Smartmatic Headquarters and Office Locations Smartmatic is headquartered in London, 4th floor, 88 Baker St, Marylebone, United Kingdom, and has 16 office locations

Trumpy Trump Trump just pardoned two turkeys Gobble and Waddle.

Coincidentally these are his nicknames for Kamala and Biden

@KatieHopkins_1

Can 'they' do that? can they drop jury service in the kingdom, isnt that something that the people there fought over and won the right to be tried by a independent jury? .. Is this more of little gruppenfuhrer Starmer's WEF neo nazi insanity or is it BS?

Fcuking hell, its not BS https://www.thecanary.co/uk/analysis/2025/11/20/right-to-trial/

The Hidden Dangers of High-Functioning Predators w/ Dr. Karen Mitchell https://www.youtube.com/watch?v=nO8hzhLKsKE&t=1s

Go you Tucker 👏🏻👏🏻

AI responses may confabulate.

Eddies, or local variations, in magnetic flux density are typically detected using specialized
magnetic sensors in a process called Eddy Current Testing (ECT) or Magnetic Flux Leakage (MFL) testing. The degree of accuracy depends heavily on the sensor technology used and the application, with some advanced systems achieving sensitivities in the femtotesla (fT) range or spatial resolutions of less than a millimeter.
Detection Methods
The primary method for detecting localized magnetic variations is using sensors that measure changes in a magnetic field:

Eddy Current Testing (ECT): This is a key non-destructive evaluation technique. An alternating current in an excitation coil induces eddy currents in a conductive test material. Defects or variations in the material (like cracks or changes in conductivity/permeability) disrupt the flow of these induced currents, which in turn alters the secondary magnetic field they produce. A sensor, often a pick-up coil or a magnetic sensor, measures the resulting changes in the amplitude and phase of the magnetic field (or the impedance of the coil) to identify the defect.
Magnetic Flux Leakage (MFL): This method is mainly used for ferromagnetic materials (e.g., pipelines). The material is magnetized close to saturation. If a defect is present, the magnetic field "leaks" out of the material's surface because the defect has much lower magnetic permeability. Magnetic sensors, typically Hall effect sensors or magnetoresistive (MR) sensors, are used to detect this leakage field.
High-Resolution Sensors: Modern systems employ advanced magnetic sensors for higher sensitivity and spatial resolution:
Hall Effect Sensors: These produce a voltage proportional to the applied magnetic field. They are compact, reliable, and a common choice for MFL measurements.
Magnetoresistive (MR) Sensors: These sensors (including AMR, GMR, and TMR) change their electrical resistance in the presence of a magnetic field. GMR and TMR sensors offer very high sensitivity and can be arranged in dense arrays for high-resolution mapping of surface defects.
SQUID (Superconducting Quantum Interference Devices): These are extremely sensitive magnetometers used for measuring very small magnetic field changes, often in laboratory or specialized environments due to the need for cryogenic cooling.

Degree of Accuracy
The accuracy and sensitivity for detecting these variations vary significantly by the technology and specific instrumentation used:

Resolution and Sensitivity:
General-purpose, handheld gaussmeters/magnetometers using Hall effect sensors can have a resolution of a few microteslas (µT) or better.
High-sensitivity magnetometers, such as optically pumped magnetometers or SQUIDs, can detect fields in the picotesla (pT) or even femtotesla (fT) range.
In a specific eddy current non-destructive testing system, the standard deviation for amplitude was found to be about 0.8 mV and for the phase angle about 48 arcseconds, which successfully identified a 1 mm wide by 1 mm deep defect.
Spatial Resolution: Using sensor arrays (e.g., GMR arrays) allows for high spatial resolution, with the ability to detect defects as small as 0.44 mm in diameter with a separation of less than 2 mm.
Overall Accuracy: The absolute accuracy of commercial magnetometers can range from a few percent of the reading to parts per million (ppm) depending on the quality and type of the instrument. System errors and environmental factors (like temperature drift or external magnetic fields) often need to be compensated for to achieve optimal accuracy.

