Transformer架构

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一文了解DeepSeek和OpenAI:企业家为什么需要认知型创新?
混沌学园· 2025-06-10 11:07
Core Viewpoint - The article emphasizes the transformative impact of AI technology on business innovation and the necessity for companies to adapt their strategies to remain competitive in the evolving landscape of AI [1][2]. Group 1: OpenAI's Emergence - OpenAI was founded in 2015 by Elon Musk and Sam Altman with the mission to counteract the monopolistic power of major tech companies in AI, aiming for an open and safe AI for all [9][10][12]. - The introduction of the Transformer architecture by Google in 2017 revolutionized language processing, enabling models to understand context better and significantly improving training speed [13][15]. - OpenAI's belief in the Scaling Law led to unprecedented investments in AI, resulting in the development of groundbreaking language models that exhibit emergent capabilities [17][19]. Group 2: ChatGPT and Human-Machine Interaction - The launch of ChatGPT marked a significant shift in human-machine interaction, allowing users to communicate in natural language rather than through complex commands, thus lowering the barrier to AI usage [22][24]. - ChatGPT's success not only established a user base for future AI applications but also reshaped perceptions of human-AI collaboration, showcasing vast potential for future developments [25]. Group 3: DeepSeek's Strategic Approach - DeepSeek adopted a "Limited Scaling Law" strategy, focusing on maximizing efficiency and performance with limited resources, contrasting with the resource-heavy approaches of larger AI firms [32][34]. - The company achieved high performance at low costs through innovative model architecture and training methods, emphasizing quality data selection and algorithm efficiency [36][38]. - DeepSeek's R1 model, released in January 2025, demonstrated advanced reasoning capabilities without human feedback, marking a significant advancement in AI technology [45][48]. Group 4: Organizational Innovation in AI - DeepSeek's organizational model promotes an AI Lab paradigm that fosters emergent innovation, allowing for open collaboration and resource sharing among researchers [54][56]. - The dynamic team structure and self-organizing management style encourage creativity and rapid iteration, essential for success in the unpredictable field of AI [58][62]. - The company's approach challenges traditional hierarchical models, advocating for a culture that empowers individuals to explore and innovate freely [64][70]. Group 5: Breaking the "Thought Stamp" - DeepSeek's achievements highlight a shift in mindset among Chinese entrepreneurs, demonstrating that original foundational research in AI is possible within China [75][78]. - The article calls for a departure from the belief that Chinese companies should only focus on application and commercialization, urging a commitment to long-term foundational research and innovation [80][82].
大模型专题:大模型架构创新研究报告
Sou Hu Cai Jing· 2025-06-06 11:38
Core Insights - The report focuses on innovations in large model architectures, particularly addressing the limitations of the Transformer architecture and exploring industry pathways for improvement [1][2][7] - As model sizes increase, the secondary computational complexity of Transformers (O(n²)) leads to significant power consumption and efficiency bottlenecks in processing long sequences, prompting a demand for innovative solutions [1][2][15] - The industry is currently exploring two main paths for architectural breakthroughs: improvements to the Transformer architecture and exploration of non-Transformer architectures [1][2][7] Transformer Architecture Improvements - Improvements to the Transformer architecture focus on optimizing the Attention mechanism, Feed-Forward Network (FFN) layers, and normalization layers [1][2][18] - Techniques such as sparse attention and dynamic attention are being developed to enhance computational efficiency, while Mixture of Experts (MoE) aims to improve sparse connection efficiency in FFN layers [1][2][18] - LongRoPE and other technologies are enhancing positional encoding to better model long sequences [1][2][18] Non-Transformer Architecture Exploration - Non-Transformer architectures include new types of RNNs (e.g., RWKV, Mamba) and CNNs (e.g., Hyena Hierarchy), as well as other innovative architectures like RetNet and LFM [1][2][7] - RWKV optimizes state evolution through a generalized Delta Rule, while Mamba leverages state space models to enhance training efficiency [1][2][7] - RetNet combines state space and multi-head attention to achieve parallel computation [1][2][7] Industry Trends and Future Directions - The industry is witnessing a trend towards hybrid architectures that combine linear Transformers with non-Transformer architectures, balancing performance and efficiency [2][7] - The current phase is characterized by a peak in traditional Transformer paradigms and an impending wave of architectural innovations, with significant focus on new RNN/CNN theoretical breakthroughs and practical engineering optimizations [2][7] - Companies like ByteDance and Alibaba are accelerating their investments in hybrid architectures, driving the evolution of large models towards higher efficiency and lower energy consumption [2][7]
三位顶流AI技术人罕见同台,谈了谈AI行业最大的「罗生门」
3 6 Ke· 2025-05-28 11:59
