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Distributed Representations of Words and Phrases

 

Tomas Mikolov, et. al
Google Inc. Mountain View

Abstract.  The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling.

An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of “Canada” and “Air” cannot be easily combined to obtain “Air Canada”. Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

 

Large Language Models and Infrastructure Technical Standards

Large Language Models (LLMs) are poised to significantly accelerate and reshape the development of infrastructure standards — including engineering codes, technical specifications for civil works, transportation, energy grids, water systems, and related Standards Development Organization (SDO) processes at ASTM, IEEE, ASABE, ISO, and similar bodies.  This connection traces back to foundational ideas in distributed representations (Hinton et al., Mikolov’s Word2Vec) that powered the transformer revolution, which in turn enabled modern LLMs and the shift from passive generative AI to active, goal-directed agentic AI.

While LLMs will not replace human expertise, consensus-building, or rigorous validation, they will transform traditionally slow, document-heavy workflows into faster, more collaborative, and data-driven processes.

1. Faster Drafting, Summarization, and Gap Analysis

LLMs can rapidly summarize lengthy documents, extract key requirements, identify inconsistencies across related standards, and generate initial draft sections or comparison tables. This is especially valuable for reviewing historical codes, research papers, regulations, and stakeholder inputs.

Infrastructure example: In renewable energy permitting or grid interconnection standards, LLMs excel at processing complex environmental impact statements and regulatory texts to accelerate reviews.

2. Enhanced Requirements Engineering and Consistency Checking

LLMs support formal requirements extraction, flag ambiguities, suggest measurable criteria, and translate between domains. They help maintain alignment between textual standards and digital implementations such as Building Information Modeling (BIM) or simulation tools.

3. Improved Accessibility, Education, and Stakeholder Participation

LLMs make standards more usable by generating plain-language explanations, FAQs, examples, and tailored training materials. They lower barriers for broader participation in SDO committees by helping non-experts understand and contribute to drafts.

4. Domain-Specific Applications in Infrastructure

  • Civil, Structural & Agricultural Engineering: Design ideation, safety analysis, and updating standards for new materials and climate resilience.
  • Permitting & Compliance: Summarizing environmental documents and speeding up infrastructure deployment.
  • Interoperability & Testing: Verification support for software-heavy systems such as smart grids and autonomous infrastructure.

5. Broader Process Changes for SDOs

  • Zero-draft acceleration for preliminary stakeholder review
  • Continuous monitoring for maintenance and timely updates
  • Multi-agent LLM systems for parallel virtual expert review before human consensus

Limitations and Important Caveats

  • “Hallucinations” & Validation: Outputs must always be human-verified, especially in safety-critical areas. Domain-specific fine-tuning and retrieval-augmented generation (RAG) help but are not foolproof.
  • Bias, Copyright & Accountability: Standards demand traceability and consensus; LLMs can introduce subtle biases or IP concerns.
  • Not a Full Replacement: Human judgment remains essential for risk assessment, ethics, and real-world tradeoffs.

Expect 2–5× faster iteration on drafts, superior knowledge management, and more adaptive standards. Early adopters using LLM assisted tools with proper governance will lead the next generation of infrastructure standards development.

Hegemon Fairfield County Connecticut

Hubbell Corporation, a leader in electrical and utility solutions, significantly contributes to data center build-outs by providing end-to-end infrastructure products. These include reliable connectivity, structured cabling, wiring devices, enclosures, and modular prefabricated systems for high-density server rooms and power distribution. Through brands like PCX and Hubbell Premise Wiring, it ensures scalability, maximum uptime, and regulatory compliance, backed by a 25-year guarantee. Amid AI-driven demands, Hubbell’s vertically integrated approach supports efficient grid-to-chip power management, enabling faster, resilient expansions for colocation and enterprise facilities.

