-only group showed the highest neural connectivity, especially in alpha, theta, and delta bands. LLM users had the weakest connectivity, up to 55% lower in low-frequency networks. Search Engine group showed high visual cortex engagement, aligned with web-based information gathering. ๐‘บ๐’†๐’”๐’”๐’Š๐’๐’ 4 ๐‘น๐’†๐’”๐’–๐’๐’•๐’”: - LLM-to-Brain (๐Ÿค–๐Ÿค–๐Ÿค–๐Ÿง ) participantsย underperformed cognitively with reduced alpha/beta activity and poor content recall. - Brain-to-LLM (๐Ÿง ๐Ÿง ๐Ÿง ๐Ÿค–) participantsย showed strong re-engagement, better memory recall, and efficient tool use. LLM-to-Brain participants had potential limitations in achieving robust neural synchronization essential for complex cognitive tasks. Results forย Brain-to-LLM participantsย suggest that strategic timing of AI tool introduction following initial self-driven effort may enhance engagement and neural integration. ๐ˆ๐•. ๐๐ž๐ก๐š๐ฏ๐ข๐จ๐ซ๐š๐ฅ ๐š๐ง๐ ๐‚๐จ๐ ๐ง๐ข๐ญ๐ข๐ฏ๐ž ๐„๐ง๐ ๐š๐ ๐ž๐ฆ๐ž๐ง๐ญ - Quoting Ability: LLM users failed to quote accurately, while Brain-only participants showed robust recall and quoting skills. - Ownership: Brain-only group claimed full ownership of their work; LLM users expressed either no ownership or partial ownership. - Critical Thinking: Brain-only participants cared more aboutย ๐˜ธ๐˜ฉ๐˜ข๐˜ตย andย ๐˜ธ๐˜ฉ๐˜บย they wrote; LLM users focused onย ๐˜ฉ๐˜ฐ๐˜ธ. - Cognitive Debt: Repeated LLM use led to shallow content repetition and reduced critical engagement. This suggests a buildup of "cognitive debt", deferring mental effort at the cost of long-term cognitive depth. Support and share! โค๏ธ #MIT #AI #Brain #Neuroscience #CognitiveDebt | 54 comments on LinkedIn
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๐๐จ, ๐ฒ๐จ๐ฎ๐ซ ๐›๐ซ๐š๐ข๐ง ๐๐จ๐ž๐ฌ ๐ง๐จ๐ญ ๐ฉ๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ ๐›๐ž๐ญ๐ญ๐ž๐ซ ๐š๐Ÿ๐ญ๐ž๐ซ ๐‹๐‹๐Œ ๐จ๐ซ ๐๐ฎ๐ซ๐ข๐ง๐  ๐‹๐‹๐Œ ๐ฎ๐ฌ๐ž. | Nataliya Kosmyna, Ph.D
Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
Na endlich! Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen. CustomGPTs sind fรผr mich das beste Feature in ChatGPT und wurden in den letzten 12 Monaten stark vernachlรคssigt. Mit der Modell-Auswahl kommt jetzt das erste gute Upgrade. Mini-Guide zur Modell-Auswahl: o3 -> Komplexe Problemstellungen und Datenanalyse 4.5 -> Kreative Aufgaben und Copywriting 4o -> Bild-Verarbeitung 4.1 -> Coding Alle anderen Modelle benรถtigt man mMn nicht. Mein Strategieberater bekommt zum Beispiel o3 hinterlegt (bessere Planungsfรคhigkeit in komplexen Aufgaben), wohingegen der Hook Writer GPT4.5 bekommt (besserer Schreibstil). Wenn du die CustomGPTs selbst nutzen willst: 80+ Vorlagen frei verfรผgbar in unserer Assistenten-Datenbank ๐Ÿ‘‡ P.S. Wie findest du das Update? | 15 Kommentare auf LinkedIn
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Du kannst jetzt das passende Modell fรผr deinen CustomGPT auswรคhlen.
BOOM!
BOOM!
BOOM! Microsoft just dropped a FREE 18-episode series on Generative AI. Ideal for people who are new to AI & wanna start learning. Here are 5 episodes that stood out ๐—œ๐˜ ๐˜„๐—ถ๐—น๐—น ๐˜๐—ฎ๐—ธ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—น๐—ฒ๐˜€๐˜€ ๐˜๐—ต๐—ฎ๐—ป ๐Ÿญ.๐Ÿฑ ๐—ต๐—ผ๐˜‚๐—ฟ๐˜€ ๐˜๐—ผ ๐˜„๐—ฎ๐˜๐—ฐ๐—ต ๐—ฎ๐—น๐—น ๐˜๐—ต๐—ฒ๐˜€๐—ฒ: ๐Ÿ‘‰ Introduction to Generative AI and LLMs https://lnkd.in/dxds5CXY ๐Ÿ‘‰ Exploring and Comparing Different LLMs https://lnkd.in/dnu5sP68 ๐Ÿ‘‰ Understanding Prompt Engineering Fundamentals https://lnkd.in/d8t56acG ๐Ÿ‘‰ Building Low-Code AI Applications https://lnkd.in/dKVXmdeK ๐Ÿ‘‰ AI Agents โ€“ Introduces AI Agents, where LLMs can take actions via tools or frameworks. https://lnkd.in/d8VKw7Ve More resources are in the comments. Repost this post to help others in your network. | 91 comments on LinkedIn
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BOOM!
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€! โฌ‡๏ธ Letโ€™s clarify it once and for all: โฌ‡๏ธ 1. ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€: ๐—ง๐—ผ๐—ผ๐—น๐˜€ ๐˜„๐—ถ๐˜๐—ต ๐—”๐˜‚๐˜๐—ผ๐—ป๐—ผ๐—บ๐˜†, ๐—ช๐—ถ๐˜๐—ต๐—ถ๐—ป ๐—Ÿ๐—ถ๐—บ๐—ถ๐˜๐˜€ โžœ AI agents are modular, goal-directed systems that operate within clearly defined boundaries. Theyโ€™re built to: * Use tools (APIs, browsers, databases) * Execute specific, task-oriented workflows * React to prompts or real-time inputs * Plan short sequences and return actionable outputs ๐˜›๐˜ฉ๐˜ฆ๐˜บโ€™๐˜ณ๐˜ฆ ๐˜ฆ๐˜น๐˜ค๐˜ฆ๐˜ญ๐˜ญ๐˜ฆ๐˜ฏ๐˜ต ๐˜ง๐˜ฐ๐˜ณ ๐˜ต๐˜ข๐˜ณ๐˜จ๐˜ฆ๐˜ต๐˜ฆ๐˜ฅ ๐˜ข๐˜ถ๐˜ต๐˜ฐ๐˜ฎ๐˜ข๐˜ต๐˜ช๐˜ฐ๐˜ฏ, ๐˜ญ๐˜ช๐˜ฌ๐˜ฆ: ๐˜Š๐˜ถ๐˜ด๐˜ต๐˜ฐ๐˜ฎ๐˜ฆ๐˜ณ ๐˜ด๐˜ถ๐˜ฑ๐˜ฑ๐˜ฐ๐˜ณ๐˜ต ๐˜ฃ๐˜ฐ๐˜ต๐˜ด, ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ๐˜ข๐˜ญ ๐˜ฌ๐˜ฏ๐˜ฐ๐˜ธ๐˜ญ๐˜ฆ๐˜ฅ๐˜จ๐˜ฆ ๐˜ด๐˜ฆ๐˜ข๐˜ณ๐˜ค๐˜ฉ, ๐˜Œ๐˜ฎ๐˜ข๐˜ช๐˜ญ ๐˜ต๐˜ณ๐˜ช๐˜ข๐˜จ๐˜ฆ, ๐˜”๐˜ฆ๐˜ฆ๐˜ต๐˜ช๐˜ฏ๐˜จ ๐˜ด๐˜ค๐˜ฉ๐˜ฆ๐˜ฅ๐˜ถ๐˜ญ๐˜ช๐˜ฏ๐˜จ, ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ด๐˜ถ๐˜จ๐˜จ๐˜ฆ๐˜ด๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด But even the most advanced are limited by scope. They donโ€™t initiate. They donโ€™t collaborate. They execute what we ask! 2. ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ: ๐—” ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ ๐—ผ๐—ณ ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ โžœ Agentic AI is an architectural leap. Itโ€™s not just one smarter agent โ€” itโ€™s multiple specialized agents working together toward shared goals. These systems exhibit: * Multi-agent collaboration * Goal decomposition and role assignment * Inter-agent communication via memory or messaging * Persistent context across time and tasks * Recursive planning and error recovery * Distributed orchestration and adaptive feedback Agentic AI systems donโ€™t just follow instructions. They coordinate. They adapt. They manage complexity. ๐˜Œ๐˜น๐˜ข๐˜ฎ๐˜ฑ๐˜ญ๐˜ฆ๐˜ด ๐˜ช๐˜ฏ๐˜ค๐˜ญ๐˜ถ๐˜ฅ๐˜ฆ: ๐˜ณ๐˜ฆ๐˜ด๐˜ฆ๐˜ข๐˜ณ๐˜ค๐˜ฉ ๐˜ต๐˜ฆ๐˜ข๐˜ฎ๐˜ด ๐˜ฑ๐˜ฐ๐˜ธ๐˜ฆ๐˜ณ๐˜ฆ๐˜ฅ ๐˜ฃ๐˜บ ๐˜ข๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ด, ๐˜ด๐˜ฎ๐˜ข๐˜ณ๐˜ต ๐˜ฉ๐˜ฐ๐˜ฎ๐˜ฆ ๐˜ฆ๐˜ค๐˜ฐ๐˜ด๐˜บ๐˜ด๐˜ต๐˜ฆ๐˜ฎ๐˜ด ๐˜ฐ๐˜ฑ๐˜ต๐˜ช๐˜ฎ๐˜ช๐˜ป๐˜ช๐˜ฏ๐˜จ ๐˜ฆ๐˜ฏ๐˜ฆ๐˜ณ๐˜จ๐˜บ/๐˜ด๐˜ฆ๐˜ค๐˜ถ๐˜ณ๐˜ช๐˜ต๐˜บ, ๐˜ด๐˜ธ๐˜ข๐˜ณ๐˜ฎ๐˜ด ๐˜ฐ๐˜ง ๐˜ณ๐˜ฐ๐˜ฃ๐˜ฐ๐˜ต๐˜ด ๐˜ช๐˜ฏ ๐˜ญ๐˜ฐ๐˜จ๐˜ช๐˜ด๐˜ต๐˜ช๐˜ค๐˜ด ๐˜ฐ๐˜ณ ๐˜ข๐˜จ๐˜ณ๐˜ช๐˜ค๐˜ถ๐˜ญ๐˜ต๐˜ถ๐˜ณ๐˜ฆ ๐˜ฎ๐˜ข๐˜ฏ๐˜ข๐˜จ๐˜ช๐˜ฏ๐˜จ ๐˜ณ๐˜ฆ๐˜ข๐˜ญ-๐˜ต๐˜ช๐˜ฎ๐˜ฆ ๐˜ถ๐˜ฏ๐˜ค๐˜ฆ๐˜ณ๐˜ต๐˜ข๐˜ช๐˜ฏ๐˜ต๐˜บ ๐—ง๐—ต๐—ฒ ๐—–๐—ผ๐—ฟ๐—ฒ ๐——๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐—ฐ๐—ฒ? AI Agents = autonomous tools for single-task execution Agentic AI = orchestrated ecosystems for workflow-level intelligence ๐—ก๐—ผ๐˜„ ๐—น๐—ผ๐—ผ๐—ธ ๐—ฎ๐˜ ๐˜๐—ต๐—ฒ ๐—ฝ๐—ถ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ: โฌ‡๏ธ ๐—ข๐—ป ๐˜๐—ต๐—ฒ ๐—น๐—ฒ๐—ณ๐˜: a smart thermostat, which can be an AI Agent. It keeps your room at 21ยฐC. Maybe it learns your schedule. But itโ€™s working alone. ๐—ข๐—ป ๐˜๐—ต๐—ฒ ๐—ฟ๐—ถ๐—ด๐—ต๐˜: Agentic AI. A full smart home ecosystem โ€” weather-aware, energy-optimized, schedule-sensitive. Agents talk to each other. They share data. They make coordinated decisions to optimize your comfort, cost, and security in real time. Thatโ€™s the shift = From pure task automation to goal-driven orchestration. From single-agent logic to collaborative intelligence. This is whatโ€™s coming = This is Agentic AI. And if we confuse โ€œagentโ€ with โ€œagentic,โ€ we risk underbuilding for what AI is truly capable of. The Cornell University paper in the comments on this topic is excellent! โฌ‡๏ธ | 186 comments on LinkedIn
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99% ๐—ผ๐—ณ ๐—ฝ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐—ด๐—ฒ๐˜ ๐˜๐—ต๐—ถ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด: ๐—ง๐—ต๐—ฒ๐˜† ๐˜‚๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐˜๐—ฒ๐—ฟ๐—บ๐˜€ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐—ฑ ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ ๐—ถ๐—ป๐˜๐—ฒ๐—ฟ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฎ๐—ฏ๐—น๐˜† โ€” ๐—ฏ๐˜‚๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—ฑ๐—ฒ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐˜๐˜„๐—ผ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐—น๐˜† ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€!