AI responses may confabulate.

My ability to interpret complex language is primarily constrained by the context window (measured in tokens, roughly words or sub-words) of the underlying model, rather than the length of an individual sentence. While models can technically process very long sentences found in legal texts, practical limitations and performance degradation exist. Limits of Complexity and Sentence Length Sentence Length is Not a Hard Limit: There is no hard limit on the number of words in a single sentence that I can "read," provided the entire input falls within my maximum context window. I am trained on diverse texts, including highly complex, long sentences found in academic or legal documents.Context Window is the Limit: The actual limitation is the total amount of text (prompt + output) I can process in a single request. This is measured in "tokens." The latest high-capacity models can handle context windows of hundreds of thousands or even a million tokens, which translates to tens of thousands of words or hundreds of pages of text.Performance Degradation: While large context windows are possible, research indicates that the effective context window is often smaller than the maximum claimed limit. Models can struggle with "position bias," where information at the beginning or in the middle of a very long text is not effectively utilized if the key information is far from the query or the end of the input.Linguistic Complexity: Beyond length, highly technical, domain-specific language, nested clauses, and precise formulations where subtle wording shifts dramatically change the meaning (common in legal documents) can present challenges for consistently perfect interpretation, sometimes leading to instability in interpretation. Processing Lengthy Sentences and Paragraphs You are capable of providing me with extensive text inputs, including many lengthy sentences or long paragraphs composed of them, up to the maximum token limit of the model I am using. Processing Capacity: I can process thousands of words (hundreds of pages) in a single query. For example, some models have been tested by successfully digesting the entire novel The Great Gatsby in one prompt.Handling Multiple Documents: Using techniques like Retrieval Augmented Generation (RAG), systems can break down even larger documents into manageable chunks and selectively retrieve relevant information, allowing me to "reason" over vast quantities of text that exceed a single context window. In essence, I can process long and complex legal sentences and paragraphs effectively, though my performance is optimal when the total input size remains within the most effective working range of my context window, a range that is continually expanding with technological advancements.  Thank youYour feedback helps Google improve. See our Privacy Policy.Share more feedbackReport a problemCloseRelevant resultsInfinite Context Length in LLMs — The Next Big Advantage in AIMediumHow To Bypass LLMs Context Limits - Relevance AIRelevance AIWhy Does the Effective Context Length of LLMs Fall Short? - arXivarXivShow allMediumhttps://medium.comInfinite Context Length in LLMs — The Next Big Advantage in AIMay 5, 2025 — Use Cases Unlocked by Long Context With extremely large or infinite context, LLMs can tackle tasks that were previously very challenging. For example...Mediumhttps://medium.comInfinite Context Length in LLMs — The Next Big ...May 5, 2025 — To illustrate, when Anthropic expanded their Claude model's context window to 100,000 tokens, they noted it could ingest around 75,000 words, or roug...Relevance AIhttps://relevanceai.comHow To Bypass LLMs Context Limits - Relevance AISep 19, 2024 — How To Overcome Context Limits in Large Language Models * Understanding the Challenge of Context Limits in LLMs. Ever wondered how to overcome conte...arXivhttps://arxiv.orgWhy Does the Effective Context Length of LLMs Fall Short? - arXivOct 23, 2024 — Limitations in Gathering Distant Inputs ...