 

 

Cornbread & Grandma’s Chicken Soup

Standards Nebraska | Statement of Net Position: $5.191B (Page 26)

WRITTEN BY Kalani Simpson PUBLISHED May 25, 2021

 

Ingredients:

  • 1 5- to 6-pound stewing hen or baking chicken
  • 1 package of chicken wings
  • 3 large onions
  • 1 large sweet potato
  • 3 parsnips
  • 2 turnips
  • 11 to 12 large carrots
  • 5 to 6 celery stems
  • 1 bunch of parsley
  • Salt and pepper to taste

Directions:

  1. Clean the chicken, put it in a large pot and cover it with cold water. Bring the water to boil.
  2. Add the chicken wings, onions, sweet potato, parsnips, turnips and carrots. Boil about 1 and a half hours. Remove fat from the surface as it accumulates.
  3. Add the parsley and celery. Cook the mixture about 45 min. longer.
  4. Remove the chicken. The chicken is not used further for the soup. (The meat makes excellent chicken parmesan.)
  5. Put the vegetables in a food processor until they are chopped fine or pass through a strainer. Both were performed in the present study.
  6. Add salt and pepper to taste.

(Note: This soup freezes well.)  Matzo balls were prepared according to the recipe on the back of the box of matzo meal (Manischewitz).

PRINT Recipe

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Cornbread & Coffee

Hot Dog University

The iconic American “street food” traces its origin to 19th century German immigrants who brought frankfurters from their homeland.

In the 1860s, the term “hot dog” emerged in reference to these sausages being sold in buns at street carts. The popularity of hot dogs soared during the late 19th and early 20th centuries particularly at baseball games where the hot dog is virtually synonymous.at the sport.

In many college towns push cart hot dog vendors may be welcomed and even embraced as part of the local food scene. They can add variety and convenience for students, faculty, and staff by offering affordable and quick meal options. These towns may have regulations and policies in place to support and accommodate such vendors.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Syllabus

The case against hot dogs as a food primarily revolves around health concerns and potential risks associated with their consumption. Some of the key arguments include:

Processed meat and additives: Hot dogs are often made from processed meats that can contain additives, preservatives, and high levels of sodium. These additives, such as nitrates and nitrites, have been linked to increased risks of certain health issues, including cancer and heart disease.

High in unhealthy fats: Hot dogs are typically high in saturated and trans fats, which can contribute to elevated cholesterol levels and increase the risk of cardiovascular diseases.

Potential for contamination: There have been instances of foodborne illnesses associated with hot dogs, such as outbreaks of bacterial contamination, including E. coli or Listeria monocytogenes. Improper handling, storage, or undercooking can increase the risk of such contamination.

Allergens and dietary restrictions: Hot dogs often contain common allergens like wheat, soy, and dairy. Additionally, they may not be suitable for individuals with dietary restrictions or preferences, such as vegetarians, vegans, or those following specific religious or cultural dietary guidelines.

Environmental impact: The production and consumption of hot dogs contribute to environmental concerns. The meat industry, including processed meat production, is associated with greenhouse gas emissions, land degradation, and water pollution.

These arguments against hot dogs do not necessarily apply to all hot dogs or to every individual. Moderation, choosing healthier options, and considering individual dietary needs and preferences can help mitigate some of the concerns associated with hot dog consumption.

Paul Mitchell The School | Tinley Park

Relevant codes, standards and regulations:

Food Safety and Inspection Service: Federal Meat Inspection Act

U.S. Department of Agriculture: Hot Dogs and Food Safety

Codex Alimentarius

Nourriture d’automne

Large Language Model Standards

Perhaps the World Ends Here | Joy Harjo

 

The world begins at a kitchen table. No matter what, we must eat to live.
The gifts of earth are brought and prepared, set on the table.
So it has been since creation, and it will go on.
We chase chickens or dogs away from it. Babies teethe at the corners. They scrape their knees under it.
It is here that children are given instructions on what it means to be human.
We make men at it, we make women.
At this table we gossip, recall enemies and the ghosts of lovers.
Our dreams drink coffee with us as they put their arms around our children.
They laugh with us at our poor falling-down selves and as we put ourselves back together once again at the table.
This table has been a house in the rain, an umbrella in the sun.
Wars have begun and ended at this table. It is a place to hide in the shadow of terror.
A place to celebrate the terrible victory.
We have given birth on this table, and have prepared our parents for burial here.
At this table we sing with joy, with sorrow. We pray of suffering and remorse. We give thanks.
Perhaps the world will end at the kitchen table, while we are laughing and crying, eating of the last sweet bite.

