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"Mexico Dolphin Show Ban, Paving the Way for Ethical Wildlife Tourism Alternatives"
#Mexico #Dolphins #Animals

Mexico Dolphin Show Ban, Paving the Way for Ethical Wildlife Tourism Alternatives | Happy Eco News
The Mexico dolphin show ban in 2025 promotes ethical wildlife tourism alternatives. Learn how this historic law changes travel and why responsible whale-watching is replacing cruel captive entertainment across Latin America.

European citizens’ initiative fur free europe. Send a message to EU Commissioners! #AnimalRights

Fur Free Europe
Over 1.5 million European citizens are calling for a #FurFreeEurope.

„Just for shits and giggles, you can de-sham the chronostatiomatrix by running —()()(]]asdg a=-do —cd go cd stay —sususudododo baby shark—][] but that’s optional.“
https://infosec.exchange/@masek/115824312054188754

Cynicism, "AI"
I've been pointed out the "Reflections on 2025" post by Samuel Albanie [1]. The author's writing style makes it quite a fun, I admit.
The first part, "The Compute Theory of Everything" is an optimistic piece on "#AI". Long story short, poor "AI researchers" have been struggling for years because of predominant misconception that "machines should have been powerful enough". Fortunately, now they can finally get their hands on the kind of power that used to be only available to supervillains, and all they have to do is forget about morals, agree that their research will be used to murder millions of people, and a few more millions will die as a side effect of the climate crisis. But I'm digressing.
The author is referring to an essay by Hans Moravec, "The Role of Raw Power in Intelligence" [2]. It's also quite an interesting read, starting with a chapter on how intelligence evolved independently at least four times. The key point inferred from that seems to be, that all we need is more computing power, and we'll eventually "brute-force" all AI-related problems (or die trying, I guess).
As a disclaimer, I have to say I'm not a biologist. Rather just a random guy who read a fair number of pieces on evolution. And I feel like the analogies brought here are misleading at best.
Firstly, there seems to be an assumption that evolution inexorably leads to higher "intelligence", with a certain implicit assumption on what intelligence is. Per that assumption, any animal that gets "brainier" will eventually become intelligent. However, this seems to be missing the point that both evolution and learning doesn't operate in a void.
Yes, many animals did attain a certain level of intelligence, but they attained it in a long chain of development, while solving specific problems, in specific bodies, in specific environments. I don't think that you can just stuff more brains into a random animal, and expect it to attain human intelligence; and the same goes for a computer — you can't expect that given more power, algorithms will eventually converge on human-like intelligence.
Secondly, and perhaps more importantly, what evolution did succeed at first is achieving neural networks that are far more energy efficient than whatever computers are doing today. Even if indeed "computing power" paved the way for intelligence, what came first is extremely efficient "hardware". Nowadays, human seem to be skipping that part. Optimizing is hard, so why bother with it? We can afford bigger data centers, we can afford to waste more energy, we can afford to deprive people of drinking water, so let's just skip to the easy part!
And on top of that, we're trying to squash hundreds of millions of years of evolution into… a decade, perhaps? What could possibly go wrong?
[1] #NoAI #NoLLM #LLM

