Mentioning this is considered impolite. Sometimes, entire disciplines become infertile but are kept alive like endangered species, or they get cordycepted by political movements that wear the shell of the dead host to legitimize academic status and funding.
RESEARCH
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Academic Paradigms Obsolescence Through Intellectual Breakthroughs
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An interesting aspect of academic paradigms that is that many of them are dead ends, made obsolete by intellectual or practical breakthroughs in different areas of intellectual inquiry, or due to internal exhaustion or ideological corruption.
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Study: Perfect AI-human value alignment mathematically impossible
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Perfect alignment between #AI and human values is mathematically impossible, study says
by PNAS Nexus @TechXplore_com Learn more: https://
bit.ly/4e7EiqJ #MachineLearning #ArtificialIntelligence #ML -
AI Enters Real Adoption Phase: Focus Shifts to Profitability
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I think the claim that a crisis is approaching is exaggerated. Nevertheless, it's clear that AI is now entering the real adoption phase. Less all-in investment and research, and more focus on revenue and adoption. Against this backdrop, AI will now have to become profitable, and
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AI Agents Auditing Automation and Augmentation Across US Workforce
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Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce Shao et al.: https://
arxiv.org/abs/2506.06576 #ArtificialIntelligence #AIAgents -
Data Preparation Discipline Separates AI Experimenters from Producers
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The next 12 months will separate AI experimenters from AI producers. The difference won't be model choice – it'll be data preparation discipline.
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AI Agents Audit U.S. Workforce Automation and Augmentation Potential
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Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce Shao et al.: https://
arxiv.org/abs/2506.06576 #ArtificialIntelligence #AIAgents -
Models Reveal Only What Our Questions Expose
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what we observe is never the model itself, only the model exposed to our method of questioning
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ML Workflow: Data Understanding to Deployment and Monitoring
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ML isn’t magic — it’s a workflow. 1. understand data 2. choose right algorithm 3. train 4. test 5. optimize 6. deploy + monitor + retrain The winners are the teams who run this loop consistently. #MachineLearning #AI #DataScience #MLops
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Kimi Proposes Solution to LLM Prefill-Decode KV Cache Transfer Problem
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A new paper just exposed how much money AI labs waste on GPUs. Running LLMs at scale hits a wall when prefill and decode share one datacenter. The KV cache transfer between them is massive. This forces expensive RDMA networks and identical hardware everywhere. Kimi proposes