 

Standards and benchmarks for evaluating large language models (LLMs). Some of the most commonly used benchmarks and standards include:

  1. GLUE (General Language Understanding Evaluation): GLUE is a benchmark designed to evaluate and analyze the performance of models across a diverse range of natural language understanding tasks, such as text classification, sentiment analysis, and question answering.
  2. SuperGLUE: SuperGLUE is an extension of the GLUE benchmark, featuring more difficult language understanding tasks, aiming to provide a more challenging evaluation for models.
  3. CoNLL (Conference on Computational Natural Language Learning): CoNLL has historically hosted shared tasks, including tasks related to coreference resolution, dependency parsing, and other syntactic and semantic tasks.
  4. SQuAD (Stanford Question Answering Dataset): SQuAD is a benchmark dataset for evaluating the performance of question answering systems. It consists of questions posed on a set of Wikipedia articles, where the model is tasked with providing answers based on the provided context.
  5. RACE (Reading Comprehension from Examinations): RACE is a dataset designed to evaluate reading comprehension models. It consists of English exam-style reading comprehension passages and accompanying multiple-choice questions.
  6. WMT (Workshop on Machine Translation): The WMT shared tasks focus on machine translation, providing benchmarks and evaluation metrics for assessing the quality of machine translation systems across different languages.
  7. BLEU (Bilingual Evaluation Understudy): BLEU is a metric used to evaluate the quality of machine-translated text relative to human-translated reference texts. It compares n-gram overlap between the generated translation and the reference translations.
  8. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE is a set of metrics used for evaluating automatic summarization and machine translation. It measures the overlap between generated summaries or translations and reference summaries or translations.

These benchmarks and standards play a crucial role in assessing the performance and progress of large language models, helping researchers and developers understand their strengths, weaknesses, and areas for improvement.

Yann Lecun & Lex Fridman: Limits of LLMs

New topic for us; time only to cover the basics.  We have followed language, generally, however — every month — because best practice discovery and promulgation in conceiving, designing, building, occupying and maintaining the architectural character of education settlements depends upon a common vocabulary.  The struggle to agree upon vocabulary presents an outsized challenge to the work we do.

Large language models hold significant potential for the building construction industry by streamlining various processes. They can analyze vast amounts of data to aid in architectural design, structural analysis, and project management. These models can generate detailed plans, suggest optimized construction techniques, and assist in cost estimation. Moreover, they facilitate better communication among stakeholders by providing natural language interfaces for discussing complex concepts. By harnessing the power of large language models, the construction industry can enhance efficiency, reduce errors, and ultimately deliver better-designed and more cost-effective buildings.

Join us today at the usual hour.  Use the login credentials at the upper right of our home page.

Related:

print(“Python”)

Standards January: Language

Standard for Large Language Model Agent Interface

 

Speech Day

Speech Day generally refers to an annual event held at schools in the United Kingdom, particularly private or independent schools, where students showcase their achievements and receive prizes or awards. The exact date of “Speech Day” varies by school and is typically determined by the school’s academic calendar. It is usually held towards the end of the academic year, either in the summer term or in the early autumn term, before students break for the summer holidays.

Westonbirt School

Gallery: Graduation Commencement Speeches

“It is at leaving the college and entering the world that the education of youth begins…

It is less uniform than that of childhood but more dependent on chance, and doubtless more important.

The youth is then attacked by a greater number of sensations: all that surrounds him strikes him,

and strikes him forcibly.”

