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OpenAI联合创始人Greg Brockman:对话黄仁勋、预言GPT-6、我们正处在一个算法瓶颈回归的时代
AI科技大本营· 2025-08-13 09:53
Core Insights - The article emphasizes the importance of focusing on practical advancements in AI infrastructure rather than just the theoretical discussions surrounding AGI [1][3] - It highlights the duality of the tech world, contrasting the "nomadic" mindset that embraces innovation and speed with the "agricultural" mindset that values order and reliability in large-scale systems [3][5] Group 1: Greg Brockman's Journey - Greg Brockman's journey from a young programmer to a leader in AI infrastructure showcases the evolution of computing over 70 years [3][5] - His early experiences with programming were driven by a desire to create tangible solutions rather than abstract theories [9][10] - The transition from academia to industry, particularly his decision to join Stripe, reflects a commitment to practical problem-solving and innovation [11][12] Group 2: Engineering and Research - The relationship between engineering and research is crucial for the success of AI projects, with both disciplines needing to collaborate effectively [27][29] - OpenAI's approach emphasizes the equal importance of engineering and research, fostering a culture of collaboration [29][30] - The challenges faced in integrating engineering and research highlight the need for humility and understanding in team dynamics [34][35] Group 3: AI Infrastructure and Future Directions - The future of AI infrastructure requires a balance between high-performance computing and low-latency responses to meet diverse workload demands [45][46] - The development of specialized accelerators for different types of AI tasks is essential for optimizing performance [47][48] - The concept of "mixture of experts" models illustrates the industry's shift towards more efficient resource utilization in AI systems [48]
凯德北京投资基金管理有限公司:软银全力投入ai,能否再造一个奇迹?
Sou Hu Cai Jing· 2025-08-12 12:37
Group 1 - Masayoshi Son, founder of SoftBank, is making a significant bet to position SoftBank as a core player in the artificial intelligence (AI) sector, predicting the emergence of "super artificial intelligence" (ASI) within the next decade [1][3] - SoftBank's recent investments include a $32 billion acquisition of Arm in 2016, which has now reached a valuation of $145 billion, and a $6.5 billion acquisition of Ampere Computing, enhancing its AI hardware capabilities [3][5] - The company's AI strategy encompasses various dimensions, including semiconductors, software, infrastructure, robotics, and cloud services, aiming to create a deeply integrated AI ecosystem [3][5] Group 2 - Son's vision for AI dates back to 2010 with the concept of "brain-computer" systems, and although some early projects like the Pepper robot did not succeed, they laid the groundwork for SoftBank's current AI strategy [5] - The Vision Fund, established in 2017 with a $100 billion scale, faced controversies due to investments in companies like Uber and WeWork, but has since shifted its focus entirely to AI investments [5][7] - The competition in the AI field is intense, with both Chinese and American tech giants vying for dominance in "general artificial intelligence" (AGI), while emerging companies are challenging the notion of U.S. AI superiority [7]
从物竞天择到智能进化,首篇自进化智能体综述的ASI之路
机器之心· 2025-08-12 09:51
Core Insights - The article discusses the limitations of static large language models (LLMs) and introduces the concept of self-evolving agents as a new paradigm in artificial intelligence [2] - A comprehensive review has been published by researchers from Princeton University and other top institutions to establish a unified theoretical framework for self-evolving agents, aiming to pave the way for artificial general intelligence (AGI) and artificial superintelligence (ASI) [2][32] Definition and Framework - The review provides a formal definition of self-evolving agents, laying a mathematical foundation for research and discussion in the field [5] - It constructs a complete framework for analyzing and designing self-evolving agents based on four dimensions: What, When, How, and Where [8] What to Evolve? - The four core pillars for self-improvement within the agent system are identified: Models, Context, Tools, and Architecture [11] - Evolution can occur at two levels for models: optimizing decision policies and accumulating experience through interaction with the environment [13] - Context evolution involves dynamic management of memory and automated optimization of prompts [13] - Tools evolution includes the creation of new tools, mastery of existing tools, and efficient management of tool selection [13] - Architecture evolution can target both single-agent and multi-agent systems to optimize workflows and collaboration [14] When to Evolve? - Evolution timing determines the relationship between learning and task execution, categorized into two main modes: intra-test-time and inter-test-time self-evolution [17] How to Evolve? - Intra-test-time self-evolution occurs during task execution, allowing agents to adapt in real-time [20] - Inter-test-time self-evolution happens after task completion, where agents iterate on their capabilities based on accumulated experiences [20] - Evolution can be driven by various methodologies, including reward-based evolution, imitation learning, and population-based methods [21][22] Where to Evolve? - Self-evolving agents can evolve in general domains to enhance versatility or specialize in specific domains such as coding, GUI interaction, finance, medical applications, and education [25] Evaluation and Future Directions - The review emphasizes the need for dynamic evaluation metrics for self-evolving agents, focusing on adaptability, knowledge retention, generalization, efficiency, and safety [28] - Future challenges include developing personalized AI agents, enhancing generalization and cross-domain adaptability, ensuring safety and controllability, and exploring multi-agent ecosystems [32]