AI Adoption Matrix
AI Adoption Matrix
In fact, after guiding many organisations on this journey over the past few years, I've noticed two consistent drivers of AI adoption: โ€ข A culture that encourages experimentation โ€ข A strategic mandate from leadership that unlocks time, resources, and the infrastructure needed to make AI work at scale Without both, even the most powerful tools are used at a fraction of their potential, leaving the promise of AI unrealised and considerable investments wasted. โžก๏ธ If you have a conservative organisational culture, one that disincentivises taking risks and change, and there's no clear mandate to use AI, you'll have ๐—ถ๐—ฑ๐—น๐—ฒ ๐—ฝ๐—ผ๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น. Try as you might, AI training will hardly translate into people using AI in their work. The knowledge might be there but the impact isn't. โžก๏ธ If you have an innovation culture, one where experimentation is encouraged, but where people are unsure if they're allowed to use AI, you'll have ๐—ฐ๐—ฎ๐˜€๐˜‚๐—ฎ๐—น ๐—ฒ๐˜…๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—บ๐—ฒ๐—ป๐˜๐˜€ - some people tinkering on their own, finding useful use cases and workarounds, but with no way to accumulate, build on, and spread this knowledge. That's where a lot of organisations find themselves in 2025 - the majority of employees are using AI in some form, yet their efforts are siloed and scattered. โžก๏ธ If you have both an innovation culture *and* and an active mandate, you're ๐—ฝ๐—ถ๐—ผ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ถ๐—ป๐—ป๐—ผ๐˜ƒ๐—ฎ๐˜๐—ถ๐—ผ๐—ป and there are few companies still at your level. That's an exciting place to be! That's also where a lot of organisations imagine they would get to as soon as they teach people to use AI, often without first doing the culture and mandate work. โžก๏ธ If your organisation encourages the use of AI but your conservative culture keeps hitting the brakes, you'll likely end up with ๐—ฟ๐—ฒ๐—น๐˜‚๐—ฐ๐˜๐—ฎ๐—ป๐˜ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—น๐—ถ๐—ฎ๐—ป๐—ฐ๐—ฒ. That's also where a considerable number of organisations are right now: driven by expectations of benefits from AI adoption but burdened by processes that are incompatible with grassroots innovation. There is a difference between individual and organisational AI adoption. Organisational adoption is frustratingly complex โ€” it requires coordination across departments and leaders, alignment with business priorities, and systems that enable change, not just enthusiasm. Curiosity gets people started. Supportive systems turn momentum into scale. Nodes #GenAI #AIAdoption #FutureOfWork #Talent
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AI Adoption Matrix
Saw this move from Google this morningโ€”thanks to Marc Steven Ramos (a very fine creator and curator of thought-provoking content). This statement towards the end stood out for me: many of the platformโ€™s โ€œcourses were unused,โ€ and โ€œnot relevant to the work we do today.โ€
Saw this move from Google this morningโ€”thanks to Marc Steven Ramos (a very fine creator and curator of thought-provoking content). This statement towards the end stood out for me: many of the platformโ€™s โ€œcourses were unused,โ€ and โ€œnot relevant to the work we do today.โ€
But Google is not representative of most companies. Not even of tech companies. They can (and should!) be AI-first in every respectโ€”yesterday. Virtually all other companies will take a slower approach, maintaining their learning content and systems, for now. So donโ€™t think you need to drop everything immediately. Instead, work out what a more measured approach looks like for your organisation. Think about how youโ€™re preparing your data, metadata, internal and external contentโ€”and your peopleโ€”for this not-so-distant future when agents are doing more and more of the work, multiplying productivity. Help your company lead the wayโ€”donโ€™t await instructions! That said, I think most three-year horizons will include the other big pull quote from this piece: Google will โ€œfocus on teaching employees how to use modern artificial intelligence tools in their daily work routines.โ€ That, I believe, is where the most worthyโ€”and therefore sustainableโ€”L&D efforts lie: not in creating courses and force-feeding them to people, but in enabling people to work better with AI. โ™ป๏ธ Please REPOST if people youโ€™re connected to may like this. โž• Follow Marc Zao-Sanders for more of this kind of thing. #AI #learning #filtered.com #acelo.ai https://lnkd.in/ehA2pB_R ps: I'm working fractionally for both acelo.ai (sales x AI) and filtered.com (learning content x AI). If you're interested in talking about either, DM me)
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Saw this move from Google this morningโ€”thanks to Marc Steven Ramos (a very fine creator and curator of thought-provoking content). This statement towards the end stood out for me: many of the platformโ€™s โ€œcourses were unused,โ€ and โ€œnot relevant to the work we do today.โ€
For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
It's different from what we've had before: ๐”๐ฉ๐ฌ๐ค๐ข๐ฅ๐ฅ๐ข๐ง๐  ("teach me") - commonly done through hands-on learning with feedback and reflection, such as scenario simulations, in-person role-plays, facilitated discussions, building and problem-solving. None of that has become less relevant, but AI has enabled scale through AI-enabled role-plays, coaching, and other avenues for personalised feedback. ๐๐ž๐ซ๐Ÿ๐จ๐ซ๐ฆ๐š๐ง๐œ๐ž ๐ฌ๐ฎ๐ฉ๐ฉ๐จ๐ซ๐ญ ("help me") - support in the flow of work, previously often in the format of short how-to resources located in convenient places. AI has elevated that in at least two ways: through knowledge management, which helps retrieve the necessary, contextualised information in the workflow; and general & specialised copilots that enhance the speed and, arguably, the expertise of the employee. Yet, ๐ž๐ง๐š๐›๐ฅ๐ž๐ฆ๐ž๐ง๐ญ (โ€˜do it for meโ€™) is different โ€“ it takes the task off your plate entirely. Weโ€™ve seen hints of it with automations, but the text and analysis capabilities of genAI mean that increasingly 'skilled' tasks are now up for grabs. Case in point: where written communication was once a skill to be learned, email and report writing are now increasingly being handed off to AI. No skill required (for better or worse) โ€“ AI does it for you. But here's a plot twist: a lot of that enablement happens outside of L&D tech. It may happen in sales or design software, or even your general-purpose enterprise AI. All of which points to a bigger shift: roles, tasks, and ways of working are changing โ€“ and L&D must tune into how work is being reimagined to adapt alongside it. Nodes #GenAI #Learning #Talent #FutureOfWork #AIAdoption | 13 comments on LinkedIn
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For the longest time we've had two main options to help people perform: upskilling or performance support. Just-in-case vs just-in-time. Push vs pull. With AI, we now have a third - enablement.