—  Claude-Adrien Helvétius (A Treatise on Man)

 

Constructor University (formerly, Jacobs University Bremen Germany) Graduation Band: “Freebird”

Intercollegiate Studies Institute | What Makes the West Strong (Sir Roger Scruton)

Jerry Seinfeld @ Duke University 2026

 

“It’s hard to think without a future.” | C.P. Snow (The Masters, 1951)

Cucumber, Tomato, & Feta Salad

https://www.scu.edu/sustainability/forgegarden/resources/recipes/forge-crafted-recipes/cucumber-tomato–feta-salad.html

 

Summer Salads

Standards Wisconsin  | Wisconsin Building Codes

https://wenglab.horticulture.wisc.edu/

Douglas County: Recipes and Canning Information

Wisconsin

Readings:

Coop: A Family, a Farm, and the Pursuit of One Good Egg (Michael Perry)

The Land Remembers (Ben Logan) 

State of Wisconsin: Department of Agriculture, Trade and Consumer Protection

 

EA Café

University of Detroit Net Position $367,257 (000) | Strategic Plan 2025-2029

Entrepreneurship Association

Four students stand around the EA Cafe coffee cart inside of the Engineering Building.

Archdiocese of Detroit

We want our young people to learn how to make a living in challenges of their time. The EA Café is a student-run coffee cart operated by the Entrepreneurship Association on the McNichols Campus. Launched in 2023 as a hands-on class project, it has quickly become a popular spot offering coffee, flavored lattes, and other beverages to students, faculty, and staff.  The café functions as a real-world learning laboratory. EA students manage every aspect — purchasing supplies, preparing drinks, customer service, marketing, and finances. The movable cart is typically set up in high-traffic locations such as the Engineering Building and campus events. Beyond providing convenient study fuel, the EA Café represents UDM’s strong commitment to experiential learning and student entrepreneurship.

Detroit (meaning “strait”, a narrow passage of water toward Lac Érié ) was founded in 1701 by French explorer and military officer Antoine de la Mothe Cadillac. This was the first permanent European settlement in what is now Wayne County Southeast Michigan and one of the earliest above tidewater in North America.  Before the French arrived, the area was inhabited by Native American tribes (Ojibwe, Odawa, Potawatomi, and others).  Relations between these tribes and the newcomers from Europe were a mix of alliances, trade, intermarriage, and violence.*   

Detroit remained under French control until 1760 (when the British took it during the French and Indian War).  Many French families stayed even after that.  Detroit still has strong French roots — street names, family surnames, and neighborhoods like Grosse Pointe  and Ecorse trace back to those early French settlers.  European immigrants to Southeast Michigan — drawn by economic opportunities enabled by the American founding documents (personal responsibility, religious tolerance, limited government, fiscal conservatism) — came in waves for the better part of 150 years.

  1. Poland — Largest group by WWI era, especially in Hamtramck.
  2. Germany — Early dominant group (1830s–1880s peak). (Indian Village)
  3. Italy — Major wave 1890s–1910s. (Little Italy)
  4. Ireland — Significant 19th-century arrivals (Corktown).
  5. United Kingdom — Steady skilled immigration. (East English Village)
  6. Hungary — Large early 20th-century influx. (Delray)
  7. Greece — Established Greektown.
  8. Romania — One of the largest Romanian communities in the U.S. (St. George Orthodox Church)
  9. Russia — Eastern European wave. (Russian Town Detroit)russian detroit, russian speaking detroit, russian michigan

Canadians across the river (City of Windsor, and much of Southwest Ontario) trace their English origins to Yorkshire, Cumberland, Devon & Cornwall, Highland Scots and all of Ireland.  Many Dutch, driven out of New York City, traveled past Detroit and settled in Kent County Western Michigan.

* Deliberately overlooked by Revisionists in the legacy US public school system: ancestral violence among the Native American tribes themselves. Cadillac invited multiple tribes in the Southeast Michigan and Ohio region to settle near Fort Pontchartrain du Détroit for protection against Iroquois invaders from Western New York.

The Detroit Decision and “White Flight”

No photo description available.

How Detroit Lost Its Way

The Most Drastic Transformation of Any American City

Families Cannot Stand the Gorilla Grip the Democratic Party (Rashida Talib & Shri Thanedar & Debbie Dingell) Has on Southeast Michigan So They Have Fled To The Suburbs

St. Catherine of Siena Academy | Oakland County Michigan

 

 

 

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