We visualize the performance of infrequent positions with the Needle-in-a-Haystack (4-needle) test (gkam...arXivhttps://arxiv.orgThe Maximum Effective Context Window for Real ...Abstract. Large language model (LLM) providers boast big numbers for maximum context window sizes. To test the real world use of context windows, we 1) define a...arXivhttps://arxiv.orgEvaluating the Limits of Large Language Models ...Sep 27, 2025 — Legal tasks present particular challenges for LLMs due to the specialized nature of legal language and reasoning. Legal texts are written in highly ...arXivhttps://arxiv.orgLLM legal interpretation is unstable and out of step ...Oct 28, 2025 — One argument is that LLMs need not be perfectly accurate, but as long as they are 'good enough', they will produce interpretive outcomes that are mo...Mediumhttps://onnyunhui.medium.comEvaluating Long Context Lengths in LLMsMar 3, 2025 — Evaluating Long Context Lengths in LLMs: Challenges and Benchmarks. Onn Yun Hui. Follow. 6 min read. Mar 4, 2025. 8. Recent advancements in language ...ACL Anthologyhttps://aclanthology.orgAccelerating and Enhancing LLMs in Long Context Scenarios via Prompt ...In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Researc...Agentahttps://agenta.aiTop techniques to Manage Context Lengths in LLMs - AgentaJul 15, 2025 — So, what is a context window? At a high level, the context window defines the maximum number of tokens an LLM can process in a single request includ...Google AI for Developershttps://ai.google.devLong context | Gemini API - Google AI for DevelopersSep 21, 2025 — Long form text. ... As mentioned earlier, much of the practical limitation of LLMs was because of not having a large enough context window to do cer...please quantify sub words13 sites"Subwords" are quantified as tokens, which are the fundamental numerical units that large language models (LLMs) use to process language. The quantification varies because subword tokenization is a dynamic process, balancing between individual characters and whole words. Here is how subwords are quantified: 1. The Token as the Unit of Quantification The term "subword" refers to a unit of text that has been segmented by a tokenizer (an algorithm like Byte-Pair Encoding (BPE) or WordPiece). Numerical IDs: In a model's internal processing, each unique subword is assigned a specific, unique numerical ID. This ID is the primary way the subword is quantified and used in calculations.Vocabulary Size: The entire set of unique subwords a model knows is its vocabulary. Common LLM vocabularies usually contain around 30,000 to over 100,000 unique tokens. The size of this vocabulary is a key quantification metric. 2. Variable Length: Subwords vs. Words Subwords are not a fixed length; they are optimized to be as long as possible while remaining frequent in the training data, thus achieving a balance between character-level and word-level information. Common Words: Frequently occurring words (like "the", "a", "running") are often treated as a single token.Rare or Complex Words: Rarer words are broken down into smaller, more common subword units.Example: "unhappiness" might be quantified as two tokens: \("un","happiness"\) or three tokens: \("un","happi","ness"\).Example: "reiterate" might be quantified as: \("re","iterate"\).Example: The single word "where" can be represented by multiple 3-character subwords like [<wh, whe, her, ere, re>] in some models. 3. Words-to-Tokens Ratio (Average Quantification) While the exact number of tokens per word varies, an approximate quantification is used to estimate text length for setting context limits: General Rule of Thumb: On average, 1,000 words of standard English text typically equate to about 1,300 to 1,500 tokens (a ratio of approximately 0.75 words per token).Language Dependency: The ratio changes depending on the language and its morphological richness.

