I’m pleased to see this line of thought from @…. I’ve been circulating the related phrase “information pollution” (e.g. here: https://hachyderm.io/@inthehands/113266954128561131) accompanied by many related thoughts. We need more people thinking along these lines.
https://scholar.social/@gedankenstuecke/115723930449871851
There tends to be this implicit assumption that only the state can provide services to the people. It's endemic to liberals. If authoritarians seize power, the thinking goes, there's nothing we can do.
But we have ample examples of the opposite. Trumpism is a specific type of authoritarianism where the state withdraws to allow corporations to control most things. They get a degree of autonomy and, in exchange, bow to the sovereign.
This is common in South American dictatorships (often those backed and supported by the US). So it can be helpful to look to these as examples of how to deal with that type of regime.
#USPol
Came across this 2010 blog post about mindfulness in computing and so much of these behaviors have only intensified to new extremes with LLM usage. So much so that not only is the process of software creation being quickly supplanted by prompts and (stochastic) "search" assemblies, but more generally the kind of mindfulness talked about in the post (here meaning thinking through & solving a problem yourself[1]) is now being openly discouraged by industry and forcefully delegate…
"Writing your idea down is not starting the damn game. Writing a design document is not starting the damn game. Assembling a team is not starting the damn game. Even doing graphics or music is not starting the damn game. It’s easy to confuse “preparing to start the damn game” with “starting the damn game”. Just remember: a damn game can be played, and if you have not created something that can be played, it’s not a damn game!"
Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Asmit Bandyopadhyay, Anindita Das Bhattacharjee, Rakesh Das
https://arxiv.org/abs/2511.12346 https://mastoxiv.page/@arXiv_csCV_bot/115570753208147835
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without ...
Dimitris Oikonomou, Nicolas Loizou
https://arxiv.org/abs/2512.02342 https://mastoxiv.page/@arXiv_mathOC_bot/115654870924418771
- Predictive Modeling of I/O Performance for Machine Learning Training Pipelines: A Data-Driven App...
Karthik Prabhakar, Durgamadhab Mishra
https://arxiv.org/abs/2512.06699 https://mastoxiv.page/@arXiv_csPF_bot/115688618582182232
- Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translat...
Lyu, Song, Kamigaito, Ding, Tanaka, Utiyama, Funakoshi, Okumura
https://arxiv.org/abs/2512.07540 https://mastoxiv.page/@arXiv_csCL_bot/115689532163491162
- In-Context Learning for Seismic Data Processing
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
https://arxiv.org/abs/2512.11575 https://mastoxiv.page/@arXiv_csCV_bot/115723040285820239
- Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy
https://arxiv.org/abs/2512.12218 https://mastoxiv.page/@arXiv_csCV_bot/115729165330908574
- Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity P...
Kei Saito
https://arxiv.org/abs/2512.13478 https://mastoxiv.page/@arXiv_csCL_bot/115729234145554554
- Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock, Andrey Morozov
https://arxiv.org/abs/2512.15675 https://mastoxiv.page/@arXiv_csCV_bot/115740141862163631
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, Elvis Nava
https://arxiv.org/abs/2512.15692 https://mastoxiv.page/@arXiv_csRO_bot/115739947869830764
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