Last night, we celebrated the graduation of the 2nd class of Leadership GCISD! This incredible group of community members got a behind the scenes look at what makes GCISD thrive, and finished their semester-long program learning more from Finance, Human Resources and Technology. pic.twitter.com/CuMt95cZBm
What a sweet way to celebrate learning! About 40 Silver Lake students were honored with medals and treated to Kona Ice for reading at least 2,100 minutes this school year as part of their campuswide reading challenge. Nice job! pic.twitter.com/kkg7sDt7y5
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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:
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.
SuperGLUE: SuperGLUE is an extension of the GLUE benchmark, featuring more difficult language understanding tasks, aiming to provide a more challenging evaluation for models.
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.
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.
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.
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.
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.
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.
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Since so much of what we do in standards setting is built upon a foundation of a shared understanding and agreement of the meaning of words (no less so than in technical standard setting) that time is well spent reflecting upon the origin of the nouns and verbs of that we use every day. Best practice cannot be discovered, much less promulgated, without its understanding secured with common language.
Title: IEEE P3119 – Standard for the Procurement of Artificial Intelligence and Automated Decision Systems
Scope: The IEEE P3119 standard establishes a uniform set of definitions and a process model for procuring Artificial Intelligence (AI) and Automated Decision Systems (ADS). It covers government procurement, in-house development, and hybrid public-private development of AI/ADS. The standard redefines traditional procurement stages—problem definition, planning, solicitation, critical evaluation (e.g., impact assessments), and contract execution—using an IEEE Ethically Aligned Design (EAD) foundation and a participatory approach to address socio-technical and responsible innovation considerations. It focuses on mitigating unique AI risks compared to traditional technologies and applies to commercial AI products and services procured through formal contracts.
Purpose: The purpose of IEEE P3119 is to help government entities, policymakers, and technologists make transparent, accountable, and responsible choices in procuring AI/ADS. It provides a framework to strengthen procurement processes, ensuring due diligence, transparency about risks, and alignment with public interest. The standard aims to minimize AI-related risks (e.g., bias, ethical concerns) while maximizing benefits, complementing existing procurement practices and shaping the market for responsible AI solutions. It supports agencies in critically evaluating AI tools, assessing vendor transparency, and integrating ethical considerations into procurement.
Developmental Timelines:
September 23, 2021: The IEEE Standards Association (SA) Standards Board approved the project and established the IEEE P3119 Working Group. The Project Authorization Request (PAR) was created to define the scope.
2021–Ongoing: Development continues, with no final publication date confirmed in available sources. As of July 18, 2024, the standard was still in progress, focusing on detailed process recommendations.
The standard is being developed as a voluntary socio-technical standard, with plans to test it against existing regulations (e.g., via regulatory sandboxes).
By Whom:
Working Group Chair: Gisele Waters, Ph.D., Director of Service Development and Operations at Design Run Group, co-founder of the AI Procurement Lab, and a human-centered design researcher focused on risk mitigation for vulnerable populations.
Working Group Vice Chair: Cari Miller, co-founder of the AI Procurement Lab and the Center for Inclusive Change, an AI governance leader and risk expert.
IEEE P3119 Working Group: Comprises a global network of IEEE SA volunteers from diverse industries, collaborating to develop standards addressing market needs and societal benefits. The group integrates expertise from government workers, policymakers, and technologists.
Inspiration: The standard was inspired by the AI and Procurement: A Primer report from the New York University Center for Responsible AI.
The IEEE P3119 standard is a collaborative effort to address the unique challenges of AI procurement, emphasizing ethical and responsible innovation for public benefit
Title: IEEE P3120 – Standard for Quantum Computing Architecture
Scope: The IEEE P3120 standard defines a general architecture for quantum computers, focusing on the structure and organization of quantum computing systems. It covers the overall system architecture, including quantum hardware components (e.g., qubits, quantum gates), control systems, interfaces with classical computing systems, and software layers for programming and operation. The standard aims to provide a framework for designing interoperable and scalable quantum computing systems, addressing both hardware and software considerations for quantum and hybrid quantum-classical architectures.
Purpose: The purpose of IEEE P3120 is to establish a standardized framework to guide the design, development, and integration of quantum computing systems. It seeks to ensure consistency, interoperability, and scalability across quantum computing platforms, facilitating innovation and collaboration in the quantum computing ecosystem. By providing clear architectural guidelines, the standard supports developers, researchers, and industry stakeholders in building reliable and efficient quantum computers, bridging the gap between theoretical quantum computing and practical implementation.
Developmental Timelines:
September 21, 2023: The IEEE Standards Association (SA) Standards Board approved the Project Authorization Request (PAR) for P3120, initiating the project under the IEEE Computer Society’s Microprocessor Standards Committee (C/MSC).
2023–Ongoing: Development is in progress, with no confirmed publication date in available sources. As a standards development project, it involves iterative drafting, review, and consensus-building, typical of IEEE processes, which can span several years.
The standard is being developed as a voluntary standard, with potential for testing and refinement through industry and academic collaboration.
By Whom:
Sponsor: IEEE Computer Society, specifically the Microprocessor Standards Committee (C/MSC), which oversees standards related to microprocessor and computing architectures.
Working Group: The IEEE P3120 Working Group consists of volunteers from academia, industry, and research institutions with expertise in quantum computing, computer architecture, and related fields. Specific chairs or members are not detailed in available sources, but IEEE SA working groups typically include global experts from relevant domains.
Stakeholders: The development involves contributions from quantum computing researchers, hardware manufacturers, software developers, and standardization experts to ensure a comprehensive and practical standard.
The IEEE P3120 standard is a critical step toward formalizing quantum computing architectures, aiming to support the growing quantum technology industry with a robust and interoperable framework.
