Pentagon orders bases to plan for 90 days without the grid as Britain urges families to stockpile food and water fortune.com/2026/10/26/penta…

Well.. As John stated, this reminds us of the Kenwood case from Las Vegas. Allegedly this is footage of a Skinwalker or a dogman. What's the details of this case? Full video. #ufox #ufotwitter

7th Project PURSUE UAP Document/Video Tranche Released. war.gov/UFO/release/07/ #ufox #ufotwitter

AI hallucinations Cases where an AI system generates content that is factually wrong, fabricated, or logically inconsistent, yet presents it confidently as if it were true. The term is a metaphor. Unlike human hallucinations (perceptions of things that aren’t there), AI “hallucinations” are statistical generation errors. The model is not seeing or believing false things; it is producing the most plausible-sounding continuation of text (or pixels, or audio) according to patterns learned during training. Why they happen Large language models are trained to predict the next token (word piece) based on vast amounts of text. They optimize for fluency and statistical likelihood, not for truth or grounding in external reality. This leads to several failure modes: - Pattern completion over knowledge: The model interpolates from training data. If something looks like a pattern it has seen, it will complete it even if the specific claim is false or never appeared. - No external grounding by default: Without tools (search, databases, calculators, code execution), the model relies only on its parametric knowledge—weights that compress statistical associations, not a verified knowledge base. - Training data issues: Errors, biases, outdated information, and fictional content in the training corpus can be reproduced or mixed. - Sampling and randomness: Generation involves probabilistic sampling. Higher temperature or creative settings increase the chance of novel (and sometimes incorrect) outputs. - Overconfidence: Models are often trained or reinforced to sound authoritative. They rarely say “I don’t know” unless explicitly encouraged. - Long-context or multi-step reasoning drift: In long responses or multi-step chains, small errors can compound. These are inherent to current autoregressive generative architectures, not just bugs in one system. Common forms - Factual invention: Non-existent books, papers, people, quotes, or events. Classic example: lawyers submitting ChatGPT-generated legal briefs that cited entirely fabricated cases. - Wrong details on real things: Incorrect dates, numbers, attributions, or technical specifications stated with high confidence. - Logical or mathematical errors: Especially on problems that require precise multi-step reasoning rather than pattern matching. - Citation fabrication: Invented sources or real sources that do not support the claim. - In other modalities: Image models generating extra fingers, impossible geometry, or text that is garbled; video models producing inconsistent physics. Why they matter Hallucinations undermine reliability in high-stakes uses (medicine, law, science, news, engineering). They also create a false sense of authority because the output is usually fluent and well-structured. Users can easily mistake confident nonsense for knowledge. All current large generative models can hallucinate, including systems that try to reduce it through retrieval-augmented generation (RAG), tool use, fine-tuning, or reinforcement learning from human feedback. Mitigation reduces frequency and severity but does not eliminate the problem. How people and systems reduce them - Retrieval and tools: Ground answers in search results, databases, or code execution instead of pure parametric memory (this is why many modern systems, including tool-using AIs, search or calculate when precision matters). - Prompting techniques: Ask for step-by-step reasoning, sources, confidence levels, or explicit “I don’t know” behavior. - Verification layers: External fact-checkers, citation checkers, or human review. - Training improvements: Better data filtering, preference tuning that rewards honesty and calibration, and architectures that better separate knowledge from generation. - User practice: Treat AI output as a draft or hypothesis. Cross-check important claims, especially numbers, names, citations, and domain-specific facts.