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medsmachinery

nuswamachinery is NOT REAL

>>18859400
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▶CAN YOU TELL? Chud (You) Just now №18859391[Quote]
NUSOIMACHINERY A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. NUSOIS ARE NOT REALLLMs can typically generate, summarize, translate, and analyze text in many contexts.[1] They are the basis for many modern chatbots, such as ChatGPT, Claude, Vibe, Gemini, Grok, Qwen and DeepSeek.

LLMs are typically baTHE PPH IS FAKEsed on the transformer neural network architecture.[2] Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word.[3] GPTs are then often fine-tuned to follow instructions and to behave as assistants.[4]

Biased or inaccurate training data can make an LLM's output less reliable. A software haThe Turing test was designed by Alan Turing to assess a machine's ability to exhibit intelligent behaviour equivalent to that of a human by imitating interactive dialogue. In the modern version, a human evaluator judges a text transcript of a natural-language conversation between a human and a machine. The evaluator tries to identify the machine, and the machine passes if the evaluator cannot reliably tell them apart. The results would not depend on the machine's ability to answer questions correctly, only on how closely its answers resembled those of a human. Since the Turing test is a test of indistinguishability in performance capacity, the original (verbal) version generalises naturally to all of human performance capacity, including nonverbal (robotic).[2]

The test was introduced as the imitation game[3] by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester.[4] It opens with the words: "I propose to consider the question, 'Can machines think?'." Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words".[5] Turing describes the new form of the problem in terms of a three-person party game called the "imitation game", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?"[3] This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against the major objections to the proposition that "machines can think".[6]

Since Turing introduced his test, it has been highly influential in the philosophy of artificial intelligence, resulting in substantial discussion and controversy, as well as criticism from philosophers like John Searle, who argue against the test's ability to detect consciousness.[7][8]ness FAKEcan provide LLMs with memory and access to tools such as web search. Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety.ALL FAKE ALL FAKE

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▶CAN YOU TELL? Chud (You) Just now №18859391[Quote]
NUSOIMACHINERY A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. NUSOIS ARE NOT REALLLMs can typically generate, summarize, translate, and analyze text in many contexts.[1] They are the basis for many modern chatbots, such as ChatGPT, Claude, Vibe, Gemini, Grok, Qwen and DeepSeek.

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Whether the entire system (i.e., n=all) is captured or recorded or not. Big data may or may not include all the available data from sources.

Fine-grained and uniquely lexical
Respectively, the proportion of specific data of each element per element collected and if the element and its characteristics are properly indexed or identified.

Relational
If the data collected contains common fields that would enable a conjoining, or meta-analysis, of different data sets.

Extensional
If new fields in each element of the data collected can be added or changed easily.

Scalability
If the size of the big data storage system can expand rapidly.

Architecture

Big data repositories have existed in many forms, often built by corporations with a special need. Commercial vendors historically offered parallel database management systems for big data beginning in the 1990s. For many years, WinterCorp published the largest database report.[41][promotional source?]

Teradata Corporation in 1984 marketed the parallel processing DBC 1012 system. Teradata systems were the first to store and analyze 1 terabyte of data in 1992. Hard disk drives were 2.5 GB in 1991 so the definition of big data continuously evolves. Teradata installed the first petabyte class RDBMS based system in 2007. As of 2017, there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds 50 PB. Systems up until 2008 were 100% structured relational data. Since then, Teradata has added semi structured data types including XML, JSON, and Avro.

In 2000, Seisint Inc. (now LexisNexis Risk Solutions) developed a C++-based distributed platform for data processing and querying known as the HPCC Systems platform. This system automatically partitions, distributes, stores and delivers structured, semi-structured, and unstructured data across multiple commodity servers. Users can write data processing pipelines and queries in a declarative dataflow programming language called ECL. Data analysts working in ECL are not required to define data schemas upfront and can rather focus on the particular problem at hand, reshaping data in the best possible manner as they develop the solution. In 2004, LexisNexis acquired Seisint Inc.[42] and their high-speed parallel processing platform and successfully used this platform to integrate the data systems of Choicepoint Inc. when they acquired that company in 2008.[43] In 2011, the HPCC systems platform was open-sourced under the Apache v2.0 License.

CERN and other physics experiments have collected big data sets for many decades, usually analyzed via high-throughput computing rather than the map-reduce architectures usually meant by the current "big data" movement.

