When universities speak of “campus growth,” what kind of growth do they mean? More students? (At the moment, international students comprise about 5 percent of total enrollment) More research? (61 percent of graduate students were international) More healthcare? (Healthcare enterprises are moving off campus to where the patients are). The traditional campus concentrated infrastructure — the modern academic health system distributes it. (At what point does a university cease to be a campus and become a regional enterprise?) More computing? (Electric or absorption chillers). Or are they simply replacing infrastructure that has reached the end of its useful life?
Data centers in colleges and universities are crucial for supporting the extensive technological infrastructure required for modern education and research. These centers house critical servers and storage systems that manage vast amounts of data, ensuring reliable access to academic resources, administrative applications, and communication networks. They enable the secure storage and processing of sensitive information, including student records, faculty research, and institutional data.
Moreover, data centers facilitate advanced research by providing the computational power needed for data-intensive studies in fields like bioinformatics, climate science, and artificial intelligence. They support virtual learning environments and online course management systems, essential for the increasingly prevalent hybrid and online education models. Efficient data centers also contribute to campus sustainability goals by optimizing energy use through modern, eco-friendly technologies.
Additionally, robust data center infrastructure enhances the university’s ability to attract top-tier faculty and students by demonstrating a commitment to cutting-edge technology and resources. They also play a vital role in disaster recovery and business continuity, ensuring that educational and administrative functions can resume quickly after disruptions. Overall, data centers are integral to the academic mission, operational efficiency, and strategic growth of colleges and universities.
Standards distinguish meaningful patterns from imagined ones.
We have followed development of the technical standards that govern the success of these “installations” since 1993; sometimes nudging technical committees — NFPA, IEEE, ASHRAE, BICSI and UL. The topic is vast and runs fast so today we will review, and perhaps respond to, the public consultations that are posted on a near-daily basis. Use the login credentials at the upper right of our home page.
Introduction. [Abstract]. The rapid growth of data centers, with their enormous energy and water demands, necessitates targeted policy interventions to mitigate environmental impacts and protect local communities. To address these issues, states with existing data center tax breaks should adopt sustainable growth policies for data centers, mandating energy audits, strict performance standards, and renewable energy integration, while also requiring transparency in energy usage reporting. “Renewable energy additionality” clauses should ensure data centers contribute to new renewable capacity rather than relying on existing resources. If these measures prove insufficient, states should consider repealing tax breaks to slow unsustainable data center growth. States without tax breaks should avoid such incentives altogether while simultaneously implementing mandatory reporting requirements to hold data centers accountable for their environmental impact. Broader measures should include protecting local tax revenues for schools, regulating utility rate hikes to prevent cost-shifting to consumers, and aligning data center energy demands with state climate goals to avoid prolonging reliance on fossil fuels.
Abstract: Power grid operation and maintenance decision-making reform is an important part of power system reform. With the construction of massive historical quasi real-time data management platform, the reform of power system is also advancing. However, in the face of massive data explosion, the business level and business logic become disorganized and redundant. Based on the actual situation of Shenzhen Power Supply Bureau, the sg-erp data center is composed of structured data center, massive data center, unstructured data center and power grid GIS data center. With the unprecedented growth of business application data, the data center can improve business logic and promote power system reform. The experimental results show that big data technology has a broad application prospect in the reform of power industry.
American English is effectively the de facto reference language for most modern LLM tokenization. During today’s session we explore the at-present advantage Americans have in the development of artificial applications — whether it should always be that way or not. Tokenization isn’t language-neutral — it’s heavily skewed toward English due to data realities. This is one of the core reasons why “English-first” prompting often works best in today’s LLMs.
We will use the document linked below to begin the exploration:
Final Input to the AI Model:
[464, 2068, 7583, 1776, 18045, 625, 262, 16925, 3290, 13]
Background:
The model only sees this list of numbers. It has no direct understanding of English words anymore — it learned patterns from billions of examples during training using these number sequences.This numerical input then goes through embeddings (turning numbers into vectors), attention layers, etc., to generate a response. Most widely used tokenizers (e.g., OpenAI’s tiktoken, Llama’s, etc.) are trained primarily on English-heavy datasets (often 60–90%+ English in pre-training corpora).
Outcome:
Better compression for English — Common English words and patterns become single tokens or short subwords.
Worse efficiency for other languages — Non-English text often gets fragmented into more tokens (sometimes 2–5× more for the same semantic content).