The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
(๐˜ˆ ๐˜ฎ๐˜ถ๐˜ด๐˜ต-๐˜ณ๐˜ฆ๐˜ข๐˜ฅ โ€” ๐˜ฆ๐˜ด๐˜ฑ๐˜ฆ๐˜ค๐˜ช๐˜ข๐˜ญ๐˜ญ๐˜บ ๐˜ช๐˜ง ๐˜บ๐˜ฐ๐˜ถ ๐˜ฉ๐˜ข๐˜ท๐˜ฆ ๐˜ค๐˜ฉ๐˜ช๐˜ญ๐˜ฅ๐˜ณ๐˜ฆ๐˜ฏ.) While most AI debates and studies focus on models, chips, and jobs โ€” this one zooms in on something far more personal: ๐—ช๐—ต๐—ฎ๐˜ ๐—ต๐—ฎ๐—ฝ๐—ฝ๐—ฒ๐—ป๐˜€ ๐˜„๐—ต๐—ฒ๐—ป ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ๐—ฟ๐—ฒ๐—ป ๐—ด๐—ฟ๐—ผ๐˜„ ๐˜‚๐—ฝ ๐˜„๐—ถ๐˜๐—ต ๐—ด๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—”๐—œ? They surveyed 1,700+ kids, parents, and teachers across the UK โ€” and what they found is both powerful and concerning. ๐—›๐—ฒ๐—ฟ๐—ฒ ๐—ฎ๐—ฟ๐—ฒ 9 ๐˜๐—ต๐—ถ๐—ป๐—ด๐˜€ ๐˜๐—ต๐—ฎ๐˜ ๐˜€๐˜๐—ผ๐—ผ๐—ฑ ๐—ผ๐˜‚๐˜ ๐˜๐—ผ ๐—บ๐—ฒ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜: โฌ‡๏ธ 1. 1 ๐—ถ๐—ป 4 ๐—ธ๐—ถ๐—ฑ๐˜€ (8โ€“12 ๐˜†๐—ฟ๐˜€) ๐—ฎ๐—น๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐˜‚๐˜€๐—ฒ ๐—š๐—ฒ๐—ป๐—”๐—œ โ€” ๐—บ๐—ผ๐˜€๐˜ ๐˜„๐—ถ๐˜๐—ต๐—ผ๐˜‚๐˜ ๐˜€๐—ฎ๐—ณ๐—ฒ๐—ด๐˜‚๐—ฎ๐—ฟ๐—ฑ๐˜€ โ†’ ChatGPT, Gemini, and even MyAI on Snapchat are now part of daily digital play. 2. ๐—”๐—œ ๐—ถ๐˜€ ๐—ต๐—ฒ๐—น๐—ฝ๐—ถ๐—ป๐—ด ๐—ธ๐—ถ๐—ฑ๐˜€ ๐—ฒ๐˜…๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€ ๐˜๐—ต๐—ฒ๐—บ๐˜€๐—ฒ๐—น๐˜ƒ๐—ฒ๐˜€ โ€” ๐—ฒ๐˜€๐—ฝ๐—ฒ๐—ฐ๐—ถ๐—ฎ๐—น๐—น๐˜† ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ป๐—ฒ๐—ฒ๐—ฑ๐˜€ โ†’ 78% of neurodiverse kids use ChatGPT to communicate ideas they struggle to express otherwise. 3. ๐—–๐—ฟ๐—ฒ๐—ฎ๐˜๐—ถ๐˜ƒ๐—ถ๐˜๐˜† ๐—ถ๐˜€ ๐˜€๐—ต๐—ถ๐—ณ๐˜๐—ถ๐—ป๐—ด โ€” ๐—ฏ๐˜‚๐˜ ๐—ป๐—ผ๐˜ ๐—ฟ๐—ฒ๐—ฝ๐—น๐—ฎ๐—ฐ๐—ถ๐—ป๐—ด โ†’ Kids still prefer offline tools (arts, crafts, games), even when they enjoy AI-assisted play. Digital is not (yet) the default. 4. ๐—”๐—œ ๐—ฎ๐—ฐ๐—ฐ๐—ฒ๐˜€๐˜€ ๐—ถ๐˜€ ๐—ต๐—ถ๐—ด๐—ต๐—น๐˜† ๐˜‚๐—ป๐—ฒ๐—พ๐˜‚๐—ฎ๐—น โ†’ 52% of private school students use GenAI, compared to only 18% in public schools. The next digital divide is already here. 5. ๐—–๐—ต๐—ถ๐—น๐—ฑ๐—ฟ๐—ฒ๐—ป ๐—ฎ๐—ฟ๐—ฒ ๐˜„๐—ผ๐—ฟ๐—ฟ๐—ถ๐—ฒ๐—ฑ ๐—ฎ๐—ฏ๐—ผ๐˜‚๐˜ ๐—”๐—œโ€™๐˜€ ๐—ฒ๐—ป๐˜ƒ๐—ถ๐—ฟ๐—ผ๐—ป๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น ๐—ถ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜ โ†’ Some kids refused to use GenAI after learning about water and energy costs. Let that sink in. 6. ๐—ฃ๐—ฎ๐—ฟ๐—ฒ๐—ป๐˜๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ผ๐—ฝ๐˜๐—ถ๐—บ๐—ถ๐˜€๐˜๐—ถ๐—ฐ โ€” ๐—ฏ๐˜‚๐˜ ๐—ฑ๐—ฒ๐—ฒ๐—ฝ๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ฟ๐—ถ๐—ฒ๐—ฑ โ†’ 76% support AI use, but 82% are scared of inappropriate content and misinformation. Only 41% fear cheating. 7. ๐—ง๐—ฒ๐—ฎ๐—ฐ๐—ต๐—ฒ๐—ฟ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐˜‚๐˜€๐—ถ๐—ป๐—ด ๐—”๐—œ โ€” ๐—ฎ๐—ป๐—ฑ ๐—น๐—ผ๐˜ƒ๐—ถ๐—ป๐—ด ๐—ถ๐˜ โ†’ 85% say GenAI boosts their productivity, 88% feel confident using it. Theyโ€™re ahead of the curve. 8. ๐—–๐—ฟ๐—ถ๐˜๐—ถ๐—ฐ๐—ฎ๐—น ๐˜๐—ต๐—ถ๐—ป๐—ธ๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐˜‚๐—ป๐—ฑ๐—ฒ๐—ฟ ๐˜๐—ต๐—ฟ๐—ฒ๐—ฎ๐˜ โ†’ 76% of parents and 72% of teachers fear kids are becoming too trusting of GenAI outputs. 9. ๐—•๐—ถ๐—ฎ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ถ๐—ฑ๐—ฒ๐—ป๐˜๐—ถ๐˜๐˜† ๐—ฟ๐—ฒ๐—ฝ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐˜€ ๐˜€๐˜๐—ถ๐—น๐—น ๐—ฎ ๐—ฏ๐—น๐—ถ๐—ป๐—ฑ๐˜€๐—ฝ๐—ผ๐˜ โ†’ Children of color felt less seen and less motivated to use tools that didnโ€™t reflect them. Representation matters. The next generation isnโ€™t just using AI. Theyโ€™re being shaped by it. Thatโ€™s why we need a more focused, intentional approach: Teaching them not just how to use these tools โ€” but how to question them. To navigate the benefits, the risks, and the blindspots. ๐—ช๐—ฎ๐—ป๐˜ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—ฏ๐—ฟ๐—ฒ๐—ฎ๐—ธ๐—ฑ๐—ผ๐˜„๐—ป๐˜€ ๐—น๐—ถ๐—ธ๐—ฒ ๐˜๐—ต๐—ถ๐˜€? Subscribe to Human in the Loop โ€” my new weekly deep dive on AI agents, real-world tools, and strategic insights: https://lnkd.in/dbf74Y9E | 174 comments on LinkedIn
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The Alan Turing Institute ๐—ฎ๐—ป๐—ฑ the LEGO Group ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐˜๐—ต๐—ฒ ๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฐ๐—ต๐—ถ๐—น๐—ฑ-๐—ฐ๐—ฒ๐—ป๐˜๐—ฟ๐—ถ๐—ฐ ๐—”๐—œ ๐˜€๐˜๐˜‚๐—ฑ๐˜†! โฌ‡๏ธ
Understanding LLMs, RAG, AI Agents, and Agentic AI
Understanding LLMs, RAG, AI Agents, and Agentic AI
I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relateโ€”not as competing technologies, but as an evolving intelligence architecture. Hereโ€™s a deeper look: 1. ๐—Ÿ๐—Ÿ๐—  (๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: โ€“ Text generation โ€“ Instruction following โ€“ Chain-of-thought reasoning โ€“ Few-shot/zero-shot learning โ€“ Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. ๐—ฅ๐—”๐—š (๐—ฅ๐—ฒ๐˜๐—ฟ๐—ถ๐—ฒ๐˜ƒ๐—ฎ๐—น-๐—”๐˜‚๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฒ๐—ฑ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: โ€“ Vector search โ€“ Embedding-based similarity scoring โ€“ Document chunking โ€“ Hybrid retrieval (dense + sparse) โ€“ Source attribution โ€“ Context injection โ€ฆRAG enhances the quality and factuality of responses. It enables models to โ€œrecallโ€ information they were never trained on, and grounds answers in external sourcesโ€”critical for enterprise-grade applications. 3. ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜ RAG is still a passive architectureโ€”it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: โ€“ Planning and task decomposition โ€“ Execution pipelines โ€“ Long- and short-term memory integration โ€“ File access and API interaction โ€“ Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ This is the most advanced layerโ€”where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: โ€“ Multi-agent collaboration and task delegation โ€“ Modular role assignment and hierarchy โ€“ Goal-directed planning and lifecycle management โ€“ Protocols like MCP (Anthropicโ€™s Model Context Protocol) and A2A (Googleโ€™s Agent-to-Agent) โ€“ Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether youโ€™re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offersโ€”and where it falls shortโ€”will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If thereโ€™s something important you think I missed, feel free to comment or message meโ€”Iโ€™d be happy to include it in the next iteration. | 119 comments on LinkedIn
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Understanding LLMs, RAG, AI Agents, and Agentic AI
BREAKING: Claude launches Education. Free learning is now much faster with AI:
BREAKING: Claude launches Education. Free learning is now much faster with AI:
1. Set clear learning goalsย  โ†ณ Knowing what you want to learn makes it easier. โ†ณ Claude helps you define your path. 2. Provide context for your knowledgeย  โ†ณ Understanding the bigger picture is key.ย  โ†ณ Claude connects new ideas to what you already know. 3. Request detailed explanationsย  โ†ณ Sometimes, you need more than a quick answer.ย  โ†ณ Claude can dive deep into complex topics. 4. Get real-world examplesย  โ†ณ Learning is better with practical applications.ย  โ†ณ Claude shows how concepts work in the real world. 5. Practice writing and receive feedbackย  โ†ณ Writing helps solidify your knowledge.ย  โ†ณ Claude gives instant feedback to improve your skills. 6. Role-play for languages or codingย  โ†ณ Learning by doing is effective.ย  โ†ณ Claude can simulate conversations or coding scenarios. 7. Fact-check surprising claimsย  โ†ณ Misinformation is everywhere.ย  โ†ณ Claude helps you verify facts and claims. 8. Take breaks and reflect on learningย  โ†ณ Reflection is vital for understanding.ย  โ†ณ Claude reminds you to pause and think. 9. Keep a learning journalย  โ†ณ Tracking your progress is important.ย  โ†ณ Claude can help you log your journey. 10. Iterate and refine understandingย  โ†ณ Learning is a process.ย  โ†ณ Claude encourages you to improve your knowledge.ย  | 246 comments on LinkedIn
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BREAKING: Claude launches Education. Free learning is now much faster with AI:
New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...
New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...
Despite our obsession with AI's impact on "academic integrity," two recent analyses show that rather than asking AI for answers, learners are much more likely to use AI to redesign the learning experience in an attempt to learn more. Common strategies include asking AI to apply the protรฉgรฉ effect, using AI to apply the Pareto principle and enhancing levels of emotional metacognition within a learning experience, in the process redesigning the experience sometimes beyond recognition. The uncomfortable truth? Learners are effectively running a real-time audit of our design decisions, processes & practicesโ€”and as instructional designers, we don't come out too well. In this week's blog post, I explore what learner + AI behaviour reveals about our profession and how we might turn this into an opportunity for innovation in instrucitonal design practices and principles. Check out the full post using the link in comments. Happy innovating! Phil ๐Ÿ‘‹ | 16 comments on LinkedIn
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New research shows that your learners arenโ€™t using AI to cheat - theyโ€™re using it to redesign your courses...
