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 efficient models for onboard operations [1] - Jeff Dean highlighted the significance of knowledge distillation in AI, reflecting on its initial rejection by NeurIPS 2014, which underestimated its potential impact [3][4] Group 1: 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 systemic lag in recognizing groundbreaking innovations [6] - Notable figures in AI, including Geoffrey Hinton and Yann LeCun, faced rejection for their pioneering work, often due to reasons that seem absurd in hindsight, such as claims of lacking theoretical basis or being overly simplistic [6] Group 2: Specific Case Studies of Rejected Innovations - LSTM, a milestone in handling sequential data, was rejected by NIPS in 1996 during a period when statistical methods were favored, only to later dominate fields like speech recognition [8] - The SIFT algorithm, which ruled the computer vision domain for 15 years, faced rejection from ICCV and CVPR due to its perceived complexity and lack of elegance, ultimately proving the value of robust engineering design [11] - Dropout, a key regularization method for deep neural networks, was rejected by NIPS in 2012 for being too radical, yet it became crucial for the success of models like AlexNet [17] - Word2Vec, despite its revolutionary impact on NLP, received a strong rejection at ICLR 2013 due to perceived lack of scientific rigor, but it quickly became a cornerstone of text representation [19][20] Group 3: Reflection on Peer Review Limitations - The peer review system often struggles to recognize disruptive innovations, leading to a "simplicity trap" where reviewers equate mathematical complexity with research contribution [40] - Reviewers tend to maintain existing paradigms, which can hinder the acceptance of novel ideas that challenge traditional metrics of success [40] - The demand for rigorous theoretical proof in an experimental field like deep learning can stifle practical breakthroughs, as seen with the initial skepticism towards methods like Adam optimizer [40] Group 4: Broader Implications - The experiences of rejected papers illustrate the nonlinear nature of scientific progress, highlighting that peer review, while essential, is limited by human cognitive biases [41] - Historical anecdotes, such as Einstein's rejection of a paper on gravitational waves, emphasize that the true measure of a research's impact is its long-term relevance rather than immediate acceptance [42][44]