AI AffAIrs

Claus Zeißler

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AI Affairs: The podcast for a critical and process-oriented look at artificial intelligence. We highlight the highlights of the technology, as well as its downsides and current weaknesses (e.g., bias, hallucinations, risk management). The goal is to be aware of all the opportunities and dangers so that we can use the technology in a targeted and controlled manner. If you like this format, follow me and feel free to leave a comment.

SEASON 2, EPISODE 1 TRAILER

013 Quicky AI Shock: Why Polish Beats English in LLMs

Episode Numberr: Q013 Titel: AI Shock: Why Polish Beats English in LLMs Is English really the "native tongue" of Artificial Intelligence? For years, Silicon Valley has operated on the assumption that English-centric data leads to the best model performance. But a groundbreaking new study has turned that assumption upside down. In this episode, we investigate the "OneRuler" benchmark—a study by researchers from Microsoft, UMD, and UMass Amherst—which revealed that Polish outperforms English in complex, long-context AI tasks. While Polish scored an 88% accuracy rate, English slumped to 6th place. 🎧 In this episode, we cover: The Benchmark Bombshell: We break down the OneRuler study involving 26 languages. Why did Polish, Russian, and French beat English? And why did Chinese struggle despite massive training data?. Synthetic vs. Analytic Languages: A crash course in linguistics for coders. We explain how "synthetic" languages like Polish use complex inflections (declensions) to pack grammatical relationships directly into words, whereas "analytic" languages like English rely on word order. Does this "dense" information help LLMs hold context better over long sequences?. The "Token Tax" & Fertility: We explore the concept of "Tokenization Fertility". While English is usually cheaper to process (1 token ≈ 1 word), low-resource languages often suffer from "over-segmentation," costing more compute and money. We discuss new findings on Ukrainian tokenization that show how vocabulary size impacts the bottom line for developers. Hype vs. Reality: Is Polish actually "superior"? We speak to the skepticism raised by co-author Marzena Karpińska. Was it the language structure, or just the fact that the Polish test utilized the complex novel Nights and Days while English used Little Women?. The Future of Multilingual AI: What this means for the next generation of foundational models like Llama 3 and GPT-4o. Why "English-centric" might be a bottleneck for AGI, and why leveraging syntactic distances to languages like Swedish or Catalan could build more efficient models. 🔍 Why listen? If you are a prompt engineer, NLP researcher, or data scientist, this episode challenges the idea that "more data" is the only metric that matters. We explore how the structure of language itself interacts with neural networks. Keywords: Large Language Models, LLM, Artificial Intelligence, NLP, Tokenization, Prompt Engineering, OpenAI, Llama 3, Linguistics, Data Science, Multilingual AI, Polish Language, OneRuler, Microsoft Research. Sources mentioned: One ruler to measure them all (Kim et al.) Tokenization efficiency of current foundational LLMs (Maksymenko & Turuta) Could We Have Had Better Multilingual LLMs? (Diandaru et al.) Subscribe for weekly deep dives into the mechanics of AI! ⭐⭐⭐⭐⭐ (Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

2 min
5 DAYS AGO

012 Invisible Tech: When Your Jewelry Spies on You

Episode Numberr: L012 Titel: Invisible Tech: When Your Jewelry Spies on You The smartphone era is ending. Welcome to the age of Invisible Tech. In this episode of "AI Affairs," we explore a future where technology disappears from our hands and attaches directly to our bodies. We are entering the world of Ambient Computing, where Smart Jewelry and Earables do more than just count steps—they know everything about your biology and your conversations. We dive deep into the latest breakthroughs and the hidden dangers of the 6G era. In this episode, we cover: The Rise of Earables: How the Lumia 2 smart earring tracks blood flow to your brain and why the ear is the new wrist for clinical-grade health monitoring. Medical-Grade Jewelry: The evolution of Smart Rings that use bioimpedance to measure blood pressure continuously without cuffs. The "Memory" Necklace: We analyze AI Pendants (like the Rewind Pendant) that record and transcribe every conversation you have. Is it a productivity booster or a privacy nightmare? The 6G Revolution: Why 6G is more than just speed—it’s about turning the network into a global sensor that creates digital twins of our reality. The Death of Anonymity: New research shows that biometric data (like your heartbeat or gait) can re-identify you with near 100% accuracy, even in "anonymized" datasets. Are we ready for a world where our accessories are listening, watching, and analyzing us 24/7? Tune in for a critical look at the future of wearable AI. (Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