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Meatloaf is traced back to ancient times when people started combining ground meat with other ingredients for a more economical and substantial meal.
Colonial America: The concept of mixing ground meat with breadcrumbs or grains dates back to medieval Europe. When settlers arrived in North America, they adapted these techniques to the ingredients available to them, such as native grains and game meats.
19th Century: As meat grinders became more widely available in the 19th century, the preparation of ground meat dishes became easier. Meatloaf gained popularity during this time, with regional variations emerging across the United States. Families would incorporate various seasonings, herbs, and fillers based on local ingredients and preferences.
World War II and Post-War Era: Meatloaf became even more popular during World War II and the post-war era due to its affordability and versatility. Families could stretch a small amount of meat by combining it with breadcrumbs, rice, or oats, making it an economical choice during times of rationing.
1950s and 1960s: Meatloaf reached its peak of popularity in the mid-20th century, becoming a staple of American home cooking. The dish was often featured in cookbooks and advertisements, showcasing its versatility and ease of preparation.
Over time, meatloaf recipes have evolved with regional and personal preferences. Some variations include using different meats (beef, pork, veal, or a combination), adding vegetables, experimenting with various seasonings, and glazing with sauces.
Ketchup, 1 & 1/2 oz.
Water, 2 3/8 tsp.
Instructions
Preheat oven to 325 degrees.
Place ground beef in mixing bowl. Add eggs and combine.
Add ketchup (2 & 1/3 Tbsp.), mustard, Worcestershire sauce, Italian seasoning, onions, celery, breadcrumbs, salt, and pepper. Mix well to combine. Meatloaf should be firm. If mixture is not, add more bread crumbs.
Place meatloaf in a loaf pan. Make sure it is spread evenly.
Bake for about 45 minutes, uncovered. Remove from oven.
Combine ketchup (1 & 1/2 oz.) and water. Spread over meatloaf.
Return to oven for another 15-20 minutes, or until internal temperature reaches 155 degrees.
Let the meatloaf rest for 30 minutes prior to cutting.
Bert Askwith worked his way through college shuttling students to and from Detroit Metropolitan Airport until his graduation in 1931; when two semester tuition cost $300. With no student debt he founded and grew Campus Coach Lines that still provides the same services at many other US campuses. He donated part of his fortune to establish a cafe in the Undergraduate Library; which now serves an expanding and bewildering catalog of caffeine-based drinks found in educational settlements worldwide.
When new foreign national students arrive at U.S. colleges and universities, they often enter environments where a left-leaning worldview is prominent and pervasive. Many campuses today emphasize themes of social justice, equity, and systemic critique that align with a preference for larger, more interventionist government. Faculty and administrative messaging can reinforce these ideas, portraying government as the primary instrument for addressing inequality, redistributing resources, and regulating social behavior.
For many international students, this can be both eye-opening and disorienting. They may come from countries with different political traditions, including skepticism of state power due to experiences with corruption or authoritarianism. Yet, in the university setting, they encounter curricula and campus cultures that valorize activist government solutions while often criticizing markets, traditional institutions, or individual responsibility. Programs in the humanities and social sciences in particular tend to frame policy questions through a progressive lens that sees expansive government action as not just beneficial but morally necessary.
This immersion—some might call it a “marination”—shapes perspectives over time. Students adopt the language of social justice, climate policy, and identity politics, often without exposure to robust counterarguments. While this experience can broaden horizons and foster empathy for marginalized groups, it also risks narrowing intellectual diversity and suppressing debate about the limits and costs of government power. For international students, it can mean graduating with a worldview significantly more aligned with the American academic left than with the diverse political traditions of their home countries.
“303.1.4: Accessory religious educational rooms and religious auditoriums with occupant loads less than 100 per room or space are not considered separate occupancies.” This informs how fire protection systems are designed.
“305.2.1: Rooms and spaces within places of worship proving such day care during religious functions shall be classified as part of the primary occupancy.” This group includes building and structures or portions thereof occupied by more than five children older than 2-1/2 years of age who receive educational, supervision or personal care services for fewer than 24 hours per day.
“308.5.2: Rooms and spaces within places of religious worship providing [Group I-4 Day Care Facilities] during religious functions shall be classified as part of the primary occupancy. When [Group I-4 Day Care Facilities] includes buildings and structures occupied by more than five persons of any age who receive custodial care for fewer than 24 hours per day by persons other than parents or guardians, relatives by blood, marriage or adoption, and in a place other than the home of the person cared for.
Tricky stuff — and we haven’t even included conditions under which university-affiliated places of worship may expected to be used as community storm shelters.
Public response to Committee Actions taken in Orlando in April will be received until July 8th.
Because standard development tends to be a backward-looking domain it is enlightening to understand the concepts in play in previous editions. The complete monograph of proposals for new building safety concepts for places of worship for the current revision cycle is linked below:
A simple search on the word “worship” will reveal what ideas are in play. With the Group B Public Comment Hearings now complete ICC administered committees are now curating the results for the Online Governmental Consensus Vote milestone in the ICC process that was completed December 6th. Status reports are linked below:
A quick review of the appeals statements reveals some concern over process, administration and technical matters but none of them directly affect how leading practice for places of worship is asserted.
We are happy to get down in the weeds with facility professionals on other technical issues regarding other occupancy classes that are present in educational communities. See our CALENDAR for next Construction (Ædificare) colloquium open to everyone.
New update alert! The 2022 update to the Trademark Assignment Dataset is now available online. Find 1.29 million trademark assignments, involving 2.28 million unique trademark properties issued by the USPTO between March 1952 and January 2023: https://t.co/njrDAbSpwBpic.twitter.com/GkAXrHoQ9T