Core Insights - The AI industry is currently experiencing a significant debate over the effectiveness of pre-training models versus first principles, with notable figures like Ilya from OpenAI suggesting that pre-training has reached its limits [1][2] - The shift from a consensus-driven approach to exploring non-consensus methods is evident, as companies and researchers seek innovative solutions in AI [6][7] Group 1: Industry Trends - The AI landscape is witnessing a transition from a focus on pre-training to exploring alternative methodologies, with companies like Sand.AI and NLP LAB leading the charge in applying multi-modal architectures to language and video models [3][4] - The emergence of new models, such as Dream 7B, demonstrates the potential of applying diffusion models to language tasks, outperforming larger models like DeepSeek V3 [3][4] - The consensus around pre-training is being challenged, with some experts arguing that it is not yet over, as there remains untapped data that could enhance model performance [38][39] Group 2: Company Perspectives - Ant Group's Qwen team, led by Lin Junyang, has faced criticism for being conservative, yet they emphasize that their extensive experimentation has led to valuable insights, ultimately reaffirming the effectiveness of the Transformer architecture [5][15] - The exploration of Mixture of Experts (MoE) models is ongoing, with the team recognizing the potential for scalability while also addressing the challenges of training stability [16][20] - The industry is increasingly focused on optimizing model efficiency and effectiveness, with a particular interest in achieving a balance between model size and performance [19][22] Group 3: Technical Innovations - The integration of different model architectures, such as using diffusion models for language generation, reflects a broader trend of innovation in AI [3][4] - The challenges of training models with long sequences and the need for effective optimization strategies are critical areas of focus for researchers [21][22] - The potential for future breakthroughs lies in leveraging increased computational power to revisit previously unviable techniques, suggesting a cycle of innovation driven by advancements in hardware [40][41]
自动驾驶未来技术趋势怎样?李想:现阶段VLA是能力最强的架构
news flash· 2025-05-07 13:27
Core Viewpoint - The CEO of Li Auto, Li Xiang, discussed the transition of the auxiliary driving system to the VLA architecture, questioning its efficiency compared to potential future architectures [1] Group 1 - VLA architecture is capable of addressing full autonomous driving, but its efficiency as the optimal solution is uncertain [1] - Li Xiang highlighted that VLA is still based on the transformer architecture, which raises questions about whether transformer is the most efficient architecture available [1] - Currently, VLA is considered the most powerful architecture in terms of capabilities [1]
深度|对话Cerebras CEO:3-5年后我们对Transformer依赖程度将降低,英伟达市占率将降至50-60%
Z Potentials· 2025-04-06 04:55
图片来源: 20VC with Harry Stebbings Z Highlights Andrew Feldman 是 Cerebras 的联合创始人兼首席执行官, Cerebras 是世界上最快的人工智能推理 + 训练平台。本次访谈为他和 20VC 主播 Harry Stebbings 探讨 AI 时代改变芯片构造需求以及行业趋势。 AI 对芯片需求的改变 Harry : 见到你真是太高兴了。我期待这次对话很久了。 Eric 经常向我提起你,一直对你赞不绝口,非常感谢你能接受我的访谈。 Andrew : Harry ,谢谢邀请。很荣幸能参与这个对话。 Harry : 这一定会是场精彩的对话,感觉今天能跟你学到很多。让我们回到 2015 年,当时你和团队在 AI 领域看到了什么机遇,促使你们创立了 Cerebras 公司? Andrew : 我们看到了一种新兴工作负载的崛起 —— 这对计算机架构师而言堪称梦想成真。我们发现了一个值得解决的新问题,这意味着或许可以为此打 造更适配的硬件系统。 2015 年时,我的联合创始人 Gary 、 Sean 、 JP 和 Michael 率先预见了 AI 的兴起。这预 ...
湖南95后女博士,力挑谷歌,要造思考时"不发烧"的AI
创业邦· 2025-03-19 09:28
Core Viewpoint - Lu Xi Technology aims to challenge the dominance of the Transformer architecture in AI by developing a brain-like computing ecosystem, introducing the NLM model that significantly reduces energy consumption while enhancing inference efficiency [2][3][4]. Group 1: Company Overview - Lu Xi Technology was founded in 2023 by two women born in the 1990s, marking it as the first domestic company focused on brain-like computing [2]. - The NLM model, launched in 2024, is the first domestically developed large model using a non-Transformer architecture based on brain-like technology [2][12]. - The company has received approval from the National Internet Information Office for its generative AI services and deep synthesis algorithm services [2][12]. Group 2: Technology and Innovation - The NLM model boasts a reduction in energy consumption by over 80% while improving inference efficiency several times compared to traditional models [12][13]. - Lu Xi Technology's brain-like architecture mimics the human brain's neural structure, allowing for efficient computation and storage by activating only relevant neurons [4][12]. - The company is developing a range of products based on the NEURARK brain-like architecture, including foundational models and industry-specific models, to meet diverse market needs [12][15]. Group 3: Market Position and Strategy - Lu Xi Technology aims to break the dependency on NVIDIA chips by developing its own FPGA and ASIC chips tailored for large models [10][12]. - The company collaborates with various state-owned enterprises and industry leaders to deploy its models across multiple sectors, including healthcare and disaster management [15]. - The company is targeting a significant increase in model parameter scale, aiming to reach 600 billion parameters by 2025, which would bring it closer to the complexity of the human brain [16].