Impact:
Higher token counts = higher API costs and shorter effective context windows for non-English users.
Poorer downstream performance on non-English tasks.
English becomes the “cheapest” and often “best-performing” language for prompting and reasoning.
Studies consistently show this “tokenization tax” or “language premium”: English typically has the lowest token-per-character or token-per-meaning ratio in major models.
Bias:
Multilingual models still underperform on low-resource languages.
It reinforces English as the default language for AI development.
It affects fairness, accessibility, and global adoption.
Efforts to fix this include dedicated multilingual tokenizers, language-specific fine-tuning, and more balanced approaches. However, because English dominates training data and benchmarks, it remains the practical standard that everything else is measured against.
Tokenization isn’t language-neutral — it’s heavily skewed toward English due to data realities. This is one of the core reasons why “English-first” prompting often works best in today’s LLMs.
* StandardsMichigan.COM normally deals with Language issues every Monday at least once per month.
Open source standards development is characterized by very open exchange, collaborative participation, rapid prototyping, transparency and meritocracy. The Python programming language is a high-level, interpreted language that is widely used for general-purpose programming. Python is known for its readability, simplicity, and ease of use, making it a popular choice for beginners and experienced developers alike. Python has a large and active community of developers, which has led to the creation of a vast ecosystem of libraries, frameworks, and tools that can be used for a wide range of applications. These include web development, scientific computing, data analysis, machine learning, and more.
Another important aspect of Python is its versatility. It can be used on a wide range of platforms, including Windows, macOS, Linux, and even mobile devices. Python is also compatible with many other programming languages and can be integrated with other tools and technologies, making it a powerful tool for software development. Overall, the simplicity, readability, versatility, and large community support of Python make it a valuable programming language to learn for anyone interested in software development including building automation.
As open source software, anyone may suggest an improvement to Python(3.X) starting at the link below:
Python can be used to control building automation systems. Building automation systems are typically used to control various systems within a building, such as heating, ventilation, air conditioning, lighting, security, and more. Python can be used to control these systems by interacting with the control systems through the building’s network or other interfaces.
There are several Python libraries available that can be used for building automation, including PyVISA, which is used to communicate with instrumentation and control systems, and PyModbus, which is used to communicate with Modbus devices commonly used in building automation systems. Python can also be used to develop custom applications and scripts to automate building systems, such as scheduling temperature setpoints, turning on and off lights, and adjusting ventilation systems based on occupancy or other variables. Overall, Python’s flexibility and versatility make it well-suited for use in building automation systems.
.@PyCon US 2025 is a wrap, and our hearts are full with #Python community love! Thanks to every single one of you who organized, volunteered, attended, & sponsored 🐍🫶 #PyConUS
The PSF office will be closed May 26-28 so our staff can rest & recover. See you back online soon! pic.twitter.com/Sy1hiRmvw4
— Python Software Foundation (@ThePSF) May 27, 2025
The International Trade Administration (ITA) of the U.S. Department of Commerce (DOC) is requesting public comments to gain insights on the current global artificial intelligence (AI) market. Responses will provide clarity about stakeholder concerns regarding international AI policies, regulations, and other measures which may impact U.S. exports of AI technologies. Additionally, the request for information (RFI) includes inquiries related to AI standards development. ANSI encourages relevant stakeholders to respond by ITA’s deadline of October 17, 2022.
They put in new turf and then immediately covered it with thousands of pounds of equipment and rigging for the Morgan Wallen concert? https://t.co/B37WCDpVHx
“Whom the gods love, die young.” Menander (341-290 BCE)
“My mother was a Protestant, of a traditional American, vague kind: she belonged to the church that the nice people in the neighbourhood went to. My wife is a Catholic, the kids are Catholic, so I’m a Catholic fellow-traveller.”
“When buying and selling are controlled by legislation, the first things to be bought and sold are legislators.”
“If government were a product, selling it would be illegal.”
“The Democrats are the party that says government will make you smarter, taller, richer, and remove the crabgrass on your lawn. The Republicans are the party that says government doesn’t work and then they get elected and prove it.”
“Not much was really invented during the Renaissance, if you don’t count modern civilization.”
“No humorist is under any obligation to provide answers and probably if you were to delve into the literary history of humour it’s probably all about not providing answers because the humorist essentially says: this is the way things are.”
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
