Opinion | Is AI the Next Climate Change? - WSJ
<a href="https://news.google.com/rss/articles/CBMi-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?oc=5" target="_blank">Opinion | Is AI the Next Climate Change?</a> <font color="#6f6f6f">WSJ</font>
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opinion![[D] How do ML engineers view vibe coding?](https://images.unsplash.com/photo-1460925895917-afdab827c52f?w=600&q=80)
[D] How do ML engineers view vibe coding?
<!-- SC_OFF --><div class="md"><p>I've seen, read and heard a lot of mixed reactions about software engineers (ie. the ones who aren't building ML models and make purely deterministic software) giving their opinions on AI usage. Some say it speeds up their workflow as it frees up their time so that they can focus on the more creative and design-oriented tasks, some say it slows them down because they don't want to spend their time reviewing AI-generated code, and a lot of other views I can't really capture in one post, and I do acknowledge the discussion on this topic is not so black and white.</p> <p>That being said, I'm sort of under the impression that ML Engineers are not strictly software engineers, even though there may be some degree of commonality between the both, and since that m
SCoOP: Semantic Consistent Opinion Pooling for Uncertainty Quantification in Multiple Vision-Language Model Systems
arXiv:2603.23853v2 Announce Type: replace-cross Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models' outputs amplifies uncertainty and increases the risk of hallucinations. We propose SCoOP (Semantic-Consistent Opinion Pooling), a training-free uncertainty quantification (UQ) framework for multi-VLM systems through uncertainty-weighted linear opinion pooling. The core idea is to treat each VLM as a probabilistic "expert," sample multiple outputs, map them to a unified space, aggregate their opinions, and produce a system-level uncertainty score. Unlike prior UQ methods designed for single models, SCoOP explicitly measures collective, system-level uncertainty across multiple VLMs, enabling effective
News - Opinion: How AI augments, not replaces the Army professional - DVIDS
<a href="https://news.google.com/rss/articles/CBMikAFBVV95cUxOQWRaVlFBVkktcjZDVTJ5WGw3eVZpWHdabDRBRXEyd1RpRkxkUDZGYmxyNEI4MTMtZTlFT1RuOThGZlRXTlNDZUxCcG5ydWM2bWgxS0xPQTFCU01kSWRKWkNBelhsd2I3bEh4OUpxWkluak0yOGdfT08tdTR4Skx0YmdjYUZEbjVVczBSRUk1R3o?oc=5" target="_blank">News - Opinion: How AI augments, not replaces the Army professional</a> <font color="#6f6f6f">DVIDS</font>
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