Camembert oder Tortendiagramm? KI erkennt bei Übersetzungen Muster, aber sie versteht keinen Sinn. Was auf Duolingo noch lustig ist, wird problematisch, wenn Maschinen dort Urteile fällen, wo Menschen verstehen müssten.
Vom Hype zum Backlash – was bleibt vom Nachhaltigkeitsversprechen, wenn der Rückenwind fehlt? Eine kritische Bestandsaufnahme zwischen Compliance, Kulturkampf und Kurskorrektur.
After researchers sparked outrage with a secret AI experiment on Reddit, the platform considers adopting Sam Altman’s World ID system via a device called the Orb. But is biometric identity the solution, or just another dubious business model?
AI doesn’t just train on academic or artistic content. Increasingly, it feeds on blogs, guides, and independent journalism; any content that shows human care, credibility, or craft. Summarized and displayed in search results, this content becomes invisible at the source. Welcome to a world where creators are reduced to training fodder.
Machines improve performance. Humans seek meaning. This piece explores why learning is more than optimization – and what we lose when we confuse adaptation with transformation.
Some still believe that training large language models (LLMs) on copyrighted content is a form of “democratizing knowledge.” But when you look closely at how these models actually handle the material they ingest, the picture looks a lot less heroic – and a lot more extractive.
My academic legacy? A monograph, a handful of articles—and now a starring role in training Meta’s Llama 3. No royalties. No citations. Just silent swallowing by a machine. A story of vanishing recognition in the age of AI.
Viele Unternehmen verabschieden sich still und leise von ihren Diversity-Zielen. Nicht aus Zwang, sondern aus Kalkül. Was bleibt von Haltung, wenn sie nicht mehr nützt?
From “AI can do all jobs” to “Humans are invaluable!”: Klarna’s AI journey is a masterclass in hype whiplash. But behind the cringe, the CEO’s rhetoric surfaces real ethical tensions. What happens when honesty about AI and jobs is no longer whispered in executive suites – but shouted?
Radiologen, Lehrer, Ärzte – alles ersetzbar? KI-Prognosen sind heute allgegenwärtig – aber oft erschreckend eindimensional. Was sie meist ausklammern: den Sinn, den Menschen in ihre Arbeit bringen. Ein Plädoyer für mehr Menschlichkeit – mit einem Zitat von Viktor Frankl.
Some say facial recognition and AI can assess your potential by analyzing your face. But what seems like innovation may be pseudoscience at scale – and a threat to privacy, fairness, and human rights.
AI makes mistakes differently from humans. And that’s a good thing. This post explores why we shouldn’t train machines to fail like humans and why weirdness might be an important safety feature of AI.
