Core Insights - Waymo has released a deep blog detailing its AI strategy centered around its foundational model, emphasizing the use of distillation methods to create high-efficiency models for onboard operations [1][2] - Jeff Dean highlighted the significance of knowledge distillation, comparing it to the creation of the Gemini Flash model, which showcases the importance of distillation in AI model efficiency [1][2] Historical Context of Rejected Papers - Many foundational technologies in AI, such as optimizers for large models and computer vision techniques, were initially rejected by top conferences, showcasing a historical pattern of oversight in recognizing groundbreaking innovations [6] - Notable figures in AI, including Geoffrey Hinton and Yann LeCun, have faced rejection for their pioneering work, which was later recognized as transformative [6] Case Studies of Rejected Innovations - LSTM, a milestone for sequence data processing, was rejected by NIPS in 1996 but later became crucial in speech recognition and machine translation, highlighting the delayed recognition of its value [7][10] - SIFT, a dominant algorithm in computer vision, faced rejection from ICCV and CVPR due to its perceived complexity, yet proved to be vital in real-world image processing [11][13] - Dropout, a key regularization method for deep neural networks, was initially rejected for its radical approach but later became essential in training deep networks effectively [17][19] - Word2Vec, despite being rejected at ICLR, became a cornerstone in NLP due to its efficiency and practical application, eventually receiving recognition for its impact [20][24] - YOLO transformed object detection by prioritizing speed over precision, facing rejection for its perceived shortcomings but later becoming a widely adopted framework in the industry [28][30] Reflection on Peer Review Limitations - The peer review system often struggles to recognize disruptive innovations, leading to a systematic cognitive lag in evaluating groundbreaking research [40][41] - The tendency to equate mathematical complexity with research contribution can hinder the acceptance of simpler yet effective methods [41] - Historical examples illustrate that the true measure of a research's impact is not determined by initial peer review outcomes but by its long-term relevance and problem-solving capabilities [43][47]

作者：linlin 编辑：haina 2025 年 3 月 11 日，语音生成初创公司 Cartesia 宣布完成 6400 万美元 A 轮融资，距其 2700 万美元种 子轮融资仅过去不到 3 个月。本轮融资由 Kleiner Perkins 领投，Lightspeed、Index、A*、Greycroft、 Dell Technologies Capital 和 Samsung Ventures 等跟投。Cartesia 还同时推出了其旗舰产品 Sonic 2.0， 系统延迟从 90 毫秒缩短至 45 毫秒，为语音 AI 领域高效、实时且低成本的多模态交互提供了新动 力。 Cartesia 的核心团队均来自 Stanford AI labs，包括 Karan Goel、Albert Gu、Arjun Desai、Brandon Yang 四位校友及其共同导师 Chris Ré。团队共同的研究方向在于 SSM（状态空间模型）。从 S4 到 Mamba 的 SSM 系列研究，以线性时间复杂度，为解决 LLMs 主流架构 Transformer 在上下文长度的 固有局限提供了潜在解决方案，意味着更快的生成速度、 ...