AI

Introduction: The Great Misunderstanding Since the hype around ChatGPT, Claude, Gemini, and others, artificial intelligence has become a buzzword. Marketing materials promise assistants that understand, learn, reason, write, and analyze. Startups put “AI-powered” on every second website. Billions change hands. Entire industries are built on the illusion. And yet, in the overwhelming majority of cases: These are not intelligent systems. They are statistically trained text generators optimized for plausibility—not truth, not understanding, not meaning. ...

From Thinking to Tapping: How Technology Took Over Our Brains A simple timeline through digital dumbing-down—deep, clear, and no-nonsense Introduction – Honest question: When was the last time you truly thought? Not just googled, not just clicked “OK,” not just followed GPS—but thought for yourself? The uncomfortable truth: the smarter our devices became, the lazier our brains got. We’ve outsourced more and more: phone numbers, routes, decisions, even our memories. ...

What do children’s games, irony, and artificial intelligence have in common? A whole lot of misunderstanding.

Introduction This in-depth analysis provides insight into the current landscape of artificial intelligence, highlighting major players like Meta, OpenAI, and Microsoft and their ties to the World Economic Forum (WEF). It explores data verification practices, platform strategies, ideological and cultural biases in training data, decentralized alternatives, and the complex network of power and influence shaping AI governance globally. 1. Data Verification: Meta, OpenAI, Microsoft Meta (Facebook, WhatsApp, Instagram) Extensive data collection for advertising purposes. Metadata sharing between WhatsApp and Facebook. Major fines: €1.2B (EU), $5B (USA). Criticism: repeated data protection violations and low transparency. OpenAI Default use of user input for training models. Temporary block in Italy (2023) over GDPR issues. Fine: €15M for GDPR violation. Response: opt-out options, API training disabled, RLHF introduced. Microsoft Azure ensures GDPR-compliant data hosting in the EU. Few data scandals, focus on antitrust litigation. Strong integration with OpenAI (Azure, product embedding). 2. Platform Strategies: Openness vs. Control Meta Open-source infrastructure (e.g., PyTorch, LLaMA2). Closed recommendation algorithms. Strategic goal: set standards via open-source frameworks. OpenAI Shift from open to proprietary. GPT models not openly released. Plugins semi-open, closed API remains the default. Microsoft Supports open-source tools (e.g., VS Code, GitHub). Azure + CoPilot = proprietary monetization. Mix of open development and closed product monetization. 3. Partnerships: OpenAI–Microsoft & Governance $13B investment from Microsoft into OpenAI. Exclusive access to GPT-4. Microsoft holds observer status on the OpenAI board. Integration into Bing, Office 365, Azure. Under review by UK’s CMA and the U.S. FTC. 4. WEF Narratives and Digital Governance Core Narratives Resilience: preparing for global crises. Digital Governance: multistakeholder control of tech. Stakeholder Capitalism: prioritizing social and environmental responsibility. Pandemic Preparedness: promoting global cooperation. The Great Reset: post-COVID economic realignment. Platforms & Tools Strategic Intelligence Maps. Global C4IR centers. ESG-aligned Stakeholder Metrics. Jobs Reset Initiative. 5. Ideological and Cultural Biases in LLMs Root Causes 85–95% of data in English (Common Crawl, Wikipedia, books). Underrepresentation of non-Western perspectives. Bias from Western-centric media and stereotypes. Effects Dominance of Western narratives. Poorer performance in low-resource languages. Sentiment bias against non-Western names and topics. Benchmarks TruthfulQA, StereoSet, CrowS-Pairs, CAMeL. Corrective measures: RLHF, ethics filters, fine-tuning. 6. Decentralized AI Alternatives OpenAssistant (LAION) Open-source chatbot with RLHF. Transparent data and models. Not yet at GPT-4 level, but progressing. Petals Peer-to-peer hosting of large models. Community-driven, experimental. Bittensor (TAO) Blockchain-based AI marketplace. Tokenized model quality and reputation. Golem Decentralized compute power for AI. GPU rental via a market-based mechanism. Mistral AI European provider of fully open models (e.g., Mistral 7B). Apache 2.0 license; high quality with small size. Governance Tools OpenRAIL licenses (Responsible AI). Data Nutrition Labels, Open Ethics Label. 7. Network Analysis: WEF, Big Tech, Foundations Key Connections Microsoft–OpenAI–WEF: investments, board observer role, Azure integration. Meta–WEF: YGL network, task forces, C4IR participation. Gates Foundation–WEF: co-founding CEPI, COVID partnerships. Open Philanthropy: funding OpenAI, effective altruism links. Chan-Zuckerberg Initiative: open science, AI projects, indirect Meta ties. Power Structure Financial flows, board overlaps, institutional alignment. Central players coordinate on AI policy. Limited transparency – mapping initiatives are essential. Conclusion The current AI ecosystem is heavily shaped by Big Tech concentration, strategic investments, and interwoven governance structures. While regulatory and open-source initiatives are advancing, dominant players continue to influence the direction of global AI. Decentralized, open alternatives are emerging, but their future depends on scaling, adoption, and public support. Transparent network analysis remains key to demystifying power and shaping equitable AI futures. ...

Introduction: The Matrix Is Here – It Just Looks Different AI is not the Matrix from the movies. It is more dangerous – because it is not perceived as deception. It works through suggestions, text, tools – not through virtuality, but through normalization. AI does not simulate a world – it structures ours. And no one notices, because everyone thinks it’s useful. 1. Invisible but Everywhere – The New Ubiquity The integration of AI into daily life is total – but silent: ...

Introduction: The Human as a Data Record Modern AI-based surveillance systems have created a new reality: Humans are no longer seen as citizens or subjects – but as datasets. Objects of algorithmic evaluation. The Stasi could watch people. AI evaluates them. Technological Basis: AI, Cameras, Pattern Recognition With AI-powered facial recognition, systems don’t just identify individuals – they analyze behavior patterns, emotions, and movements. Systems like Clearview AI or PimEyes turn open societies into statistical sampling zones. ...

May 4, 2025 – Alexander Renz Translations: DE GPT and similar models simulate comprehension. They imitate conversations, emotions, reasoning. But in reality, they are statistical probability models, trained on massive text corpora – without awareness, world knowledge, or intent. What Does GPT Actually Do? GPT (Generative Pretrained Transformer) is not a thinking system, but a language prediction model. It calculates which token (word fragment) is most likely to come next – based on the context of previous tokens. ...

ELIZA was a parrot with rules – GPT is a chameleon with probabilities. Yet both are symbolic manipulators without true understanding.

What Is “AI” Really? The term “Artificial Intelligence” suggests thinking, awareness, and understanding. But models like GPT are merely statistical pattern completers – they understand nothing. Statistics ≠ Thinking GPT doesn’t choose the next word because it makes sense, but because it is likely. What it produces is linguistic surface without depth – impressive, but hollow. ELIZA vs. GPT – Large-Scale Symbol Manipulation Both ELIZA (1966) and GPT-4 (2023) are based on symbol processing without meaning. The illusion comes from plausible language – not from comprehension. ...