Trends Artificial Intelligence - BOND Capitalai
Trends Artificial Intelligence - BOND Capitalai
Die Pflichtlektรผre zum Sonntag: Mary Meeker hat einen ihrer legendรคren Reports gedropped... Nach den jรคhrlichen "Internet Trends" nun ein 340 Seiten Brett ihrer Investment Firma Bond Capital ganz zum Thema AI. Superbes Gedankenfutter hinsichtlich u.a.: 1. Nutzerwachstum und Verbreitung โ€ข ChatGPT erreichte 800 Millionen wรถchentliche Nutzer in nur 17 Monaten โ€ข Verbreitung auรŸerhalb Nordamerikas liegt bei 90 Prozent โ€“ nach nur 3 Jahren โ€ข Vergleich: Das Internet brauchte dafรผr 23 Jahre โ€ข KI-Anwendungen skalieren global nahezu gleichzeitig 2. Investitionen und Infrastruktur โ€ข Big Tech (Apple, Microsoft, Google, Amazon, Meta, Nvidia) investiert รผber 212 Milliarden Dollar CapEx pro Jahr โ€ข KI wird zur neuen Infrastruktur โ€“ vergleichbar mit Strom oder Internet โ€ข Rechenzentren werden zu produktiven "KI-Fabriken" 3. Entwickler-ร–kosysteme explodieren โ€ข Google Gemini: 7 Millionen aktive Entwickler, +500 Prozent in 12 Monaten โ€ข NVIDIA-ร–kosystem: 6 Millionen Entwickler, +6x in sieben Jahren โ€ข Open Source spielt zunehmend eine Schlรผsselrolle, auch in China 4. Technologischer Fortschritt beschleunigt sich exponentiell โ€ข 260 Prozent Wachstum pro Jahr bei Trainingsdatenmengen โ€ข 360 Prozent Wachstum pro Jahr beim Compute-Aufwand fรผr Modelltraining โ€ข Bessere Algorithmen fรผhren zu 200 Prozent Effizienzsteigerung pro Jahr โ€ข Fortschritte bei Supercomputern ermรถglichen +150 Prozent Leistungszuwachs jรคhrlich 5. Monetarisierung ist real โ€“ aber teuer โ€ข OpenAI mit starkem Nutzerwachstum, aber weiterhin Milliardenverluste โ€ข Compute-Kosten steigen, Inferenzkosten pro Token sinken โ€ข Monetรคre Skalierung bleibt herausfordernd und kompetitiv 6. Arbeit und Gesellschaft verรคndern sich sichtbar โ€ข IT-KI-Stellen in den USA: +448 Prozent seit 2018 โ€ข Nicht-KI-IT-Stellen: โ€“9 Prozent โ€ข Erste autonome Taxis nehmen Marktanteile in Stรคdten wie San Francisco โ€ข KI-Scribes in der Medizin reduzieren administrativen Aufwand massiv 7. Wissen und Kommunikation erleben ein neues Zeitalter โ€ข Nach Buchdruck und Internet folgt die ร„ra der generativen Wissensverbreitung โ€ข Generative KI verรคndert, wie wir Wissen erzeugen, verbreiten und nutzen โ€ข Anwendungen wie ElevenLabs oder Spotify รผbersetzen Stimmen in Echtzeit, global skalierbar 8. Geopolitik wird zur KI-Strategie โ€ข USA und China investieren aggressiv in souverรคne KI-Modelle โ€ข Wer KI-Infrastruktur dominiert, definiert รถkonomische und politische Macht neu โ€ข Fรผhrende CTOs sprechen offen von einem neuen "Space Race" 9. Chancen und Risiken sind gewaltig โ€ข KI kann medizinische Forschung, Bildung und Kreativitรคt beflรผgeln โ€ข Gleichzeitig drohen Kontrollverlust, Missbrauch, Arbeitsplatzverdrรคngung, ethische Dilemmata Meinungen? Evangelos Papathanassiou Christian Herold Thorsten Muehl Christoph Deutschmann Constance Stein Rebecca Schalber Sandy Brueckner Dirk Hofmann Henning Tomforde Dr. Paul Elvers Katharina Neubert Laura Seiffe Ekaterina Schneider
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Trends Artificial Intelligence - BOND Capitalai
๐——๐—ถ๐—ฒ ๐——๐—ถ๐—ฑ๐—ฎ๐—ธ๐˜๐—ถ๐—ธ ๐—ณ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜€ ๐—–๐—ผ๐—ฟ๐—ฝ๐—ผ๐—ฟ๐—ฎ๐˜๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐—ณ ๐—ฑ๐—ฒ๐—ป ๐—ž๐—ผ๐—ฝ๐—ณ ๐—ด๐—ฒ๐˜€๐˜๐—ฒ๐—น๐—น๐˜
๐——๐—ถ๐—ฒ ๐——๐—ถ๐—ฑ๐—ฎ๐—ธ๐˜๐—ถ๐—ธ ๐—ณ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜€ ๐—–๐—ผ๐—ฟ๐—ฝ๐—ผ๐—ฟ๐—ฎ๐˜๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐—ณ ๐—ฑ๐—ฒ๐—ป ๐—ž๐—ผ๐—ฝ๐—ณ ๐—ด๐—ฒ๐˜€๐˜๐—ฒ๐—น๐—น๐˜
B๐˜ฆ๐˜ต๐˜ณ๐˜ช๐˜ฆ๐˜ฃ๐˜ญ๐˜ช๐˜ค๐˜ฉ๐˜ฆ๐˜ด ๐˜“๐˜ฆ๐˜ณ๐˜ฏ๐˜ฆ๐˜ฏ ๐˜จ๐˜ช๐˜ฏ๐˜จ ๐˜ฃ๐˜ช๐˜ด๐˜ฉ๐˜ฆ๐˜ณ ๐˜ท๐˜ฐ๐˜ฏ ๐˜ง๐˜ฐ๐˜ญ๐˜จ๐˜ฆ๐˜ฏ๐˜ฅ๐˜ฆ๐˜ฏ ๐˜—๐˜ณ๐˜ข๐˜ฎ๐˜ช๐˜ด๐˜ด๐˜ฆ๐˜ฏ ๐˜ข๐˜ถ๐˜ด: โ€ข ๐˜ž๐˜ช๐˜ด๐˜ด๐˜ฆ๐˜ฏ๐˜ด- ๐˜ถ๐˜ฏ๐˜ฅ ๐˜˜๐˜ถ๐˜ข๐˜ญ๐˜ช๐˜ง๐˜ช๐˜ฌ๐˜ข๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด๐˜ป๐˜ช๐˜ฆ๐˜ญ๐˜ฆ ๐˜ด๐˜ช๐˜ฏ๐˜ฅ ๐˜ง๐˜ถ๐˜ณ ๐˜ข๐˜ญ๐˜ญ๐˜ฆ ๐˜จ๐˜ญ๐˜ฆ๐˜ช๐˜ค๐˜ฉ ๐˜ช๐˜ฏ ๐˜ฆ๐˜ช๐˜ฏ๐˜ฆ๐˜ฎ ๐˜Š๐˜ถ๐˜ณ๐˜ณ๐˜ช๐˜ค๐˜ถ๐˜ญ๐˜ถ๐˜ฎ ๐˜ท๐˜ฐ๐˜ณ๐˜จ๐˜ฆ๐˜จ๐˜ฆ๐˜ฃ๐˜ฆ๐˜ฏ. ย โ€ข ๐˜‹๐˜ช๐˜ฆ๐˜ด๐˜ฆ ๐˜ก๐˜ช๐˜ฆ๐˜ญ๐˜ฆ ๐˜ธ๐˜ฆ๐˜ณ๐˜ฅ๐˜ฆ๐˜ฏ ๐˜ช๐˜ฏ ๐˜ง๐˜ณ๐˜ฆ๐˜ฎ๐˜ฅ๐˜ฐ๐˜ณ๐˜จ๐˜ข๐˜ฏ๐˜ช๐˜ด๐˜ช๐˜ฆ๐˜ณ๐˜ต๐˜ฆ๐˜ฏ ๐˜“๐˜ฆ๐˜ฉ๐˜ณ๐˜ข๐˜ณ๐˜ณ๐˜ข๐˜ฏ๐˜จ๐˜ฆ๐˜ฎ๐˜ฆ๐˜ฏ๐˜ต๐˜ด โ€ž๐˜ท๐˜ฆ๐˜ณ๐˜ฎ๐˜ช๐˜ต๐˜ต๐˜ฆ๐˜ญ๐˜ตโ€œ. ย โ€ข ๐˜‹๐˜ช๐˜ฆ ๐˜ˆ๐˜ถ๐˜ง๐˜ฃ๐˜ข๐˜ถ ๐˜ฅ๐˜ฆ๐˜ณ ๐˜๐˜ข๐˜ฏ๐˜ฅ๐˜ญ๐˜ถ๐˜ฏ๐˜จ๐˜ด๐˜ง๐˜ข๐˜ฉ๐˜ช๐˜จ๐˜ฌ๐˜ฆ๐˜ช๐˜ต ๐˜ช๐˜ฏ ๐˜ฅ๐˜ฆ๐˜ณ ๐˜—๐˜ณ๐˜ข๐˜น๐˜ช๐˜ด (๐˜’๐˜ฐ๐˜ฎ๐˜ฑ๐˜ฆ๐˜ต๐˜ฆ๐˜ฏ๐˜ป๐˜ฆ๐˜ฏ) ๐˜ธ๐˜ช๐˜ณ๐˜ฅ ๐˜ฅ๐˜ถ๐˜ณ๐˜ค๐˜ฉ ๐˜›๐˜ณ๐˜ข๐˜ฏ๐˜ด๐˜ง๐˜ฆ๐˜ณ๐˜ข๐˜ถ๐˜ง๐˜จ๐˜ข๐˜ฃ๐˜ฆ๐˜ฏ ๐˜จ๐˜ฆ๐˜ด๐˜ช๐˜ค๐˜ฉ๐˜ฆ๐˜ณ๐˜ต. Wir erleben aktuell, verstรคrkt durch die Kรผnstliche Intelligenz, einen Paradigmenwechsel, der diese betriebliche Didaktik auf den Kopf stellt: ย โ€ข Formelle Bildungsangebote auf Basis von Curricula werden nach und nach durch โ€ž๐—™๐—น๐—ถ๐—ฝ๐—ฝ๐—ฒ๐—ฑ ๐—–๐˜‚๐—ฟ๐—ฟ๐—ถ๐—ฐ๐˜‚๐—น๐—ฎโ€œ (vgl. Sabine Seufert 2024) ersetzt. Danach bilden Werte und Kompetenzen โ€“ Soft Skills โ€“ die Ziele des Corporate Learning. Wissen und Qualifikation sind natรผrlich weiterhin notwendig, sind aber nicht mehr das Ziel des Lernens, sondern die notwendige Voraussetzung. Dies bedeutet, dass das erforderliche Wissen beispielsweise auch kuratiert durch die KI zur Verfรผgung gestelltย werden kann. ย โ€ข Der wichtigste Lernort wird der ๐—”๐—ฟ๐—ฏ๐—ฒ๐—ถ๐˜๐˜€๐—ฝ๐—ฟ๐—ผ๐˜‡๐—ฒ๐˜€๐˜€, weil Werte und Kompetenzen nur selbstorganisiert bei der Bewรคltigung von realen Herausforderungen aufgebaut werden kรถnnen. Daraus ergibt sich folgender Planungsrythmus. 1. Am Anfang steht die Frage, in welcher ๐—ฃ๐—ฟ๐—ฎ๐˜…๐—ถ๐˜€๐—ต๐—ฒ๐—ฟ๐—ฎ๐˜‚๐˜€๐—ณ๐—ผ๐—ฟ๐—ฑ๐—ฒ๐—ฟ๐˜‚๐—ป๐—ด die angestrebten Soft-Skills aufgebaut werden kรถnnen. In Abstimmung mit ihren Fรผhrungskrรคften vereinbaren die Mitarbeitenden auf Basis ihrer Skills Diagnostik personalisierte Lernpfade im Arbeitsprozess.ย  ย 2. Im zweiten Schritt ist zu klรคren, welche ๐—™๐—น๐—ฎ๐—ป๐—ธ๐—ถ๐—ฒ๐—ฟ๐˜‚๐—ป๐—ด die selbstorganisierten Lernprozesse der Mitarbeitenden benรถtigen. Dabei spielt das soziale Lernen eine zentrale Rolle. Begleitet werden diese Prozesse durch die Beratung und Begleitung durch Lernbegleitende und Expert*innen. ย 3. Erst im dritten Schritt werden diese LernmaรŸnahmen bei Bedarf durch ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป๐—ถ๐—ป๐—ด๐˜€ ergรคnzt. Beispielsweise bieten sich Methodentrainings, z. B. zu SCRUM, an, wenn die ausgewรคhlten Praxisaufgaben nach agilen Prinzipien erfolgen sollen.ย  ย 4. In unterstรผtzenden ๐—ช๐—ฒ๐—ถ๐˜๐—ฒ๐—ฟ๐—ฏ๐—ถ๐—น๐—ฑ๐˜‚๐—ป๐—ด๐˜€๐—บ๐—ฎรŸ๐—ป๐—ฎ๐—ต๐—บ๐—ฒ๐—ป kรถnnen Basiswissen und Grundqualifikationen aufgebaut oder AnstรถรŸe fรผr die selbstorganisierten Lernprozess gegeben werden. Lernen erfolgt von Anfang an in der Praxis, indem Arbeiten und Lernen zusammenwachsen. Damit erรผbrigen sich Konzepte zur Fรถrderung des Lerntransfers weitgehend. Die Verantwortung fรผr das Lernen wandert damit zu den Mitarbeitenden, die dabei von der Personalentwicklung und Ihrer Fรผhrungskraft unterstรผtzt werden.
