Conspiracy Author David Icke Repeatedly 'Named and Shamed' in New Jeffrey Epstein Files - The People's Voice
Oh dear!
Though I'm not entirely sure how reliable a source these guys are, that's certainly very interesting isn't it, regardless of if it's true or not?
It's either true, or Dave's said / seen / come across something he really shouldn't have?
https://thepeoplesvoice.tv/conspiracy-author-david-icke-repeatedly-named-and-shamed-in-new-jeffrey-epstein-files/
Oh dear!
Though I'm not entirely sure how reliable a source these guys are, that's certainly very interesting isn't it, regardless of if it's true or not?
It's either true, or Dave's said / seen / come across something he really shouldn't have?
https://thepeoplesvoice.tv/conspiracy-author-david-icke-repeatedly-named-and-shamed-in-new-jeffrey-epstein-files/
The People's Voice
Conspiracy Author David Icke Repeatedly 'Named and Shamed' in New Jeffrey Epstein Files
Disgraced conspiracy author David Icke has been named and shamed at least 15 times in Congress's latest release of Jeffrey Epstein files— the very documents that peel back the curtain on the financier's global pedophile network.
Sam Fisher (Data Drops) pinned «Conspiracy Author David Icke Repeatedly 'Named and Shamed' in New Jeffrey Epstein Files - The People's Voice Oh dear! Though I'm not entirely sure how reliable a source these guys are, that's certainly very interesting isn't it, regardless of if it's true…»
Forwarded from foxblog channel
Family demand reform after police officer cleared in fatal Duchess of Edinburgh escort crash https://www.itv.com/news/2025-11-18/family-demand-reform-after-officer-cleared-in-royal-escort-crash
There's a poll up on my YouTube channel, for how you all would like my series on the System to proceed. Is the gradual, more detailed dives, into who / where / what they are, what you all want and if so, which topic?
But, if that's not what you want, would you prefer a breakdown of the structures in place to hold their power?
http://youtube.com/post/UgkxBJ685uU2-Ka4K0khZri8sCBW7Ntq6WPA?si=7etjYXpJG_nrFQqC
Please place your vote and if you've a suggestion, or something you want to say on this series, lemme know in the comments.
But, if that's not what you want, would you prefer a breakdown of the structures in place to hold their power?
http://youtube.com/post/UgkxBJ685uU2-Ka4K0khZri8sCBW7Ntq6WPA?si=7etjYXpJG_nrFQqC
Please place your vote and if you've a suggestion, or something you want to say on this series, lemme know in the comments.
YouTube
Poll from Sam Fisher
What do you want the topic to be in the next episode of The System 'For Dummies' I've set out a selection of topics, for in depth episodes on just that topic...
❤2🙏1
Sam Fisher (Data Drops) pinned «There's a poll up on my YouTube channel, for how you all would like my series on the System to proceed. Is the gradual, more detailed dives, into who / where / what they are, what you all want and if so, which topic? But, if that's not what you want, would…»
Organ Harvesting: The Global Black Market They Don’t Want You to See
https://youtube.com/watch?v=MWIqDj8u81w&si=5nS4TxiT_bc__q0X
https://youtube.com/watch?v=MWIqDj8u81w&si=5nS4TxiT_bc__q0X
YouTube
Organ Harvesting: The Global Black Market They Don’t Want You to See
Across every continent, a hidden economy thrives where human organs are bought and sold like commodities. Victims are kidnapped, trafficked, or deceived into surrendering their bodies, feeding a billion-dollar black market that stretches from Latin America…
👍1
Forwarded from Movie Night
Fifty Dead Men Walking 2008 Drama/Thriller
Martin McGartland joins the I.R.A. and feeds information to Britain's Special Branch Agent Fergus.
Martin McGartland joins the I.R.A. and feeds information to Britain's Special Branch Agent Fergus.
Forwarded from Jade Helm 15 Rehash (ned)
AI responses may include mistakes.