14 min
SEASON 1, EPISODE 12 TRAILER

012 Quicky Invisible Tech: When Your Jewelry Spies on You

The smartphone era is ending. Welcome to the age of Invisible Tech. In this episode of "AI Affairs," we explore a future where technology disappears from our hands and attaches directly to our bodies. We are entering the world of Ambient Computing, where Smart Jewelry and Earables do more than just count steps—they know everything about your biology and your conversations. We dive deep into the latest breakthroughs and the hidden dangers of the 6G era. In this episode, we cover: The Rise of Earables: How the Lumia 2 smart earring tracks blood flow to your brain and why the ear is the new wrist for clinical-grade health monitoring. Medical-Grade Jewelry: The evolution of Smart Rings that use bioimpedance to measure blood pressure continuously without cuffs. The "Memory" Necklace: We analyze AI Pendants (like the Rewind Pendant) that record and transcribe every conversation you have. Is it a productivity booster or a privacy nightmare? The 6G Revolution: Why 6G is more than just speed—it’s about turning the network into a global sensor that creates digital twins of our reality. The Death of Anonymity: New research shows that biometric data (like your heartbeat or gait) can re-identify you with near 100% accuracy, even in "anonymized" datasets. Are we ready for a world where our accessories are listening, watching, and analyzing us 24/7? Tune in for a critical look at the future of wearable AI. (Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

2 min
25/12/2025 · BONUS

011 AGI Stages From Narrow AI to Superintelligence

Episode Numberr: L011 Titel: AGI Stages: From Narrow AI to Superintelligence The development of Artificial Intelligence (AI) is progressing rapidly, with Artificial General Intelligence (AGI)—defined as cognitive abilities at least equivalent to human intelligence—coming increasingly into focus. But how can progress towards this human-like or even superhuman intelligence be objectively measured and managed? In this episode, we illuminate a new, detailed framework proposed by leading AI researchers that defines clear AGI stages. This model does not view AGI as a binary concept but as a continuous path of performance and generality levels. Key Concepts of the AGI Framework: Performance and Generality: The framework classifies AI systems based on the depth of their capabilities (Performance) and the breadth of their application areas (Generality). The scale ranges from Level 1: Emerging to Level 5: Superhuman. Current Status: Today's highly developed language models like ChatGPT are classified within this framework as Level 1 General AI (Emerging AGI). This is because they currently lack consistent performance across a broader spectrum of tasks required for a higher classification. Generally, most current applications fall under Weak AI (ANI) or Artificial Narrow Intelligence, which is specialized for specific, predefined tasks (e.g., voice assistants or image recognition). Autonomy and Interaction: In addition to capabilities, the model also defines six Autonomy Levels (from AI as a tool up to AI as an agent), which become technically feasible with increasing AGI levels. The conscious design of human-AI interaction is crucial for responsible deployment. Risk Management: Defining AGI in stages enables the identification of specific risks and opportunities for each phase of development. While "Emerging AGI" systems primarily present risks such as misinformation or faulty execution, higher stages increasingly focus on existential risks (X-risks). Regulatory Context and the Future: Parallel to technological advancement, regulation is progressing. The EU AI Act, the world's first comprehensive AI law, which provides for concrete prohibitions starting February 2025 against high-risk AI systems (such as social scoring), establishes a binding framework for human-centric and trustworthy AI. Understanding the AGI stages serves as a valuable compass for navigating the complexity of AI development, setting realistic expectations for current systems, and charting a course towards a secure and responsible future of human-AI coexistence. (Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