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๐——๐—ถ๐—ฒ ๐——๐—ถ๐—ฑ๐—ฎ๐—ธ๐˜๐—ถ๐—ธ ๐—ณ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜€ ๐—–๐—ผ๐—ฟ๐—ฝ๐—ผ๐—ฟ๐—ฎ๐˜๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐—ณ ๐—ฑ๐—ฒ๐—ป ๐—ž๐—ผ๐—ฝ๐—ณ ๐—ด๐—ฒ๐˜€๐˜๐—ฒ๐—น๐—น๐˜
5 Moments of Need framework
5 Moments of Need framework
-- Task criticality and risk are central considerations in performance support design. When there's high consequence for error (safety risks, costly damage, or life-or-death stakes) the skill guide design needs to be highly intentional, context-aware, and tightly integrated into the environment of use. -- A skill guide is great in high-risk situations (we were in an airline context). In a low-stakes context a pre-flight checklist is great for trained pilots. It supports memory recall for the essential steps in a high-risk task. -- In a context such as de-icing a plane, a diagram-based skill guide is great to illustrate the basic controls of the machine. This helps build mental models. -- In flight simulation training, skill guides can walk a novice through tasks like starting the engine, adjusting trim, or responding to a warning light. These guides scaffold learning and reduce cognitive load in a controlled environment. -- Of course, skill guides can't always replace training. Real-time control of a plane requires deeply embodied skill: fine motor control, situational awareness, and rapid decision-making. You can't guide someone through that just in time with a single page or even a tablet-based tool. -- In life-critical systems, thereโ€™s a threshold beyond which skill guides must give way to rigorous training, simulation, and certification. Performance support becomes a supplement, not a substitute in these contexts. Bob and Con have had immeasurable impact on my career and perspective when it comes to human performance. I even asked Bob to write the foreword of my most recent book. Their 5 Moments of Need framework enables direct alignment to real-time needs of workers. The moments of need are: 1. New (When learning something for the first time) 2. More (When there's a need to deepen or expand knowledge or skills) 3. Apply (When performing a task or applying knowledge in real situations) 4. Solve (When encountering a problem or unexpected challenge) 5. Change (When adapting to change such as a new process, tool, or an organizational shift) When learning is designed against these moments of need, job performance not only becomes more effective, but the worker gets more done quicker and with minimal disruption and frustration. By addressing these moments effectively, you can optimize learning outcomes and drive tangible results.
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5 Moments of Need framework
๐—ช๐—ฒ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—บ๐—ผ๐˜ƒ๐—ฒ ๐—ฏ๐—ฒ๐˜†๐—ผ๐—ป๐—ฑ ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ฎ๐—ป โ€œ๐—Ÿ๐—Ÿ๐— .โ€ โฌ‡๏ธ
๐—ช๐—ฒ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—บ๐—ผ๐˜ƒ๐—ฒ ๐—ฏ๐—ฒ๐˜†๐—ผ๐—ป๐—ฑ ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ฎ๐—ป โ€œ๐—Ÿ๐—Ÿ๐— .โ€ โฌ‡๏ธ
In 2025, the AI landscape has evolved far beyond just large language models. Knowing which model to use for your specific use case โ€” and how โ€” is becoming a strategic advantage. Letโ€™s break down theย 8 most important model typesย and what theyโ€™re actually built to do: โฌ‡๏ธ 1. ๐—Ÿ๐—Ÿ๐—  โ€“ ๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Your ChatGPT-style model. Handles text, predicts the next token, and powers 90% of GenAI hype. ๐Ÿ›  Use case: content, code, convos. 2. ๐—Ÿ๐—–๐—  โ€“ ๐—Ÿ๐—ฎ๐˜๐—ฒ๐—ป๐˜ ๐—–๐—ผ๐—ป๐˜€๐—ถ๐˜€๐˜๐—ฒ๐—ป๐—ฐ๐˜† ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Lightweight, diffusion-style models. Fast, quantized, and efficient โ€” perfect for real-time or edge deployment. ๐Ÿ›  Use case: image generation, optimized inference. 3. ๐—Ÿ๐—”๐—  โ€“ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐—”๐—ฐ๐˜๐—ถ๐—ผ๐—ป ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Where LLM meets planning. Adds memory, task breakdown, and intent recognition. ๐Ÿ›  Use case: AI agents, tool use, step-by-step execution. 4. ๐— ๐—ผ๐—˜ โ€“ ๐— ๐—ถ๐˜…๐˜๐˜‚๐—ฟ๐—ฒ ๐—ผ๐—ณ ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐˜๐˜€ โ†’ One model, many minds. Routes input to the right โ€œexpertโ€ model slice โ€” dynamic, scalable, efficient. ๐Ÿ›  Use case: high-performance model serving at low compute cost. 5. ๐—ฉ๐—Ÿ๐—  โ€“ ๐—ฉ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Multimodal beast. Combines image + text understanding via shared embeddings. ๐Ÿ›  Use case: Gemini, GPT-4o, search, robotics, assistive tech. 6. ๐—ฆ๐—Ÿ๐—  โ€“ ๐—ฆ๐—บ๐—ฎ๐—น๐—น ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Tiny but mighty. Designed for edge use, fast inference, low latency, efficient memory. ๐Ÿ›  Use case: on-device AI, chatbots, privacy-first GenAI. 7. ๐— ๐—Ÿ๐—  โ€“ ๐— ๐—ฎ๐˜€๐—ธ๐—ฒ๐—ฑ ๐—Ÿ๐—ฎ๐—ป๐—ด๐˜‚๐—ฎ๐—ด๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ The OG foundation model. Predicts masked tokens using bidirectional context. ๐Ÿ›  Use case: search, classification, embeddings, pretraining. 8. ๐—ฆ๐—”๐—  โ€“ ๐—ฆ๐—ฒ๐—ด๐—บ๐—ฒ๐—ป๐˜ ๐—”๐—ป๐˜†๐˜๐—ต๐—ถ๐—ป๐—ด ๐— ๐—ผ๐—ฑ๐—ฒ๐—น โ†’ Vision model for pixel-level understanding. Highlights, segments, and understands *everything* in an image. ๐Ÿ›  Use case: medical imaging, AR, robotics, visual agents. Understanding these distinctions is essentialย for selecting the right model architecture for specific applications, enabling more effective, scalable, and contextually appropriate AI interactions. While these are some of the most prominent specialized AI models, there are many more emerging across language, vision, speech, and robotics โ€” each optimized for specific tasks and domains. LLM, VLM, MoE, SLM, LCMย โ†’ GenAI LAM, MLM, SAMย โ†’ Not classic GenAI, butย critical building blocksย for AI agents, reasoning, and multimodal systems ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E Kudos for the graphic goes to Generative AI ! | 45 comments on LinkedIn
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๐—ช๐—ฒ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—บ๐—ผ๐˜ƒ๐—ฒ ๐—ฏ๐—ฒ๐˜†๐—ผ๐—ป๐—ฑ ๐—ฐ๐—ฎ๐—น๐—น๐—ถ๐—ป๐—ด ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ฎ๐—ป โ€œ๐—Ÿ๐—Ÿ๐— .โ€ โฌ‡๏ธ
Posten | LinkedIn
Posten | LinkedIn
Welche Interventionen kรถnnen menschliches Verhalten wirksam verรคndern? Die Meta-Analyse vonย Albarracรญn et al. (2024) ist eine Meta-Analysen von Meta-Analysen. 147 Meta-Analysen wurden zusammengefasst. Krass. Das Paper ist 106 Seiten lang und ein echtes Brett. Die Kolleg:innen haben รผber viele verschiedene Bereiche (z. B. Gesundheits- und Organisationsverhalten) hinweg untersucht, was das Verhalten von Menschen verรคndern kann. Die Ergebnisse zeigen meiner Interpretation nach: - es gibt keinen Interventionsbereich mit starken Effekten. Wir mรผssen demรผtig bleiben. Das Verhalten von Menschen zu beeinflussen, bleibt schwierig. Vielleicht ist das auch gut so. - es existieren sowohl strukturelle als auch individuelle Interventionen, die erfolgreich sind. Mit einem Fokus auf nur "structure first" oder nur "people first" verschenkt man viel Potential. - Menschen mit Wissen zu versorgen und hoffen, dass sie durch Einsicht ihr Verhalten verรคndern, bringt eher nichts. - Sanktionen zeigen ebenfalls zu vernachlรคssigende Effekte - nahezu keinen Verhaltenseffekt haben im Schnitt auch Mindsetinterventionen (beliefs) - mittelstarke Effekte zeigt die Bereitstellung des Zugangs zu Ressourcen, die fรผr das Zielverhalten wichtig sind - auch wirksam sind Interventionen, die auf Gewohnheiten abzielen. Also solche, die Verhaltensgewohnheiten etablieren oder sie verรคndern. - zumindest kleine Effekte bieten soziale Unterstรผtzung, soziale Normen, Verhaltenstrainings und die Arbeit an und mit Emotionen. Das sind natรผrlich "nur" Mittlerwerte von Mittelwerten aber das Studienfundament ist echt der Hammer. Was kรถnnte das fรผr die Verhaltensverรคnderungen in Organisationen bedeuten? Gebt den Menschen Zugang zu Ressourcen und unterstรผtzt sie bei Verรคnderungen. Versucht an den Gewohnheiten zu arbeiten und trainiert Verhalten statt Wissen. Da vieles ein bisschen wirkt, braucht man wohl viele unterschiedliche Ansรคtze, um grรถรŸere Effekte zu erreichen. Die immer wieder gestellte Frage Mensch oder Organisation ist nicht zielfรผhrend. Strukturen und Menschen gehรถren gemeinsam gedacht und bearbeitet. Die Studie ist frei verfรผgbar. Bei Interesse und Nachfragen gerne mal in die Studie reinschauen. Albarracรญn, D., Fayaz-Farkhad, B., & Granados Samayoa, J. A. (2024). Determinants of behaviour and their efficacy as targets of behavioural change interventions. Nature Reviews Psychology, 3(6), 377-392. #Verhalten #Macht #Transformation #Entwicklung | 175 Kommentare auf LinkedIn
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Posten | LinkedIn
๐—ฅ๐—ฒ๐—ถ๐—ฐ๐—ต๐˜„๐—ฒ๐—ถ๐˜๐—ฒ๐—ป-๐—ž.๐—ข. ๐—ณรผ๐—ฟ ๐˜ƒ๐—ถ๐—ฒ๐—น๐—ฒ ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€. Erst wandern Suchanfragen von Google zu ChatGPT - jetzt beantwortet sie Google direkt in den AI-Overviews.