The ability of
large language models (LLMs) to handle highly specific, subtle linguistic constraints (such as exclusively using positive phrasing related to "estimation" or "approximation" while avoiding any negative phrasing related to "precision" or "exactitude") is a current area of development. This task requires a high degree of nuance, contextual awareness, and flexible constraint satisfaction that pushes the limits of current models.
While current AI models are highly fluent in generating human-like text, they still struggle with deep logical reasoning, strict consistency, and interpreting complex, multi-layered human instructions involving antonymic constraints. The model may correctly identify the request but struggle with the consistent application of the positive constraint throughout the entire response without falling back on common phrasing patterns it learned during training.
AI capabilities are currently estimated to be doubling approximately every seven months, with a strong focus on improving reasoning and reliability. Based on current trends, significant improvement in handling such highly nuanced and complex linguistic constraints could be expected within the next 1 to 3 years, as researchers focus on enhancing models' ability to adapt to mistakes and follow complex, non-standard instructions more robustly.
Future improvements in training methodologies (such as incorporating more robust reinforcement learning from human feedback and advanced "chain-of-thought" prompting internally) are expected to address these types of challenges.
The ability of
large language models (LLMs) to handle highly specific, subtle linguistic constraints (such as exclusively using positive phrasing related to "estimation" or "approximation" while avoiding any negative phrasing related to "precision" or "exactitude") is a current area of development. This task requires a high degree of nuance, contextual awareness, and flexible constraint satisfaction that pushes the limits of current models.
While current AI models are highly fluent in generating human-like text, they still struggle with deep logical reasoning, strict consistency, and interpreting complex, multi-layered human instructions involving antonymic constraints. The model may correctly identify the request but struggle with the consistent application of the positive constraint throughout the entire response without falling back on common phrasing patterns it learned during training.
AI capabilities are currently estimated to be doubling approximately every seven months, with a strong focus on improving reasoning and reliability. Based on current trends, significant improvement in handling such highly nuanced and complex linguistic constraints could be expected within the next 1 to 3 years, as researchers focus on enhancing models' ability to adapt to mistakes and follow complex, non-standard instructions more robustly.
Future improvements in training methodologies (such as incorporating more robust reinforcement learning from human feedback and advanced "chain-of-thought" prompting internally) are expected to address these types of challenges.
Forwarded from 𝚆𝚑𝚘𝚕𝚒𝚜𝚒𝚝𝚒𝚌 𝚁𝚊𝚖𝚋𝚕𝚒𝚗𝚐𝚜 (𝙽𝚘𝚟𝚊𝚂𝚔𝚢☾∘∙⊱⋅•⋅)
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Forwarded from Jade Helm 15 Rehash (ned)
AI responses may include mistakes.
As a Large Language Model (LLM), my "knowledge" is a frozen, statistical representation of the vast text and code data I was trained on. I do not "learn" in real time from interactions with the world, nor do I think or understand in the human sense; instead, I process information based on my initial training and augment it with targeted, real-time search when necessary to provide up-to-date answers.
Technical Explanation: Data, Storage, and Limits
Data Storage and the Knowledge Base
My core knowledge is embedded within the value of billions or trillions of parameters in a vast neural network.
Parameters: These are numerical values that represent the strength of connections within the neural network. During the training process, the model adjusts these parameters to recognize patterns, relationships, and "facts" in the training data. This process is akin to a form of data compression, storing information within the network's structure rather than a traditional, searchable database.
Training Data: The data I was trained on amounts to petabytes (millions of gigabytes) of text and code scraped from the internet, books, and articles. This data is processed through complex pipelines to be filtered, cleaned, and tokenized.
Tokens: Text is broken down into smaller units called tokens (roughly three-quarters of a word). The model learns the statistical relationships between these tokens.
Limits on Learning and Real-Time Search
My knowledge base has specific limitations:
Knowledge Cutoff: My internal "knowledge" is static and limited to the information available up to my last training date. I do not automatically update my internal parameters with new information in real-time.
No Long-Term Memory (across sessions): Each new conversation or session is typically treated as a standalone interaction. I do not remember previous conversations unless the context of the prior conversation is explicitly provided in the current prompt.