14 min
22/12/2025 · BONUS

011 Quicky AGI Stages From Narrow AI to Superintelligence

Episode Numberr: Q011 Titel: AGI Stages: From Narrow AI to Superintelligence The development of Artificial Intelligence (AI) is progressing rapidly, with Artificial General Intelligence (AGI)—defined as cognitive abilities at least equivalent to human intelligence—coming increasingly into focus. But how can progress towards this human-like or even superhuman intelligence be objectively measured and managed? In this episode, we illuminate a new, detailed framework proposed by leading AI researchers that defines clear AGI stages. This model does not view AGI as a binary concept but as a continuous path of performance and generality levels. Key Concepts of the AGI Framework: Performance and Generality: The framework classifies AI systems based on the depth of their capabilities (Performance) and the breadth of their application areas (Generality). The scale ranges from Level 1: Emerging to Level 5: Superhuman. Current Status: Today's highly developed language models like ChatGPT are classified within this framework as Level 1 General AI (Emerging AGI). This is because they currently lack consistent performance across a broader spectrum of tasks required for a higher classification. Generally, most current applications fall under Weak AI (ANI) or Artificial Narrow Intelligence, which is specialized for specific, predefined tasks (e.g., voice assistants or image recognition). Autonomy and Interaction: In addition to capabilities, the model also defines six Autonomy Levels (from AI as a tool up to AI as an agent), which become technically feasible with increasing AGI levels. The conscious design of human-AI interaction is crucial for responsible deployment. Risk Management: Defining AGI in stages enables the identification of specific risks and opportunities for each phase of development. While "Emerging AGI" systems primarily present risks such as misinformation or faulty execution, higher stages increasingly focus on existential risks (X-risks). Regulatory Context and the Future: Parallel to technological advancement, regulation is progressing. The EU AI Act, the world's first comprehensive AI law, which provides for concrete prohibitions starting February 2025 against high-risk AI systems (such as social scoring), establishes a binding framework for human-centric and trustworthy AI. Understanding the AGI stages serves as a valuable compass for navigating the complexity of AI development, setting realistic expectations for current systems, and charting a course towards a secure and responsible future of human-AI coexistence. (Note: This podcast episode was created with the support and structuring provided by Google's NotebookLM.)

2 min
18/12/2025

010 Is the Career Ladder Tipping AI Automation, Entry-Level Jobs, and the Power of Training

Episode number: L010 Titel: Is the Career Ladder Tipping? AI Automation, Entry-Level Jobs, and the Power of Training. Generative AI is already drastically changing the job market and hitting entry-level workers in exposed roles hard. A new study, based on millions of payroll records in the US through July 2025, found that younger workers aged 22 to 25 experienced a relative employment decline of 13 percent in the most AI-exposed occupations. In contrast, older workers in the same occupations remained stable or even saw gains. According to researchers, the labor market shock is concentrated in roles where AI automates tasks rather than merely augments them. Tasks that are codifiable and trainable, and often taken on as the first steps by junior employees, are more easily replaced by AI. Tacit knowledge, acquired by experienced workers over years, offers resilience. This development has far-reaching consequences: The end of the career ladder is postulated, as the "lowest rung is disappearing". The loss of these entry-level positions (such as in software development or customer service) disrupts traditional competence development paths, as learning ladders for new entrants become thinner. Companies are therefore faced with the challenge of redesigning training programs to prioritize tasks that impart tacit knowledge and critical judgment. In light of these challenges, targeted training and adoption become a crucial factor. The Google pilot program "AI Works" showed that just a few hours of training can double or even triple the daily AI usage of workers. Such interventions are key to closing the AI adoption gap, which exists particularly among older workers and women. The training transformed participants' perception: while many initially considered AI irrelevant, users reported after the training that AI tools saved them an average of over 122 hours per year – exceeding modeled estimates. The increased usage and better understanding of application-specific benefits lead to the initial fear of AI being replaced by optimism, as employees learn to use the technology as a powerful tool for augmentation that creates space for more creative and strategic tasks. In this episode, we illuminate how the AI revolution is redefining entry-level employment, why the distinction between automation and augmentation is critical, and what role continuous professional development plays in equipping workers with the necessary skills for the "new bottom rung". (Note: This podcast episode was created with the support and structuring of Google's NotebookLM.)

