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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. ...

Opinion Replaces Thinking – A Societal Symptom We live in a world where information is omnipresent – and yet, thinking seems increasingly rare. For decades, we’ve been offered a reality based less on independent reflection and more on constant overstimulation. So who is still surprised when people consume more than they question? A widespread practice is the uncritical adoption of opinions. Thinking is outsourced – to influencers, algorithms, group identities. The individual intellect retreats behind the comfort of belonging. The ability to verify and evaluate is not lost – it is simply no longer demanded. ...

The Illusion of Intelligence: Why Deep Learning Alone is Not Enough In the age of AI hype, Deep Learning is often hailed as the magical ingredient behind the “intelligence” of large language models (LLMs) like GPT, Gemini, or Claude. But here’s a necessary reality check: Deep Learning alone is not enough. The true power lies in the Internet. The architecture may be cutting-edge, but it’s the data that gives these systems their apparent brilliance. ...

Omission as a Tool of Manipulation: Three Case Studies – Pandemic, Climate, Middle East Omitting information is a subtle yet powerful form of manipulation. It doesn’t create overt “fake news” but skews perception through selectivity and loss of context. This article documents three critical topics – COVID-19, the climate crisis, and the Middle East conflict – where factual distortion through omission has been demonstrably present. 🦠 COVID-19 Pandemic: Fear through Imagery, Distortion through Scandalization Alternative Perspective: CORONA.film Series The six-part documentary series “CORONA.film” by Gunther Merz and Robert Cibis (OVALmedia), featuring Wolfgang Wodarg, offers a critical counter-narrative to mainstream COVID reporting. Topics include data manipulation, pathology, vaccine side effects, and legal investigations. Parts 1–4 were temporarily unavailable and later republished by MWGFD. ...

“Will we still need humans?” “Not for most things.” — Bill Gates, 2025 Watch the video Hosted by the Devil The image of the devil entering the world through data centers is just a symbol — a metaphor for a far more complex, systemic conspiracy. It’s not just about ruthless ambition, but about deeply embedded mechanisms that govern the development and deployment of artificial intelligence. The illusion of neutrality serves as a sophisticated lever for power expansion — a tool to consolidate control and deepen societal fragmentation. ...

🧠 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. ...

📘 “What Can Be Done About Hate Speech and Fake News?” A paper from FH Kiel attempts to provide answers – but mainly delivers one thing: the controlled opposite of enlightenment. 🧩 The Content, Disenchanted This 161-page document addresses topics like deepfakes, social bots, and platform responsibility – but it remains superficial and avoids critical questions: Who constructs terms like “hate speech”? Why is trust in official narratives eroding? What role does language play in structurally controlled communication? Instead, it is dominated by: ...

“It’s like comparing apples and pears — but what if you don’t know what either is? Welcome to GPT.” The debate around artificial intelligence often ignores a critical fact: Large Language Models like GPT do not understand semantic concepts. They simulate understanding — but they don’t “know” what an apple or a pear is. This isn’t just academic; it has real-world implications, especially as we increasingly rely on such systems in decision-making. ...

“I only believe the evidence of my sensors.” – Bomb No. 20, Dark Star (1974) The Bomb That Thought In the film Dark Star, a nuclear bomb refuses to abort its detonation. Its reasoning: it can only trust what its sensors tell it – and they tell it to explode. [Watch video – YouTube, scene starts around 0:38: “Only empirical data”] This scene is more than science fiction – it’s an allegory for any data-driven system. Large Language Models like GPT make decisions based on what their “sensors” give them: text tokens, probabilities, chat history. No understanding. No awareness. No control. ...