编译 | Tina Meta 首席执行官马克·扎克伯格（Mark Zuckerberg）在周三分享了他对"个人超级智能"（personal superintelligence）的愿景——也就是每个人都能够 借助 AI 实现自己的个人目标。 不过，在这封信中也悄然透露出一个信号：Meta 正在调整其 AI 模型的发布策略，以更好地推动"超级智能"的发展。 扎克伯格写道："我们相信，超级智能带来的好处应尽可能广泛地惠及全球。但与此同时，它也将带来前所未有的安全挑战。我们必须严谨地管理这些风 险，并慎重考虑哪些内容适合开源。" 这段关于开源的表述耐人寻味。一直以来，扎克伯格都将 Meta 的 Llama 开源模型系列视为公司区别于 OpenAI、xAI 和 Google DeepMind 等竞争对手 的关键优势。Meta 的目标是构建性能媲美甚至优于闭源模型的开源 AI 模型。 不过他此前也曾留下余地。"我们当然非常支持开源，但我并没有承诺会发布我们做的每一项成果。"他在去年的一档播客节目中这样说，"如果某个阶段 模型的能力发生了质的变化，而我们觉得开源它是不负责任的，那我们就不会开源。这一切都很难预测。" 扎克伯 ...

Core Viewpoint - The article highlights the significant achievements of Chinese authors in the field of computational linguistics, particularly focusing on the award-winning paper from DeepSeek that introduces a novel sparse attention mechanism for long-context modeling, showcasing its efficiency and performance improvements over traditional methods [1][17]. Group 1: Award and Recognition - The ACL announced that over 51% of the award-winning papers for 2025 had Chinese authors, with the USA at 14% [1]. - A paper by DeepSeek, led by author Liang Wenfeng, won the Best Paper award, which has generated considerable discussion [1]. Group 2: Technical Innovations - The paper introduces a Natively Trainable Sparse Attention (NSA) mechanism, which combines algorithmic innovation with hardware optimization for efficient long-context modeling [4][6]. - NSA employs a dynamic hierarchical sparse strategy that balances global context awareness with local precision through token compression and selection [11]. Group 3: Performance Evaluation - NSA demonstrated superior performance in various benchmarks, outperforming traditional full attention models in 7 out of 9 metrics, particularly in long-context tasks [8][10]. - In a "needle in a haystack" test with 64k context, NSA achieved perfect retrieval accuracy and significant speed improvements in decoding and training processes [9][15]. Group 4: Future Implications - The upcoming DeepSeek model is expected to incorporate NSA technology, generating anticipation for its release [17]. - There are speculations regarding the delay of DeepSeek R2's release, attributed to the founder's dissatisfaction with its current performance [17].

Core Viewpoint - The article discusses the competitive landscape of AI models, particularly focusing on DeepSeek and its challenges in maintaining user engagement and market position against emerging competitors like Kimi and others in the "AI Six Dragons" group. Group 1: DeepSeek's Performance and Challenges - DeepSeek experienced a significant decline in monthly active users, dropping from a peak of 169 million in January to a decrease of 5.1% by May [1][2]. - The download ranking of DeepSeek has plummeted, moving from the top of the App Store charts to outside the top 30 [2]. - The user engagement rate for DeepSeek has fallen from 7.5% at the beginning of the year to 3% by the end of May, with a 29% decrease in website traffic [2][3]. Group 2: Competition and Market Dynamics - Competitors like Kimi and others are rapidly releasing new models, with Kimi K2 achieving significant performance benchmarks and offering competitive pricing [1][8]. - The pricing strategy of Kimi K2 aligns closely with DeepSeek's API pricing, making it a direct competitor in terms of cost [8]. - Other players in the market are also emphasizing lower costs and better performance, which is eroding DeepSeek's previously established reputation for cost-effectiveness [7][8]. Group 3: Technological and Strategic Implications - DeepSeek's reliance on the H20 chip has been impacted by export restrictions, which has hindered its ability to scale and innovate [3][4]. - The lack of major updates to DeepSeek's models has led to a perception of stagnation, while competitors are rapidly iterating and improving their offerings [6][12]. - The article highlights the importance of multi-modal capabilities, which DeepSeek currently lacks, potentially limiting its appeal in a market that increasingly values such features [13]. Group 4: Future Outlook - To regain market interest, DeepSeek needs to expedite the release of new models like V4 and R2, as well as enhance its tool capabilities to meet developer needs [12][13]. - The competitive landscape is shifting rapidly, and without significant updates or innovations, DeepSeek risks losing further ground to its rivals [12][14]. - The article suggests that maintaining developer engagement and user interest is crucial for DeepSeek's long-term success in the evolving AI market [11].