13 min
SEASON 1, EPISODE 10 TRAILER

010 Quicky Is the Career Ladder Tipping AI Automation, Entry-Level Jobs, and the Power of Training

Episode number: Q010 Titel: Is the Career Ladder Tipping? AI Automation, Entry-Level Jobs, and the Power of Training. Generative AI is already drastically changing the job market and hitting entry-level workers in exposed roles hard. A new study, based on millions of payroll records in the US through July 2025, found that younger workers aged 22 to 25 experienced a relative employment decline of 13 percent in the most AI-exposed occupations. In contrast, older workers in the same occupations remained stable or even saw gains. According to researchers, the labor market shock is concentrated in roles where AI automates tasks rather than merely augments them. Tasks that are codifiable and trainable, and often taken on as the first steps by junior employees, are more easily replaced by AI. Tacit knowledge, acquired by experienced workers over years, offers resilience. This development has far-reaching consequences: The end of the career ladder is postulated, as the "lowest rung is disappearing". The loss of these entry-level positions (such as in software development or customer service) disrupts traditional competence development paths, as learning ladders for new entrants become thinner. Companies are therefore faced with the challenge of redesigning training programs to prioritize tasks that impart tacit knowledge and critical judgment. In light of these challenges, targeted training and adoption become a crucial factor. The Google pilot program "AI Works" showed that just a few hours of training can double or even triple the daily AI usage of workers. Such interventions are key to closing the AI adoption gap, which exists particularly among older workers and women. The training transformed participants' perception: while many initially considered AI irrelevant, users reported after the training that AI tools saved them an average of over 122 hours per year – exceeding modeled estimates. The increased usage and better understanding of application-specific benefits lead to the initial fear of AI being replaced by optimism, as employees learn to use the technology as a powerful tool for augmentation that creates space for more creative and strategic tasks. In this episode, we illuminate how the AI revolution is redefining entry-level employment, why the distinction between automation and augmentation is critical, and what role continuous professional development plays in equipping workers with the necessary skills for the "new bottom rung". (Note: This podcast episode was created with the support and structuring of Google's NotebookLM.)

2 min
11/12/2025

009 The Human Firewall: How to Spot AI Fakes in Just 5 Minutes

Episode: L009 Titel: The Human Firewall: How to Spot AI Fakes in Just 5 Minutes The rapid development of generative AI has revolutionized the distinction between real and artificial content. Whether it’s deceptively real faces, convincing texts, or sophisticated phishing emails: humans are the last line of defense. But how good are we at recognizing these fakes? And can we quickly improve our skills? The Danger of AI Hyperrealism Research shows that most people without training are surprisingly poor at identifying AI-generated faces—they often perform worse than random guessing. In fact, fake faces are frequently perceived as more realistic than actual human photographs (hyperrealism). These synthetic faces pose a serious security risk, as they have been used for fraud, misinformation, and to bypass identity verification systems. Training in 5 Minutes: The Game-Changer The good news: A brief, five-minute training session focused on detecting common rendering flaws in AI images—such as oddly rendered hair or incorrect tooth counts—can significantly improve the detection rate. Even so-called super-recognizers, individuals naturally better at face recognition, significantly increased their accuracy through this targeted instruction (from 54% to 64% in a two-alternative forced choice task). Crucially, this improved performance was based on an actual increase in discrimination ability, rather than just heightened general suspicion. This brief training has practical real-world applications for social media moderation and identity verification. The Fight Against Text Stereotypes Humans also show considerable weaknesses in detecting AI-generated texts (e.g., created with GPT-4o) without targeted feedback. Participants often hold incorrect assumptions about AI writing style—for example, they expect AI texts to be static, formal, and cohesive. Research conducted in the Czech language demonstrated that individuals without immediate feedback made the most errors precisely when they were most confident. However, the ability to correctly assess one's own competence and correct these false assumptions can be effectively learned through immediate feedback. Stylistically, human texts tend to use more practical terms ("use," "allow"), while AI texts favor more abstract or formal words ("realm," "employ"). Phishing and Multitasking A pressing cybersecurity issue is human vulnerability in the daily workflow: multitasking significantly reduces the ability to detect phishing emails. This is where timely, lightweight "nudges", such as colored warning banners in the email environment, can redirect attention to risk factors exactly when employees are distracted or overloaded. Adaptive, behavior-based security training that continuously adjusts to user skill is crucial. Such programs can boost the success rate in reporting threats from a typical 7% (with standard training) to an average of 60% and reduce the total number of phishing incidents per organization by up to 86%. In summary: humans are not helpless against the rising tide of synthetic content. Targeted training, adapted to human behavior, transforms the human vulnerability into an effective defense—the "human firewall". (Note: This podcast episode was created with the support and structure provided by Google's NotebookLM.)

15 min

AI Affairs: The podcast for a critical and process-oriented look at artificial intelligence. We highlight the highlights of the technology, as well as its downsides and current weaknesses (e.g., bias, hallucinations, risk management). The goal is to be aware of all the opportunities and dangers so that we can use the technology in a targeted and controlled manner. If you like this format, follow me and feel free to leave a comment.

Creator

Claus Zeißler
Years Active

2025 - 2026
Episodes

25
Rating

Clean
Copyright

© Claus Zeißler
Show Website

AI AffAIrs