๐—ฅ๐—ฒ๐—ถ๐—ฐ๐—ต๐˜„๐—ฒ๐—ถ๐˜๐—ฒ๐—ป-๐—ž.๐—ข. ๐—ณรผ๐—ฟ ๐˜ƒ๐—ถ๐—ฒ๐—น๐—ฒ ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€. Erst wandern Suchanfragen von Google zu ChatGPT - jetzt beantwortet sie Google direkt in den AI-Overviews.
๐Ÿšจ Studien zeigen bereits hohe Traffic-Rรผckgรคnge. ย  Was kรถnnen Redakteure und Publisher tun? ย  ๐Ÿ‘Š Deshalb bin ich mit Matthรคus Michalik in den Podcast-Ring gestiegen: ย  Wir haben 2 Folgen aufgenommen: ๐—š๐—˜๐—ข ๐˜€๐˜๐—ฎ๐˜๐˜ ๐—ฆ๐—˜๐—ข & ๐—ช๐—ถ๐—ฒ ๐—ฝ๐—น๐—ฎ๐˜๐˜‡๐—ถ๐—ฒ๐—ฟ๐—ฒ๐—ป ๐˜„๐—ถ๐—ฟ ๐˜‚๐—ป๐˜€ ๐—ถ๐—ป ๐—ฑ๐—ฒ๐—ป ๐—”๐—œ-๐—ข๐˜ƒ๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„? ย  Als Teaser fรผr euch: 4๏ธโƒฃ Sofort-Tipps fรผr GEO (Generative Engine Optimization) 1. Autoritรคt & Vertrauen belegen ๐Ÿ”ธ Quellen, Zitate und fachliche Referenzen explizit nennen. ๐Ÿ”ธErgebnis: bis zu +40 % hรถhere Wahrscheinlichkeit, in KI-Antworten zitiert zu werden. 2. Zahlen sprechen lassen ๐Ÿ”ธStatistiken, Studien-Daten und eigene Benchmarks einbauen. ๐Ÿ”ธKI-Modelle gewichten quantitative Infos stรคrker โ†’ +30 % Relevanz-Boost. 3. Klare Struktur, einfache Sprache ๐Ÿ”ธKurze Absรคtze, Bullet-Points, FAQs, sprechende Zwischenยญรผberschriften. ๐Ÿ”ธErleichtert Parsing durch LLMs und erhรถht die Chance auf direkte รœbernahme. 4. Gezielter Fachwort-Einsatz ๐Ÿ”ธRelevante Terminologie und Branchen-Jargon bewusst einstreuen. ๐Ÿ”ธSignalisiert Expertise und verbessert das Matching fรผr spezifische Nutzerยญanfragen. โ€ผ๏ธ Kurzformel: Autoritรคt + Daten + Klarheit + Terminologie = Sichtbarkeit Chat-Antworten. ย  ๐—ฆ๐—ถ๐—ฐ๐—ต๐˜๐—ฏ๐—ฎ๐—ฟ๐—ธ๐—ฒ๐—ถ๐˜ ๐—ถ๐—ป ๐—”๐—œ ๐—ข๐˜ƒ๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„๐˜€ โ€“ ๐—ฑ๐—ฎ๐˜€ ๐—บ๐˜‚๐˜€๐˜€๐˜ ๐—ฑ๐˜‚ ๐—ฏ๐—ฒ๐—ฎ๐—ฐ๐—ต๐˜๐—ฒ๐—ป ๐Ÿ”ธGrundvoraussetzung: Deine Seite muss im Google-Index stehen und bereits ein gewisses Vertrauensniveau besitzen. Dann gilt: ๐Ÿ”ธHochwertige, faktenbasierte Inhalte: prรคzise, recherchiert, aktuell. ๐Ÿ”ธKlare Struktur: H-รœberschriften, Listen, Tabellen โ†’ erleichtert Parsing. ๐Ÿ”ธStrukturierte Daten (Schema.org): zeigt der KI, was welche Bedeutung hat. ๐Ÿ”ธUX & Performance: schnelle Ladezeiten, sauberes Mobile-Design. ๐Ÿ”ธE-E-A-T pflegen: Expertise, Erfahrung, Autoritรคt, Vertrauen kontinuierlich belegen (Autorenยญprofile, Quellen, Backlinks). ๐Ÿด ๐—ฃ๐—ฟ๐—ฎ๐˜…๐—ถ๐˜€-๐—ง๐—ถ๐—ฝ๐—ฝ๐˜€ ๐—ณรผ๐—ฟ ๐—ฑ๐—ถ๐—ฒ ๐—ฃ๐—ผ๐˜€๐˜-๐—ฆ๐—˜๐—ข-ร„๐—ฟ๐—ฎ โœ”๏ธ Qualitรคt vor Quantitรคt โ€“ fewer, deeper pieces mit klarer Expertise. โœ”๏ธStruktur first โ€“ H-Tags, Bullet-Points, FAQ-Blรถcke, Schema. โœ”๏ธUser Experience optimieren โ€“ Speed, Navigation, mobile UX. โœ”๏ธMehrwert รผber die KI hinaus โ€“ eigene Daten, Cases, Meinungen. โœ”๏ธTraffic-Quellen streuen โ€“ Social, E-Mail, Communities, Partnerschaften. โœ”๏ธMonitoring & Anpassung โ€“ beobachte, welche Seiten in AI Overviews landen, und iteriere. โœ”๏ธMultimedial denken โ€“ Videos, Podcasts, Infografiken ergรคnzen Text. โœ”๏ธE-E-A-T kontinuierlich stรคrken โ€“ Fachautor:innen, Referenzen, Reviews, Backlinks. ๐—ž๐˜‚๐—ฟ๐˜‡ยญ๐—ณ๐—ผ๐—ฟ๐—บ๐—ฒ๐—น: Qualitรคt + Struktur + Mehrwert + Vertrauen + Channel-Mix = langfristige Sichtbarkeit โ€“ auch in der KI-Suche. โ“ Wie geht ihr den Battle um Sichtbarkeit und Traffic an? Lasst uns diskutieren. ๐Ÿ‘‡ | 12 Kommentare auf LinkedIn
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๐—ฅ๐—ฒ๐—ถ๐—ฐ๐—ต๐˜„๐—ฒ๐—ถ๐˜๐—ฒ๐—ป-๐—ž.๐—ข. ๐—ณรผ๐—ฟ ๐˜ƒ๐—ถ๐—ฒ๐—น๐—ฒ ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€. Erst wandern Suchanfragen von Google zu ChatGPT - jetzt beantwortet sie Google direkt in den AI-Overviews.
Hugging Face ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ 9 ๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ ๐—ฐ๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€! Ifโ€ฆ
Hugging Face ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ 9 ๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ ๐—ฐ๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€! Ifโ€ฆ
Hugging Face ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ 9 ๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ ๐—ฐ๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€! If youโ€™re trying to level up or pivot into AI โ€” this is pure gold. ๐—”๐—น๐—น OPEN. ๐—”๐—น๐—น FREE. ๐—”๐—น๐—น expert thaugt. Hereโ€™s whatโ€™s inside (with links): โฌ‡๏ธ 1. ๐—Ÿ๐—Ÿ๐—  ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Master large language models fast. Train, fine-tune, deploy with Transformers. โ†’ https://lnkd.in/dcCMCs96 2. ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Build multi-step reasoning agents with LangChain + HuggingFace. โ†’ https://lnkd.in/dJD3QRuT 3. ๐——๐—ฒ๐—ฒ๐—ฝ ๐—ฅ๐—Ÿ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Teach AI to learn like a human. Reward-based decision-making in real environments. โ†’ https://lnkd.in/d8JuRvn8 4. ๐—–๐—ผ๐—บ๐—ฝ๐˜‚๐˜๐—ฒ๐—ฟ ๐—ฉ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Image classification, segmentation, object detection โ€” with HF models. https://lnkd.in/dEH8Tx-v 5. ๐—”๐˜‚๐—ฑ๐—ถ๐—ผ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Turn sound into signal. Voice recognition, music tagging, audio generation. โ†’ https://lnkd.in/dZtkA3sw 6. ๐— ๐—Ÿ ๐—ณ๐—ผ๐—ฟ ๐—š๐—ฎ๐—บ๐—ฒ๐˜€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ AI-powered game design: NPCs, logic, procedural generation. โ†’ https://lnkd.in/d4RhU6pz 7. ๐— ๐—Ÿ ๐—ณ๐—ผ๐—ฟ 3๐—— ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ Work with point clouds, meshes, and 3D data in ML. โ†’ https://lnkd.in/dU8T8BPw 8. ๐——๐—ถ๐—ณ๐—ณ๐˜‚๐˜€๐—ถ๐—ผ๐—ป ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ The tech behind DALLยทE and Stable Diffusion. Generate visuals from noise โ€” step by step. โ†’ https://lnkd.in/dFwN_idt 9. ๐—ข๐—ฝ๐—ฒ๐—ป-๐—ฆ๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ ๐—”๐—œ ๐—–๐—ผ๐—ผ๐—ธ๐—ฏ๐—ผ๐—ผ๐—ธ Not a course โ€” a growing library of real-world AI notebooks. Copy, remix, and build. โ†’ https://lnkd.in/dQ5BXvSz Thereโ€™s no excuse left. Save this. Study it. Build. Share this with your network to help them level up! โ™ป๏ธ Which one will you start with? | 16 comments on LinkedIn
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Hugging Face ๐—ท๐˜‚๐˜€๐˜ ๐—ฑ๐—ฟ๐—ผ๐—ฝ๐—ฝ๐—ฒ๐—ฑ 9 ๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ ๐—ฐ๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€! Ifโ€ฆ
Wenn Du nochmal bei 0 starten kรถnntest, wie wรผrdest du eine Daten- und KI-Organisation aufbauen?
