“Will we still need humans?” “For most things, no.” — Bill Gates, 2025 Watch the video The Original Quote “People think, wow, this is a bit eerie. It’s completely uncharted territory. Will we still need humans? — Ah, for most things, no. Ah, you know, we’ll just… We’ll make it work. I mean…” Bill Gates: The Architect of Dependency Gates’ genius lies not in invention but in systematizing lock-in. His playbook since the 1980s: ...
🧠 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. ...
“Please tell me more about that.” – ELIZA If you want to understand how language simulation worked before the AI boom, this is your starting point: 🔗 Try ELIZA now in your browser This demo replicates Joseph Weizenbaum’s original 1966 program. It simulates a Rogerian psychotherapist and responds using simple pattern rules – no understanding, no memory, no intelligence. Why ELIZA still matters ELIZA’s success surprised even Weizenbaum. Many users felt understood by a program that merely mirrored their statements with generic replies. ...
The Book Nobody Wrote AI on Amazon – and How Words Become Nothing Again It feels like a bad joke. A “self-help” guide about narcissistic abuse, packed with clichés, buzzwords, and pseudo-therapeutic fluff – supposedly written by a human, but most likely generated by a language model. Sold on Amazon. Ordered by people in distress. And no one checks if the book was ever seen by an actual author. The New Business Model: Simulation Amazon has long since transformed. From a retailer to a marketplace of content that just feels “real enough.” Real authors? Real expertise? Real help? Not required. It’s enough for an algorithm to produce words that sound like advice. Text blocks that are grammatically correct, friendly in tone, and SEO-optimized. ...
What actually happens to your prompt before an AI system responds? The answer: a lot. And much of it remains intentionally opaque. This post presents scientifically documented control mechanisms by which transformer-based models like GPT are steered – layer by layer, from input to output. All techniques are documented, reproducible, and actively used in production systems. 1. Control Begins Before the Model: Input Filtering Even before the model responds, the input text can be intercepted and replaced – for example, through a “toxicity check”: ...
🧭 Perspectives in Comparison Not everyone sees GPT and similar systems as mere deception. Some voices highlight: that LLMs enable creative impulses that they automate tasks once reserved for humans that they are tools – neither good nor evil, but shaped by use and context Others point out: LLMs are not intelligent – they only appear to be they generate trust through language – but carry no responsibility they replicate societal biases hidden in their training data So what does this mean for us? This site takes a critical stance – but does not exclude other viewpoints. On the contrary: Understanding arises through contrast. ...