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Towards Scalable Oversight via Partitioned Human Supervision
Ren Yin, Takashi Ishida, Masashi Sugiyama
https://arxiv.org/abs/2510.22500 https://mastoxiv.page/@arXiv_csLG_bot/115451787490434401
- ContextPilot: Fast Long-Context Inference via Context Reuse
Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai
https://arxiv.org/abs/2511.03475 https://mastoxiv.page/@arXiv_csLG_bot/115502245581974540
- Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Nabil Belacel, Mohamed Rachid Boulassel
https://arxiv.org/abs/2601.11283 https://mastoxiv.page/@arXiv_csLG_bot/115921183182326799
- PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Akila Sampath, Vandana Janeja, Jianwu Wang
https://arxiv.org/abs/2601.17074
- SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras, Fabio Brau, Maura Pintor, Battista Biggio
https://arxiv.org/abs/2602.03596
- LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordina...
Anand, Helbling, Davenport, Berman, Alagapan, Rozell
https://arxiv.org/abs/2602.04192
- Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
https://arxiv.org/abs/2602.07712 https://mastoxiv.page/@arXiv_csLG_bot/116046369672796465
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-T\"ur, Hao Peng
https://arxiv.org/abs/2602.07729 https://mastoxiv.page/@arXiv_csLG_bot/116046377539155485
- AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine L...
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen
https://arxiv.org/abs/2602.07906 https://mastoxiv.page/@arXiv_csLG_bot/116046423413650658
- VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
https://arxiv.org/abs/2602.10693 https://mastoxiv.page/@arXiv_csLG_bot/116057229834947730
- KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang
https://arxiv.org/abs/2602.11184 https://mastoxiv.page/@arXiv_csLG_bot/116062537528208461
- MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Correia, Ferreira, Martins, Bento, Guerreiro, Pereira, Gomes, Bono, Ferreira, Bizarro
https://arxiv.org/abs/2602.11776 https://mastoxiv.page/@arXiv_csLG_bot/116062952355379801
- Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
https://arxiv.org/abs/2602.13813 https://mastoxiv.page/@arXiv_csLG_bot/116085828112928218
- Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misa...
Hong Li, Zhen Zhou, Honggang Zhang, Yuping Luo, Xinyue Wang, Han Gong, Zhiyuan Liu
https://arxiv.org/abs/2602.14462 https://mastoxiv.page/@arXiv_csLG_bot/116085997857526328
- Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
https://arxiv.org/abs/2602.14495 https://mastoxiv.page/@arXiv_csLG_bot/116086011618741857
- \"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, et al.
https://arxiv.org/abs/2602.15210 https://mastoxiv.page/@arXiv_csLG_bot/116090912256712568
- GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, et al.
https://arxiv.org/abs/2602.15763 https://mastoxiv.page/@arXiv_csLG_bot/116091080686771018
- Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganizat...
Jayadev Billa
https://arxiv.org/abs/2602.15997 https://mastoxiv.page/@arXiv_csLG_bot/116096541546306333
- AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
KC Santosh, Srikanth Baride, Rodrigue Rizk
https://arxiv.org/abs/2602.16042 https://mastoxiv.page/@arXiv_csLG_bot/116096581524696028
- Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye, Ziyu Zhao, Yuhe Jiang, Tara Saba, Xinyu Wang, Xujie Si
https://arxiv.org/abs/2602.16947 https://mastoxiv.page/@arXiv_csLG_bot/116102426238903124
toXiv_bot_toot

Good for Josh Segarra that he is well employed, but his characters in Best Medicine, The Big Door Prize, Animal Control and Abbott Elementary are so similar to each other, it makes his presence irritating.

"Deforestation leads to more extreme weather events in the Amazon region"
#AmazonRainforest #Trees #Environment

Deforestation leads to more extreme weather events in the Amazon region
From a bird's eye view, the Amazon rainforest appears as a lush green mosaic of treetops stretching as far as the eye can see. It is home to countless animal and plant species, many of which are endemic, and the forest plays an important role in the global climate as a carbon sink. However, deforestation threatens to destroy this unique ecosystem and its important functions. One-fifth of the area has already been cleared, and there is no end in sight to this intensive use. This has serious cons…

Deforestation leads to more extreme weather events in the Amazon region #Amazon

Deforestation leads to more extreme weather events in the Amazon region
From a bird's eye view, the Amazon rainforest appears as a lush green mosaic of treetops stretching as far as the eye can see. It is home to countless animal and plant species, many of which are endemic, and the forest plays an important role in the global climate as a carbon sink. However, deforestation threatens to destroy this unique ecosystem and its important functions. One-fifth of the area has already been cleared, and there is no end in sight to this intensive use. This has serious cons…

🇺🇦 #NowPlaying on #BBC6Music's #HuwStephens
Super Furry Animals:
🎵 God! Show Me Magic
#SuperFurryAnimals
https://open.spotify.com/track/6iIE1l1QopHXcDTfbLE9oI

🇺🇦 #NowPlaying on #BBC6Music's #LaurenLaverne
Super Furry Animals:
🎵 God! Show Me Magic
#SuperFurryAnimals
https://open.spotify.com/track/6iIE1l1QopHXcDTfbLE9oI