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Indigenous women engineered energy-efficient baby carriers
The technology helped them while harvesting the vast majority of their community's food. Apache, Navajo, and Shoshoni (pictured above) are only a few of the Indigenous tribes that utilized cradleboards. Breakthroughs, discoveries, and DIY tips sent every weekday. Indigenous women were technological trailblazers. But while lived experiences and communal histories have long supported this, they routinely fail to receive the credit they deserve .
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Inside the California 'AI factory' that showcases the contradiction at the heart of the tech race
Google's ultra-private CEO Sundar Pichai is showing me around Googleplex, its California headquarters. A walkway runs along the length of it, passing by a giant dinosaur skeleton, a beach volleyball pitch and dozens of Googlers lunching under the hazy November sun. But it's a laboratory, hidden away at the back of the campus behind some trees, that he is most excited to show me. This is where the invention that Google believes is its secret weapon is being developed. Known as a Tensor Processing Unit (or TPU), it looks like an unassuming little chip but, says Mr Pichai, it will one day power every AI query that goes through Google.
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Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark
Guo, Ziyu, Chen, Xinyan, Zhang, Renrui, An, Ruichuan, Qi, Yu, Jiang, Dongzhi, Li, Xiangtai, Zhang, Manyuan, Li, Hongsheng, Heng, Pheng-Ann
Recent video generation models can produce high-fidelity, temporally coherent videos, indicating that they may encode substantial world knowledge. Beyond realistic synthesis, they also exhibit emerging behaviors indicative of visual perception, modeling, and manipulation. Yet, an important question still remains: Are video models ready to serve as zero-shot reasoners in challenging visual reasoning scenarios? In this work, we conduct an empirical study to comprehensively investigate this question, focusing on the leading and popular Veo-3. We evaluate its reasoning behavior across 12 dimensions, including spatial, geometric, physical, temporal, and embodied logic, systematically characterizing both its strengths and failure modes. To standardize this study, we curate the evaluation data into MME-CoF, a compact benchmark that enables in-depth and thorough assessment of Chain-of-Frame (CoF) reasoning. Our findings reveal that while current video models demonstrate promising reasoning patterns on short-horizon spatial coherence, fine-grained grounding, and locally consistent dynamics, they remain limited in long-horizon causal reasoning, strict geometric constraints, and abstract logic. Overall, they are not yet reliable as standalone zero-shot reasoners, but exhibit encouraging signs as complementary visual engines alongside dedicated reasoning models. Project page: https://video-cof.github.io
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