Welcome Jack Roberts – For everybody out there: Jack is running a vivid and helpful AI-community – check it out! Lautaro Casetta Samina Rao Mahesh Umarane Daniel Lewis Johnnie James Thomas Tzouridis Abdullah Gassim Evelyn. Emily Jane Sanz This is how to level up in a Skool-community. One of the best examples for community engagement on Skool is GOOSIFY

Welcome (some of you: again) and good to have you here in this community! Post your Social Media profile and your website in the comments and interconnect 🤝 👏 👥 ☀️ Terrie Gholston Anthony Earp Bansari Panchal Usman Karamat Stephen Corpas Holger Peschke 🔥 (the flame makes me link to the Level-up-Article) Kris Pothana Mwanjo Molongo Germans Frolovs Joel Wilson Abdurrahman Ibrahim Frances P Sd N Future of Work Professor Pei Mei Mick Holloway Camila Drego Manuel Betancurt Shawn S Ivan Wong Sylvia Sara

Thank you, @Joel Wilson, for recommending the wonderful LLM runs a shop-video in your community. This forces me to recommend the channel: https://www.youtube.com/@FingoChat/videos

To level up in a Skool community, focus on consistent, high-value engagement: – Post helpful content that sparks discussion or solves a common problem. – Comment meaningfully on other members’ posts to build relationships. – Show up regularly — short daily interactions matter more than rare long ones. – Ask thoughtful questions that invite shared experience and insight. – Support others' progress — give feedback, encouragement, or tools they can use. Skool rewards relevance, reliability, and relational behavior. Be visible for the right. Be the best reason for other members to be here .... Photo credit: You see my picture of „Bayerische Staatsbibliothek, Munich, Germany“ (a wonderful place to stay forever for a day at least.)

You ask why does AI confidently state false information? Models cannot distinguish between true and false - they generate statistically likely text. This is a proof that in AI is no intelligence in a narrow sense. The confidence illusion within AI Large language models rely on transformer-based architectures that optimize token likelihood but ignore ontological grounding, so the generated outputs resemble plausible knowledge graphs rather than verified fact-structures. This creates the origin for AI_hallucinations, semantic drift, and unintended consequences, when AI systems enter domains with high-stakes decision-making such as healthcare diagnostics, jurisprudence, corporate auditing, or scientific publishing. The illusion of credibility is amplified by semantically clustered n-grams like “evidence shows,” “the data confirms,” or “studies prove,” which co-occur with domain-specific entities in the training corpus, thereby reinforcing a false perception of validity. Training data contains authoritative writing style. Models learn to mimic confidence markers ("clearly," "obviously," "certainly") without understanding truth. The lack of understanding is equal with the absence of real intelligence. Psychology research shows humans trust confident-sounding AI 65% more than uncertain AI, even when wrong. A typical sign for confirmation bias. "If it seems plausible, it is, for sure ..." Real-world consequences: - Medical misdiagnosis from AI tools - Legal briefs with fictional citations - Financial advice causing losses - Educational misinformation spread This phenomenon reflects a multidimensional interplay between truth validation, epistemic uncertainty, and linguistic probability distributions. Large language models rely on transformer-based architectures that optimize token likelihood but ignore ontological grounding, so the generated outputs resemble plausible knowledge graphs rather than verified fact-structures. This creates the origin for AI_hallucinations, semantic drift, and unintended consequences, when AI systems enter domains with high-stakes decision-making such as healthcare diagnostics, jurisprudence, corporate auditing, or scientific publishing.