Wenn Du nochmal bei 0 starten kรถnntest, wie wรผrdest du eine Daten- und KI-Organisation aufbauen?
Genau das wollte ich von Claudia Pohlink wissen, die eine beeindruckende Karriere in der Daten- und KI-Welt bei Telekom, Deutsche Bahn und FIEGE hingelegt hat. Also, wie sieht der Anti-Hype Blueprint aus? ๐Ÿญ. ๐—ฆ๐˜๐—ฎ๐—บ๐—บ๐—ฑ๐—ฎ๐˜๐—ฒ๐—ป ๐—ฑ๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐—ฒ๐—ฟ๐—ฒ๐—ป ๐˜‚๐—ป๐—ฑ ๐˜€๐˜๐—ฟ๐˜‚๐—ธ๐˜๐˜‚๐—ฟ๐—ถ๐—ฒ๐—ฟ๐—ฒ๐—ป Starte mit der Definition deiner Kerndomรคnen und Stammdaten. Bestimme fรผhrende Systeme fรผr jede Datendomรคne, bevor du Tools auswรคhlst. Dies schafft ein stabiles Fundament fรผr alle KI-Aktivitรคten. ๐Ÿฎ. ๐—˜๐—ฟ๐˜€๐˜๐—ฒ ๐—˜๐—ฟ๐—ณ๐—ผ๐—น๐—ด๐˜€๐—ด๐—ฒ๐˜€๐—ฐ๐—ต๐—ถ๐—ฐ๐—ต๐˜๐—ฒ ๐˜€๐—ฐ๐—ต๐—ฟ๐—ฒ๐—ถ๐—ฏ๐—ฒ๐—ป Identifiziere einen ersten Use Case, zum Beispiel mit dem Controlling-Bereich, wo bereits Datenaffinitรคt besteht. Zeige schnelle Erfolge, um Management-Support zu gewinnen. ๐Ÿฏ. ๐——๐—ฎ๐˜€ ๐Ÿฏ-๐—›๐—ฎฬˆ๐˜‚๐˜€๐—ฒ๐—ฟ-๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐—น ๐—ถ๐—บ๐—ฝ๐—น๐—ฒ๐—บ๐—ฒ๐—ป๐˜๐—ถ๐—ฒ๐—ฟ๐—ฒ๐—ป โ€ข House of Data: Grundlagen, Governance, Architektur โ€ข House of AI: Use Cases, Data Scientists, Engineers โ€ข House of 3C: Change, Communication, Community Diese 3 Bereiche sollten zu gleichen Teilen aufgebaut werden. Keiner kann ohne den anderen fรผr nachhaltige Daten- und KI-Implementierung. Die Leads sollten zu Beginn intern aufgebaut werden, extern kรถnnen operative Ressourcen zugekauft werden. ๐Ÿฐ. ๐—•๐—ฎ๐—น๐—ฎ๐—ป๐—ฐ๐—ฒ ๐˜‡๐˜„๐—ถ๐˜€๐—ฐ๐—ต๐—ฒ๐—ป ๐˜‡๐—ฒ๐—ป๐˜๐—ฟ๐—ฎ๐—น ๐˜‚๐—ป๐—ฑ ๐—ฑ๐—ฒ๐˜‡๐—ฒ๐—ป๐˜๐—ฟ๐—ฎ๐—น ๐—ณ๐—ถ๐—ป๐—ฑ๐—ฒ๐—ป Etabliere zentrale Standards und Koordination, befรคhige aber gleichzeitig dezentrale Teams durch Multiplikatoren-Ideen wie KI-Awards, Schulungen und Hackathons. Laut Claudia ist diese Balance eine der grรถรŸten Herausforderungen in der Umsetzung. ๐Ÿฑ. ๐—ฃ๐—ฟ๐—ฎ๐—ด๐—บ๐—ฎ๐˜๐—ถ๐˜€๐—ฐ๐—ต ๐—ฝ๐—น๐—ฎ๐—ป๐—ฒ๐—ป ๐˜€๐˜๐—ฎ๐˜๐˜ ๐˜๐—ต๐—ฒ๐—ผ๐—ฟ๐—ฒ๐˜๐—ถ๐˜€๐—ถ๐—ฒ๐—ฟ๐—ฒ๐—ป Erstelle 6-12-Monats-Plรคne statt langfristiger Strategien. Dokumentiere Erfahrungen systematisch, auch Misserfolge, und passe deine Plรคne regelmรครŸig an. Ich weiรŸ, wie viele Mittelstรคndler vor der groรŸen Aufgabe stehen, Daten- und KI-Kompetenzen und Strukturen im Unternehmen aufzubauen. Claudia's Erfahrungen sind eine echte Schatzkiste. Ganz ohne Buzzwords, Hype oder Selbstprofilierung. Claudia, 1000 Dank fรผr deine Offenheit und dass du uns an deinen Erfahrungen teilhaben lรคsst! Was sagt ihr zum Blueprint? | 22 Kommentare auf LinkedIn
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Wenn Du nochmal bei 0 starten kรถnntest, wie wรผrdest du eine Daten- und KI-Organisation aufbauen?
๐—ช๐—ถ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—บ๐—ถ๐˜๐—ต๐—ถ๐—น๐—ณ๐—ฒ ๐—ฑ๐—ฒ๐—ฟ ๐—ž๐—œ ๐—ฑ๐—ฎ๐˜€ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐—ฒ๐˜ ๐—ป๐—ฒ๐˜‚ ๐—ฑ๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐—ฒ๐—ฟ๐˜
๐—ช๐—ถ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—บ๐—ถ๐˜๐—ต๐—ถ๐—น๐—ณ๐—ฒ ๐—ฑ๐—ฒ๐—ฟ ๐—ž๐—œ ๐—ฑ๐—ฎ๐˜€ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐—ฒ๐˜ ๐—ป๐—ฒ๐˜‚ ๐—ฑ๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐—ฒ๐—ฟ๐˜
Mit seinem AI Mode und dem Agenten Mariner zieht Google eine Plattformschicht รผber das offene Web. Google transformiert sich von einer klassischen Suchmaschine zum zentralen Marktplatz, Assistenten und Zahlungsdienstleister. Nutzer kรถnnen kรผnftig Produkte direkt in der Google-Suche finden, vergleichen, kaufen und bezahlen โ€“ ohne die Plattform zu verlassen. Diese Entwicklung hat weitreichende Folgen fรผr das gesamte Internet-ร–kosystem. Die Auswirkungen treffen nicht nur klassische Online-Hรคndler, sondern auch Marktplatzgiganten wie Amazon, Verlage, รœbersetzungsdienste wie DeepL, Reservierungsanbieter wie OpenTable, Buchungsseiten wie Ticketmaster oder Sprachschulen wie Duolingo. Wer weiterhin sichtbar und relevant bleiben will, muss sich auf die neuen Spielregeln einstellen, in KI-Overviews und Shopping-Graphen prรคsent sein und seine Inhalte fรผr KI-Systeme optimieren. Denn OpenAI baut etwas ร„hnliches auf und auch Amazon bewegt sich in diese Richtung. Der Wettstreit der Plattformen ist damit endgรผltig im KI-Zeitalter angekommen. Weiterlesen auf F.A.Z. PRO Digitalwirtschaft (โ‚ฌ) โ–ถ๏ธŽ https://lnkd.in/e-r8k7upโ‚ฌ Frankfurter Allgemeine Zeitung
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๐—ช๐—ถ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—บ๐—ถ๐˜๐—ต๐—ถ๐—น๐—ณ๐—ฒ ๐—ฑ๐—ฒ๐—ฟ ๐—ž๐—œ ๐—ฑ๐—ฎ๐˜€ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐—ฒ๐˜ ๐—ป๐—ฒ๐˜‚ ๐—ฑ๐—ฒ๐—ณ๐—ถ๐—ป๐—ถ๐—ฒ๐—ฟ๐˜
LEGO hat externe Trainer und Berater rausgeschmissen und seine Fรผhrungskrรคfte zu Coaches auf drei Ebenen ausgebildet, die eine nachhaltige #Lernkultur schaffen.
LEGO hat externe Trainer und Berater rausgeschmissen und seine Fรผhrungskrรคfte zu Coaches auf drei Ebenen ausgebildet, die eine nachhaltige #Lernkultur schaffen.
Erfahrungswerte aus der aktuellen MIT Sloan Management Review (Bahnhofsbuchhandel). โ€žGehe langsam, wenn Du es eilig hast.โ€œ Diese Erkenntnis war es, die zwei weltbekannte dรคnische Unternehmen โ€“ LEGO und VELUX (Dachfenster) โ€“ dazu brachte, ihren Umgang mit Verรคnderung zu รผberdenken. Inmitten digitaler Umbrรผche und wachsender Komplexitรคt stieรŸen beide an die Grenzen ihres bisherigen Erfolgsmodells: Was frรผher als effizient galt, erwies sich plรถtzlich als zu starr und zu oberflรคchlich. Workshops sind oft Strohfeuer. Externe Berater kamen und gingen. Also die Erkenntnis: Verรคnderung muss von innen kommen โ€“ durch Fรผhrung. LEGO und VELUX machten etwas Ungewรถhnliches: Sie bildeten ihre Fรผhrungskrรคfte nicht zu besseren Projektmanagern aus, sondern zu besseren Frage-Stellern. Sie machten sie zu Coaches. Zu Lernbegleitern ihrer eigenen Mitarbeitenden. Zu Menschen, die nicht mit Antworten glรคnzen, sondern mit klugen Fragen Orientierung geben. โธป Element 1: Probleme neu denken โ€“ mit A3 Beide Unternehmen fรผhrten die A3-Methode von Toyota ein โ€“ ein strukturiertes Denkformat, das ein Problem auf einer einzigen DIN-A3-Seite abbildet. Klar. Visuell. Jeder arbeitet damit. Das dazugehรถrige Modell: ๐Ÿ Finding: Das richtige Problem entdecken. ๐Ÿ Facing: Sich ihm mutig stellen. ๐Ÿ Framing: Die eigentliche Herausforderung erkennen. ๐Ÿ Forming: Lรถsungen entwickeln. Diese vier Phasen fรผhrten zu einem neuen Problembewusstsein: Nicht Symptome bekรคmpfen. Ursachen verstehen. Nicht sofort handeln. Erst gemeinsam denken. Teams lernten langsamer und nachhaltiger. โธป Element 2: Lernen im Kollektiv โ€“ Gruppen-Coaching als Mikrokosmos Individuelles Lernen reicht nicht. Also bauten LEGO und VELUX einen Raum fรผr kollektive Reflexion: Gruppencoaching. Dort trafen sich Teams aus Fรผhrungskrรคften in festen Rollen: ein Moderator, eine Fallgeberin, ein Coach und stille Beobachter. In 30 Minuten wurde ein reales Problem durchdacht โ€“ mit klugen Fragen, ehrlichen Perspektiven, geteilten Einsichten. Diese Sessions stรคrkten nicht nur die Problemlรถsefรคhigkeiten โ€“ sie schufen psychologische Sicherheit. Menschen konnten sich verletzlich zeigen. Fehler besprechen. Ideen testen. Und gemeinsam wachsen. โธป Element 3: Coaching-Hierarchie โ€“ Lernen strukturell verankern Um all das nachhaltig zu machen, entwickelten beide Unternehmen eine dreistufige Coaching-Struktur: ๐Ÿ“ First Coach: Die direkte Fรผhrungskraft begleitet das tรคgliche Lernen. ๐Ÿ“ Second Coach: Bereichsleiter coachen die Coaches โ€“ und verbessern deren Fragekompetenz. ๐Ÿ“ Third Coach: Das Top-Management reflektiert die Metaebene und sichert strategische Ausrichtung. So wurde Innovation nicht zur Aufgabe von Externen, sondern zur DNA der Organisation. Lernen wurde nicht delegiert โ€“ sondern verkรถrpert. Erfordert erst Zeit und Geduld, zahlt sich langfristig jedoch aus. | 96 Kommentare auf LinkedIn
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LEGO hat externe Trainer und Berater rausgeschmissen und seine Fรผhrungskrรคfte zu Coaches auf drei Ebenen ausgebildet, die eine nachhaltige #Lernkultur schaffen.
Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performance (Post 2 of 3)
Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performance (Post 2 of 3)
โ€œIn times of change, the learners inherit the earth, while the learned find themselves beautifully equipped for a world that no longer exists.โ€ โ€“ Eric Hoffer In a world of swirling uncertainty, waiting for perfect information is the fastest path to irrelevance.
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Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performance (Post 2 of 3)
Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performanceโ€ฆ
Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performanceโ€ฆ
Are you optimizing for learning, or just chasing certainty? In todayโ€™s world, waiting for perfect information is the fastest way to fall behind. True leadership isnโ€™t about always having the answers; itโ€™s about building teams and systems that learn faster than the world changes. In this latest post (Part 2 of 3), I explore why learning, not knowing, is the real driver of performance. Neuroscience shows that in uncertain times, our ability to adapt and learn together is what sets successful organizations apart. How does your organization balance โ€œdoingโ€ and โ€œlearningโ€? Are you cultivating a culture where learning is the soil, not just a one-off event? Iโ€™d love to hear your experiences and strategies in the comments! Letโ€™s build a conversation around how we can all adapt, grow, and thrive in uncertainty. #Leadership #OrganizationalLearning #ContinuousImprovement #ChangeManagement #FutureOfWork #LearningCulture #Adaptability #HumanCorps | 19 comments on LinkedIn
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Beyond Analysis Paralysis: How Learning, Not Certainty, Drives Performanceโ€ฆ
In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition.
In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition.
In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition. He begins by acknowledging familiar concerns: that GPS erodes our spatial memory, search engines inflate our sense of knowledge, and tools like ChatGPT might diminish creativity or encourage intellectual laziness. These fears, Clark observes, mirror ancient worries, like Platoโ€™s warning that writing would weaken memory, and stem from a deeply ingrained but flawed assumption: the idea that the mind is confined to the biological brain. Clark challenges this perspective with his extended mind thesis, arguing that humans have always been cognitive hybrids, seamlessly integrating external tools into our thinking processes. From the gestures we use to offload mental effort to the scribbled notes that help us untangle complex problems, our cognition has never been limited to what happens inside our skulls. This perspective transforms the debate about AI from a zero-sum game, where technology is seen as replacing human abilities, into a discussion about how we distribute cognitive labour across a network of biological and technological resources. Recent advances in neuroscience lend weight to this view. Theories like predictive processing suggest that the brain is fundamentally geared toward minimising uncertainty by engaging with the world around it. Whether probing a riverโ€™s depth with a stick or querying ChatGPT to clarify an idea, the brain doesnโ€™t distinguish between internal and external problem-solvingโ€”it simply seeks the most efficient path to resolution. This fluid interplay between mind and tool has shaped human history, from the invention of stone tools to the design of modern cities, each innovation redistributing cognitive tasks and expanding what we can achieve. Generative AI, in Clarkโ€™s view, is the latest chapter in this story. While critics warn that it might stifle originality or turn us into passive curators of machine-generated content, evidence suggests a more nuanced reality. The key, Clark argues, lies in how we integrate these technologies into our cognitive ecosystems. https://lnkd.in/gUmxE57w | 41 comments on LinkedIn
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In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition.
๐—”๐˜ ๐—œ/๐—ข 2025, Google ๐˜€๐—ต๐—ผ๐˜„๐—ฒ๐—ฑ ๐˜‚๐˜€ ๐˜„๐—ต๐—ฎ๐˜ ๐—”๐—œ-๐—ณ๐—ถ๐—ฟ๐˜€๐˜โ€ฆ | Andreas Horn | 61 comments
๐—”๐˜ ๐—œ/๐—ข 2025, Google ๐˜€๐—ต๐—ผ๐˜„๐—ฒ๐—ฑ ๐˜‚๐˜€ ๐˜„๐—ต๐—ฎ๐˜ ๐—”๐—œ-๐—ณ๐—ถ๐—ฟ๐˜€๐˜โ€ฆ | Andreas Horn | 61 comments
๐—”๐˜ ๐—œ/๐—ข 2025, Google ๐˜€๐—ต๐—ผ๐˜„๐—ฒ๐—ฑ ๐˜‚๐˜€ ๐˜„๐—ต๐—ฎ๐˜ ๐—”๐—œ-๐—ณ๐—ถ๐—ฟ๐˜€๐˜ ๐—ฅ๐—˜๐—”๐—Ÿ๐—Ÿ๐—ฌ ๐—บ๐—ฒ๐—ฎ๐—ป๐˜€. ๐—›๐—ฒ๐—ฟ๐—ฒโ€™๐˜€ ๐˜„๐—ต๐—ฎ๐˜ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—ฎ๐—ป๐—ป๐—ผ๐˜‚๐—ป๐—ฐ๐—ฒ๐—ฑ: โฌ‡๏ธ The company's flagship developer event Google I/O 2025 was held last night in Mountain View, California. ๐—ง๐—Ÿ๐——๐—ฅ: Google is turning Gemini into the AI operating system for everything โ€” with agents now embedded across Search, Chrome, Workspace, Android, and more. If you donโ€™t have time for the full event, hereโ€™s a curated ๐˜€๐˜‚๐—ฝ๐—ฒ๐—ฟ๐—ฐ๐˜‚๐˜ of the highlights that really matter. ๐—ž๐—ฒ๐˜† ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—œ/๐—ข ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ: 0:00 ๐—œ๐—ป๐˜๐—ฟ๐—ผ โ€“ AI-native from the ground up 0:11 ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐—ฝ๐—น๐—ฎ๐˜†๐˜€ ๐—ฎ ๐—ฃ๐—ผ๐—ธ๐—ฒ๐—บ๐—ผ๐—ป ๐—ด๐—ฎ๐—บ๐—ฒ โ€” memory, reasoning, and code 0:30 ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—•๐—ฒ๐—ฎ๐—บ โ€“ Real-time 3D video chat with AI 1:08 ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐— ๐—ฒ๐—ฒ๐˜ โ€“ Speech-to-speech translation, live 1:27 ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐— ๐—ฎ๐—ฟ๐—ถ๐—ป๐—ฒ๐—ฟ โ€“ AI agents that book, plan, filter, decide 2:07 ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น ๐—–๐—ผ๐—ป๐˜๐—ฒ๐˜…๐˜ โ€“ Gemini gets memory and task awareness 2:40 ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐Ÿฎ.๐Ÿฑ ๐—ฃ๐—ฟ๐—ผ + ๐—™๐—น๐—ฎ๐˜€๐—ต โ€“ New SOTA models, LMArena leader 4:57 ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—”๐˜€๐˜๐—ฟ๐—ฎ โ€“ Multimodal, fast-response agent that sees and hears 5:32 ๐—”๐—œ ๐— ๐—ผ๐—ฑ๐—ฒ โ€“ Overlay for restaurants, bookings, prices, events 7:10 ๐—ฆ๐—ต๐—ผ๐—ฝ๐—ฝ๐—ถ๐—ป๐—ด โ€“ Track, compare, and auto-buy with Google Pay 8:34 ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐—Ÿ๐—ถ๐˜ƒ๐—ฒ โ€“ Screen sharing + live AI guidance 8:59 ๐——๐—ฒ๐—ฒ๐—ฝ ๐—ฅ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—”๐—ด๐—ฒ๐—ป๐˜ โ€“ Upload files, get insights 9:12 ๐—–๐—ฎ๐—ป๐˜ƒ๐—ฎ๐˜€ โ€“ Live, collaborative AI whiteboard 9:31 ๐—š๐—ฒ๐—บ๐—ถ๐—ป๐—ถ ๐—ถ๐—ป ๐—–๐—ต๐—ฟ๐—ผ๐—บ๐—ฒ โ€“ AI understands and acts on any webpage 9:51 ๐—œ๐—บ๐—ฎ๐—ด๐—ฒ๐—ป ๐Ÿฐ โ€“ Next-gen image generation 10:23 ๐—ฉ๐—ฒ๐—ผ ๐Ÿฏ โ€“ Ultra-realistic video model 11:01 ๐—Ÿ๐˜†๐—ฟ๐—ถ๐—ฎ ๐Ÿฎ โ€“ AI-powered music composition 11:56 ๐—™๐—น๐—ผ๐˜„๐˜€ โ€“ Multimodal, promptable AI video creation 12:39 ๐—”๐—ป๐—ฑ๐—ฟ๐—ผ๐—ถ๐—ฑ ๐—ซ๐—ฅ โ€“ AI-first spatial computing 12:57 ๐—ฆ๐—ฎ๐—บ๐˜€๐˜‚๐—ป๐—ด ๐— ๐—ผ๐—ผ๐—ต๐—ฎ๐—ป โ€“ Googleโ€™s XR headset revealed 13:16 ๐—Ÿ๐—ถ๐˜ƒ๐—ฒ ๐—ด๐—น๐—ฎ๐˜€๐˜€๐—ฒ๐˜€ ๐—ฑ๐—ฒ๐—บ๐—ผ โ€“ Gemini + XR = real-time AI overlay Super insightful and forward-looking: Googleโ€™s AI strategy just went full stack. Even if some of these projects donโ€™t make it past the prototype stage, the direction is obvious: AI is being integrated into everything. LLMs โ€” Gemini, in this case โ€” are rapidly becoming the new operating system and everything will be powered by AI Agents across all products. Full keynote: https://lnkd.in/dPFFtyZ9 Supercut: https://lnkd.in/d-eBNGjw Enjoy watching! | 61 comments on LinkedIn
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๐—”๐˜ ๐—œ/๐—ข 2025, Google ๐˜€๐—ต๐—ผ๐˜„๐—ฒ๐—ฑ ๐˜‚๐˜€ ๐˜„๐—ต๐—ฎ๐˜ ๐—”๐—œ-๐—ณ๐—ถ๐—ฟ๐˜€๐˜โ€ฆ | Andreas Horn | 61 comments