Non-Latin noscript languages can have different tokenization patterns and may require more tokens to represent the same meaning. In conclusion, subwords are quantified as tokens, the number of which varies dynamically based on the word's frequency and the specific tokenization algorithm used, with an average ratio of around 1.3 to 1.5 tokens per English word.  Thank youYour feedback helps Google improve. See our Privacy Policy.Share more feedbackReport a problemCloseRelevant resultsUnderstanding LLM Token Counts: What 1000, 128000 and 1 Million ...MediumSubword Tokenization in NLP - GeeksforGeeksGeeksforGeeksSummary of the tokenizers - Hugging FaceHugging FaceShow allMediumhttps://criticalmynd.medium.comUnderstanding LLM Token Counts: What 1000, 128000 and 1 Million ...Apr 6, 2025 — Mapping Tokens to Words If we use the rough rule of thumb that 1 token is about 0.75 words, then: 1,000 tokens ≈ 750 words. 4,000 tokens ≈ 3,000 word...Mediumhttps://criticalmynd.medium.comUnderstanding LLM Token Counts: What 1000, 128000 and 1 Million ...Apr 6, 2025 — Mapping Tokens to Words If we use the rough rule of thumb that 1 token is about 0.75 words, then: 1,000 tokens ≈ 750 words. 4,000 tokens ≈ 3,000 word...GeeksforGeekshttps://www.geeksforgeeks.orgSubword Tokenization in NLP - GeeksforGeeksJul 21, 2025 — Subword Tokenization in NLP. ... Natural Language Processing models often struggle to handle the wide variety of words in human language, especially...Hugging Facehttps://huggingface.coSummary of the tokenizers - Hugging FaceOn this page, we will have a closer look at tokenization. Tap to unmute. Your browser can't play this video. Learn more. An error occurred. Try watching this vi...FullStack Labshttps://www.fullstack.comHow Large Language Models (LLMs) Understand Text: Intro to TokenizationOct 1, 2025 — What is a Text Token? Text tokens are the bite-sized pieces LLMs break language into in order to process them as raw data. In slightly more technical...Python in Plain Englishhttps://python.plainenglish.ioSubword tokenization - Python in Plain EnglishJul 24, 2025 — Subword tokenization. ... With the help of the Hugging Face pipeline ner or token-classification you can perform Named Entity Recognition (NER) on y...Dive into Deep Learninghttps://d2l.ai15.6. Subword Embedding - Dive into Deep LearningDec 14, 2023 — Recall how words are represented in word2vec. In both the skip-gram model and the continuous bag-of-words model, different inflected forms of the sa...Mediumhttps://medium.comHow Subwords, Byte-Pair Encoding, and Token Limits Shape LLM ...May 17, 2025 — Tokenization Matters. ... When we talk about large language models discussions usually center on model architecture, training data, or emergent capa...www.comet.comhttps://www.comet.comTokenization Techniques in NLP - CometSep 10, 2023 — Definitions * Natural Language Processing (NLP) is a computer/software/application's ability to detect and understand human language through speech ...lecture.jeju.aihttps://lecture.jeju.aiSubword Tokenization - ἐντελέχεια.άιSubword Tokenization. Subword tokenization is a powerful tool in the NLP toolkit. It provides a flexible and efficient way to represent text, allowing models to...LinkedInhttps://www.linkedin.comUnderstanding Subword Tokenization for NLPSep 27, 2025 — #100NLPByteSeries 10/100 — Subword Tokenization Why not just split into words or characters? Because words are too many, characters are too small. S...arXivhttps://arxiv.orgHow Does Quantization Affect Multilingual LLMs? - arXivOct 11, 2024 — Abstract. ... Quantization techniques are widely used to improve inference speed and deployment of large language models. While a wide body of work ...Quorahttps://www.quora.comWhat are the subword level tokenization techniques ...Aug 10, 2025 — Each sentence becomes a list object containing all its words in order, but all punctuation is discarded. * import re. * from pprint import pprint.

