"Massive budget cuts for US science proposed again by Trump administration."
https://www.nature.com/articles/d41586-026-01105-7

"For the second year in a row, US President Donald Trump has proposed significant cuts to the budgets of major US science agencies…[The plan] includes a ban on using federal funds for subscriptions and publishing fees for some academic journals…Some of the steepest cuts would be made to the National Science Foundation (#NSF) and the Environmental Protection Agency (#EPA): the budgets of both would fall more than 50% in 2027 compared to their current levels…The budget for the US National Institutes of Health [#NIH] would drop 13%…The budget would increase funding for…the military, which would receive US $1.5 trillion, a 44% increase."

#DefendResearch #Funding #Trump #TrumpVResearch #USPol #USPolitics

"Congress set to reject Trump’s major budget cuts to NSF, NASA, and energy science."
https://www.science.org/content/article/congress-set-reject-trump-s-major-budget-cuts-nsf-nasa-and-energy-science

"The U.S. Congress has delivered another rebuke of President Donald Trump’s plans to slash this year’s budgets of several science agencies. Today, lawmakers hammering out final bills covering the National Science Foundation (#NSF), #NASA science, and Department of Energy (#DOE) research programs unveiled an agreement to spend very close to current levels."

#DefendResearch #Funding #Trump #TrumpVResearch #USPol #USPolitics

#NSF #NSFFunding #funding #FederalFunding #GrantFunding #science #ScienceFunding #AntiScience #AntiIntellectual #USPol

https://mastodon.world/@Mer__edith/113197090927589168

Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI

With the growing attention and investment in recent AI approaches such as large language models, the narrative that the larger the AI system the more valuable, powerful and interesting it is is increasingly seen as common sense. But what is this assumption based on, and how are we measuring value, power, and performance? And what are the collateral consequences of this race to ever-increasing scale? Here, we scrutinize the current scaling trends and trade-offs across multiple axes and refute two common assumptions underlying the 'bigger-is-better' AI paradigm: 1) that improved performance is a product of increased scale, and 2) that all interesting problems addressed by AI require large-scale models. Rather, we argue that this approach is not only fragile scientifically, but comes with undesirable consequences. First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint. Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate. Finally, it exacerbates a concentration of power, which centralizes decision-making in the hands of a few actors while threatening to disempower others in the context of shaping both AI research and its applications throughout society.

I've heard through the grapevine that US federal grantmaking agencies like the #NSF (National Science Foundation) are also consolidating around generative AI. This trend is evident if you follow directorates like CISE (Computer and Information Science and Engineering). A friend told me there are several NSF programs that tacitly demand LLMs of some form be used in project proposals, even when doing so is not obviously appropriate. A friend of a friend, who is a university professor, has said "if you're not doing LLMs you're not doing machine learning".

This is an absolutely devastating mindset. While it might be true at a certain cynical, pragmatic level, it's clearly indefensible at an intellectual, scholarly, scientific, and research level. Willingly throwing away the diversity of your own discipline is bizarre, foolish, and dangerous.

#AI #GenAI #GenerativeAI #LLM #ML