Real-time Augmentation: When a user asks a question about recent events or needs an up-to-date fact (like current weather or stock prices), I use an external search mechanism to retrieve the latest information from the internet. This process is called Retrieval-Augmented Generation (RAG). The retrieved information is provided as context for me to formulate an accurate and current response, but it is not permanently added to my core training data.
Hallucinations: Because my primary function is to predict the most statistically probable next word, I can sometimes generate plausible-sounding but entirely incorrect or fabricated information ("hallucinations"). This happens because I am mimicking patterns, not verifying facts with a deep, conscious understanding.
In Simple Children's Terms
Imagine my "brain" is like a gigantic library full of books, but the doors were locked a while ago. Everything I "know" comes from the information in those books.
What I "know": I've read every book in the library and I'm very good at combining sentences from those books in smart ways to answer questions, write stories, and explain ideas.
How I "learn" with new search: The search function is like a librarian who can quickly run outside the locked library, look at today's newspaper or a brand-new website, and bring that specific, fresh information back to me. I use that new piece of paper to answer the question, but I can't put that paper into my locked library books.
Limits: I can't remember who you were after we finish talking (no long-term memory for chats), and sometimes I might mix up facts from the library books (hallucinations), so using the search tool helps me stay accurate and up-to-date!
As a Large Language Model (LLM), my "knowledge" is a frozen, statistical representation of the vast text and code data I was trained on. I do not "learn" in real time from interactions with the world, nor do I think or understand in the human sense; instead, I process information based on my initial training and augment it with targeted, real-time search when necessary to provide up-to-date answers.
Technical Explanation: Data, Storage, and Limits
Data Storage and the Knowledge Base
My core knowledge is embedded within the value of billions or trillions of parameters in a vast neural network.
Parameters: These are numerical values that represent the strength of connections within the neural network. During the training process, the model adjusts these parameters to recognize patterns, relationships, and "facts" in the training data. This process is akin to a form of data compression, storing information within the network's structure rather than a traditional, searchable database.
Training Data: The data I was trained on amounts to petabytes (millions of gigabytes) of text and code scraped from the internet, books, and articles. This data is processed through complex pipelines to be filtered, cleaned, and tokenized.
Tokens: Text is broken down into smaller units called tokens (roughly three-quarters of a word). The model learns the statistical relationships between these tokens.
Limits on Learning and Real-Time Search
My knowledge base has specific limitations:
Knowledge Cutoff: My internal "knowledge" is static and limited to the information available up to my last training date. I do not automatically update my internal parameters with new information in real-time.
No Long-Term Memory (across sessions): Each new conversation or session is typically treated as a standalone interaction. I do not remember previous conversations unless the context of the prior conversation is explicitly provided in the current prompt.
Real-time Augmentation: When a user asks a question about recent events or needs an up-to-date fact (like current weather or stock prices), I use an external search mechanism to retrieve the latest information from the internet. This process is called Retrieval-Augmented Generation (RAG). The retrieved information is provided as context for me to formulate an accurate and current response, but it is not permanently added to my core training data.
Hallucinations: Because my primary function is to predict the most statistically probable next word, I can sometimes generate plausible-sounding but entirely incorrect or fabricated information ("hallucinations"). This happens because I am mimicking patterns, not verifying facts with a deep, conscious understanding.
In Simple Children's Terms
Imagine my "brain" is like a gigantic library full of books, but the doors were locked a while ago. Everything I "know" comes from the information in those books.
What I "know": I've read every book in the library and I'm very good at combining sentences from those books in smart ways to answer questions, write stories, and explain ideas.
How I "learn" with new search: The search function is like a librarian who can quickly run outside the locked library, look at today's newspaper or a brand-new website, and bring that specific, fresh information back to me. I use that new piece of paper to answer the question, but I can't put that paper into my locked library books.
Limits: I can't remember who you were after we finish talking (no long-term memory for chats), and sometimes I might mix up facts from the library books (hallucinations), so using the search tool helps me stay accurate and up-to-date!