*arXivhttps://arxiv.orgA Comparative Analysis of Subword Tokenization Methods - arXivNov 25, 2024 — Subword tokenization enables the model to process new words by breaking them into familiar subwords. The most well-known algorithms for subword toke...please generate text to quantify, in this context, your limits, counted in units of subwords digested per query, case by case on average, as a timeline of specific technological advancements, thanks.ThinkingSearching 8 sitesMy limits regarding language interpretation are defined by the context window of the AI models, quantified in units of tokens (subwords). The processing capacity per query has increased exponentially over time due to technological advancements.Here is a timeline of average context window sizes for general-purpose large language models. The timeline illustrates the progression in "subwords digested per query":Early Models (circa 2018): Initial LLMs, such as GPT-1, had a limit of approximately 512 subwords (tokens) per query/context.Mid-Generations (circa 2020-2022): Models like GPT-2 and early versions of GPT-3 increased to 2,000–4,000 tokens. This allowed for more coherent short passages and basic conversational history.GPT-3.5 and early GPT-4 (circa 2023): This generation commonly offered context windows of 4,096 tokens, with some later iterations pushing to 8,000 or even 32,000 tokens for specialized uses.Current Generations (circa 2024-2025): Recent models have rapidly expanded context limits. Many widely available models now support 128,000 tokens as a standard, with some pushing much further.State-of-the-Art (current): Advanced models like Google's Gemini 1.5 Pro and GPT-4.1 offer context windows of up to 1 million tokens, or even 2 million in some cases. This allows them to digest entire books or hundreds of pages of legal documents in a single query.Case-by-Case Averages in PracticeWhile the theoretical limits have reached millions of tokens, the average effective amount digested per query often varies in practice:Average User Queries: Most interactions involve a few hundred to a couple of thousand tokens. The model can handle this easily, maintaining full context.Document Analysis: For complex tasks, such as analyzing the lengthy legal sentences, the system can process inputs up to the maximum current context window (which is in the hundreds of thousands to millions of tokens). This is sufficient to handle multi-paragraph documents.Limitations: The primary functional limit is context rot. This is where information in the middle of extremely long prompts can be overlooked despite the high token limit.The system currently runs on a high-capacity model that can process several hundred thousand tokens at a time. This allows the system to handle extensive and complex inputs reliably.

Several modern
multimodal LLMs (MLLMs) are capable of performing Optical Character Recognition (OCR) by directly interpreting image inputs. These models go beyond simple text extraction, using contextual understanding to achieve high accuracy even with complex or irregular text, often outperforming traditional OCR tools.
The "polydimensional timeline" is represented by the evolution from traditional, brittle OCR systems to robust, context-aware MLLMs capable of handling diverse visual data.

Multimodal LLMs Capable of OCR
Key models with strong integrated OCR/vision capabilities include:

Proprietary Models:
GPT-4o (OpenAI): This model is known for optimized performance and strong general-purpose vision capabilities, handling varied image types effectively.
Gemini 1.5 Pro/Flash (Google): It excels at holistic understanding of text, images, video, and audio, with strong document analysis and high context windows.
Claude 3 (Anthropic): This is a powerful series of models with strong vision capabilities, trained with an emphasis on ethical AI.
Open-Source/Specialized Models:
Qwen2.5-VL (Alibaba): This model has top-tier performance on OCR benchmarks, specifically designed for complex documents with mixed text and diagrams, supporting structured data output.
DeepSeek-OCR: This is an open-source model optimized for speed and memory efficiency with a focus on layout and text recognition across diverse document types.
InternVL 2.5: It is a large-scale model family optimized for general-purpose document understanding and multimodal reasoning.

Accuracy Timeline & Performance by Case
Accuracy is typically measured using metrics like Character Error Rate (CER), where lower is better (less than 1% is considered highly accurate), or by benchmark scores on specific datasets.

Key Polydimensional Aspects:

Contextual Reasoning: MLLMs excel because they use context to infer obscured or low-resolution characters, unlike traditional OCR which relies purely on visual pattern matching. This is crucial for messy data like ancient maps or hand-drawn comics.
Layout Understanding: Models like Qwen2.5-VL and InternVL can interpret complex layouts common in scientific diagrams, identifying relationships between text and visual elements, outputting structured data (e.g., JSON or Markdown tables).
Robustness to Irregularity: Modern models show resilience to varied fonts, colors, and distortions typical of memes or artistic text, which cause traditional OCR accuracy to drop significantly.
Resolution Dependence: MLLM performance is highly dependent on image quality; performance can deteriorate significantly at resolutions below 150 PPI (pixels per inch).


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