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"Some AI prompts could cause 50 times more CO₂ emissions than others, researchers find"
#AI #ArtificialIntelligence #Technology

Some AI prompts could cause 50 times more CO₂ emissions than others, researchers find
No matter which questions we ask an AI, the model will come up with an answer. To produce this information—regardless of whether the answer is correct or not—the model uses tokens. Tokens are words or parts of words that are converted into a string of numbers that can be processed by the LLM.

Principle Methods of Rendering Non-equivalent Words from Uzbek and Dari to Russian and English
Mohammad Ibrahim Qani
https://arxiv.org/abs/2508.15453 https://

Principle Methods of Rendering Non-equivalent Words from Uzbek and Dari to Russian and English
These pure languages understanding directly relates to translation knowledge where linguists and translators need to work and research to eradicate misunderstanding. Misunderstandings mostly appear in non-equivalent words because there are different local and internal words like food, garment, cultural and traditional words and others in every notion. Truly, most of these words do not have equivalent in the target language and these words need to be worked and find their equivalent in the targe…

Words like “prowess” and “tapestry,”
which are favored by ChatGPT,
are creeping into our vocabulary,
while words like “bolster,” “unearth,” and “nuance,”
words less favored by ChatGPT,
have declined in use.
Researchers are already documenting shifts in the way we speak and communicate as a result of ChatGPT
— and they see this linguistic influence accelerating into something much larger.

You sound like ChatGPT
AI isn’t just impacting how we write — it’s changing how we speak and interact with others. And there’s only more to come.

Traditionsbetrieb zu - Millionen benötigt! Bürger wollen Wirtshaus retten #News #Nachrichten

Das hier (#IFG -Anfragem zu verlangen, scheint sc…

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

Evaluation methodology of Model Predictive Controllers for building's energy systems
Ali Chouman (UGA, CSTB), Peter Riederer (CSTB), Fr\'ed\'eric Wurtz (UGA)
https://arxiv.org/abs/2506.17291

Evaluation methodology of Model Predictive Controllers for building's energy systems
Climate change poses a serious threat to the Earth's ecosystems, fueled primarily by escalating greenhouse gas emissions. Among the main contributors, the building sector stands out due to its significant energy demand. Addressing this challenge requires innovative techniques in the control of energy systems in buildings. This paper deals with the formulation of a methodology designed to evaluate the performance of these controllers. The evaluation process involves the establishment of a compre…

"Cutting funding for science research will have far-reaching consequences" - "We are on the precipice of losing an entire generation of scientists."- InForum | #Fargo #NorthDakota

Letter: Cutting funding for science research will have far-reaching consequences
"The loss of these training programs harms the students of North Dakota - they are losing opportunities to explore possible careers as scientists. We are on the precipice of losing an entire generation of scientists," writes Jenni Momsen of Fargo.

Memoirs of the CP/M creator released:
“Our father, Gary Kildall, was one of the founders of the personal computer industry, but you probably don’t know his name. Those who have heard of him may recall the myth that he ‘missed’ the opportunity to become Bill Gates by going flying instead of meeting with IBM. Unfortunately, this tall tale paints Gary as a ‘could-have-been,’ ignores his deep contributions, and overshadows his role as an inventor of key technologies that define how compute…

In His Own Words: Gary Kildall
Gary Kildall was a pioneer of personal computer software. He wrote programming language tools, including assemblers (Intel 4004), interpreters (BASIC), and compilers (PL/M). He created a widely-used disk operating system (CP/M). He and his wife, Dorothy McEwen, started a successful company called Digital Research to develop and market CP/M, which for years was the dominant operating system for personal microcomputers. Thousands of programs were written to run under it, and a million or more peo…

Stemming -- The Evolution and Current State with a Focus on Bangla
Abhijit Paul, Mashiat Amin Farin, Sharif Md. Abdullah, Ahmedul Kabir, Zarif Masud, Shebuti Rayana
https://arxiv.org/abs/2508.15711

Stemming -- The Evolution and Current State with a Focus on Bangla
Bangla, the seventh most widely spoken language worldwide with 300 million native speakers, faces digital under-representation due to limited resources and lack of annotated datasets. Stemming, a critical preprocessing step in language analysis, is essential for low-resource, highly-inflectional languages like Bangla, because it can reduce the complexity of algorithms and models by significantly reducing the number of words the algorithm needs to consider. This paper conducts a comprehensive su…