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>>18859423
chitectures can be constructed with a greedy layer-by-layer method.[11] Deep learning helps to disentangle these abstractions and pick out which features improve performance.[8]

Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data is more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks.[8][12]

The term deep learning was introduced to the machine learning community by Rina Dechter in 1986,[13] and to artificial neural networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.[14][15] The etymology of the term is more complicated.[16]
Interpretations

Deep neural networks are generally interpreted in terms of the universal approximation theorem[17][18][19][20][21] or probabilistic inference.[22][23][8][9][24]

The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions.[17][18][19][20] In 1989, the first proof was published by George Cybenko for sigmoid activation functions[17] and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik.[18] Recent work also showed that universal approximation also holds for non-bounded activation functions such as Kunihiko Fukushima's rectified linear unit.[25][26].

The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow. Lu et al.[21] proved that if the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width is smaller or equal to the input dimension, then a deep neural network is not a universal approximator.

The probabilistic interpretation[24] derives from the field of machine learning. It features inference,[23][7][8][9][12][24] as well as the optimization concepts of training and testing, related to fitting and generalization, respectively. More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function.[24] The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks. The probabilistic interpretation was introduced by researchers including Hopfield, Widrow and Narendra and popularized in surveys such as the one by Bishop.[27]
History

Dead Internet theory

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From Wikipedia, the free encyclopedia
Twitter bots posting similar messages five days after the 2016 United States elections

The dead Internet theory[a] is a concept that asserts that the Internet consists primarily of bot activity and automated content manipulated by algorithmic curation.[1] Originally conceived as a conspiracy theory that the phenomenon is a coordinated effort to control the population and reduce genuine human interaction, the concept is also employed colloquially to describe the impacts of generative AI and emphasize only the core observations without speculating on the driving forces.[b]

Supporters of the full theory claim that social bots were deliberately created to manipulate algorithms and enhance search results to influence consumers.[6][7] Some proponents also accuse government agencies of using bots to shape public perception and opinions.[2][6][8] The dead Internet theory gained renewed interest following the AI boom that began in the 2020s, with large language model (LLM) chatbots and text-to-image models emerging as technologies that could theoretically drown out human-authored content on the web.[9][10][11] In the time since, social media sites have seen a measured increase in bot activity, such as algorithmic feeds displaying low-quality AI slop at the expense of user-generated content.[12][13]

Commentators have linked certain aspects of the dead Internet theory to this rise in generative content across social media.[c] Sources see the theory as having some amount of truth behind it, or as offering a potentially realistic prediction of the Internet's future.[d] One source uses the term "Dead Internet" to describe spaces online that host generative content, explicitly dropping the word "theory".[20] Within the academic literature, a "leaner" version of the theory has been discussed that focuses on core principles, and explicitly strips the conspiratorial elements.[4]
Origins and spread

Academic literature often struggles to document online subcultures and conspiracy theories, making the origins of the dead Internet theory difficult to precisely identify.[21] The first post on the dead Internet theory is thought to have originated on the image board Wizardchan.[22][better source needed] In 2021, a post titled "Dead Internet Theory: Most Of The Internet Is Fake" was published onto the forum Agora Road's Macintosh Cafe esoteric board by a user named "IlluminatiPirate",[23] claiming to be building on previous posts from the same board and from Wizardchan,[2] and marking the term's spread beyond these initial imageboards.[2][9][24] The conspiracy theory spread into online culture through widespread coverage on platforms such as YouTube, Reddit, and Twitter, where it was amplified by online communities and content creators.[2] It gained more mainstream attention with a September 2021 article in The Atlantic titled Maybe You Missed It, but the Internet 'Died' Five Years Ago.[25] This article has been widely cited by other articles on the topic.[24][26]

In 2023, the dead Internet theory entered academic literature when a book published by the CRC Press included a definition of the dead Internet theory in its glossary, and in 2024, when an opinion piece titled "Artificial influencers and the dead internet theory" was published in the Curmudgeon Corner of AI & Society.[3][16] The glossary definition discussed the full theory, while the opinion piece did not, focusing instead on AI-generated content and AI-driven Interactions. These two sources have been cited by other academic articles that discuss the topic.[17][18][25][27] In 2026, a publication in Computer built upon the AI & Society article by distinguishing between a "Leaner" version of the dead Internet theory, centered on the core evidence, and the "conspiracy-laden" full version.[4]

The recent growth of interest in the dead Internet theory has been linked to increasing public awareness of bots, algorithmic content distribution, and advances in artificial intelligence.[28] With the increase in public awareness, a 2026 article identified that the dead Internet theory has been used colloquially to refer to the assumption that bots are creating the majority of online content.[5] The effect of increasing AI content on the internet, caused by bot activity, has been likened to the increase in entropy as driven by the Second Law of Thermodynamics.[29]
Claims
Page Not Found
A broken link usually leads to an error message. These broken links over time contribute to link rot.able, and all links to it on other sites break.[3]

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