商汤林达华万字长文回答AGI:4层破壁,3大挑战
量子位· 2025-08-12 09:35
Core Viewpoint - The article emphasizes the significance of "multimodal intelligence" as a key trend in the development of large models, particularly highlighted during the WAIC 2025 conference, where SenseTime introduced its commercial-grade multimodal model, "Riri Xin 6.5" [1][2]. Group 1: Importance of Multimodal Intelligence - Multimodal intelligence is deemed essential for achieving Artificial General Intelligence (AGI) as it allows AI to interact with the world in a more human-like manner, processing various forms of information such as images, sounds, and text [7][8]. - The article discusses the limitations of traditional language models that rely solely on text data, arguing that true AGI requires the ability to understand and integrate multiple modalities [8]. Group 2: Technical Pathways to Multimodal Models - SenseTime has identified two primary technical pathways for developing multimodal models: Adapter-based Training and Native Training. The latter is preferred as it allows for a more integrated understanding of different modalities from the outset [11][12]. - The company has committed significant computational resources to establish a "native multimodal" approach, moving away from a dual-track system of language and image models [10][12]. Group 3: Evolutionary Path of Multimodal Intelligence - SenseTime outlines a "four-breakthrough" framework for the evolution of AI capabilities, which includes advancements in sequence modeling, multimodal understanding, multimodal reasoning, and interaction with the physical world [13][22]. - The introduction of "image-text intertwined reasoning" is a key innovation that allows models to generate and manipulate images during the reasoning process, enhancing their cognitive capabilities [16][18]. Group 4: Data Challenges and Solutions - The article highlights the challenges of acquiring high-quality image-text pairs for training multimodal models, noting that SenseTime has developed automated pipelines to generate these pairs at scale [26][27]. - SenseTime employs a rigorous "continuation validation" mechanism to ensure data quality, only allowing data that demonstrates performance improvement to be used in training [28][29]. Group 5: Model Architecture and Efficiency - The focus on efficiency over sheer size in model architecture is emphasized, with SenseTime optimizing its model to achieve over three times the efficiency while maintaining performance [38][39]. - The company believes that future model development will prioritize performance-cost ratios rather than simply increasing parameter sizes [39]. Group 6: Organizational and Strategic Insights - SenseTime's success is attributed to its strong technical foundation in computer vision, which has provided deep insights into the value of multimodal capabilities [40]. - The company has restructured its research organization to enhance resource allocation and foster innovation, ensuring a focus on high-impact projects [41]. Group 7: Long-term Vision and Integration of Technology and Business - The article concludes that the path to AGI is a long-term endeavor that requires a symbiotic relationship between technological ideals and commercial viability [42][43]. - SenseTime aims to create a virtuous cycle between foundational infrastructure, model development, and application, ensuring that real-world challenges inform research directions [43].
全球多模态推理新标杆 智谱视觉推理模型GLM-4.5V正式上线并开源
Zheng Quan Ri Bao Wang· 2025-08-12 08:46
Group 1 - Beijing Zhiyuan Huazhang Technology Co., Ltd. (Zhiyuan) launched the GLM-4.5V, a 100B-level open-source visual reasoning model with a total of 106 billion parameters and 12 billion active parameters [1][2] - GLM-4.5V is a significant step towards Artificial General Intelligence (AGI) and achieves state-of-the-art (SOTA) performance across 41 public visual multimodal benchmarks, covering tasks such as image, video, document understanding, and GUI agent functionalities [2][5] - The model features a "thinking mode" switch, allowing users to choose between quick responses and deep reasoning, balancing efficiency and effectiveness [5][6] Group 2 - GLM-4.5V is composed of a visual encoder, MLP adapter, and language decoder, supporting 64K multimodal long contexts and enhancing video processing efficiency through 3D convolution [6] - The model employs a three-stage strategy: pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL), which collectively enhance its capabilities in complex multimodal understanding and reasoning [6][7] - The pricing for API calls is set at 2 yuan per million tokens for input and 6 yuan per million tokens for output, providing a cost-effective solution for enterprises and developers [5]
马斯克,指责苹果“偏心”
Zheng Quan Shi Bao· 2025-08-12 04:59
马斯克,突然发声。 美国当地时间8月11日,特斯拉首席执行官埃隆·马斯克在社交平台发文称,苹果公司涉嫌通过限制措 施,使除美国开放人工智能研究中心(OpenAI )外的任何人工智能公司都无法在其应用商店排行榜中 登顶,称此为"明确的反垄断违规行为"。马斯克表示,其旗下xAI公司将立即采取法律行动。 显然,在人工智能道路上,xAI公司与OpenAI正处于激烈对抗中。 在马斯克威胁对苹果采取法律行动后,Sam Altman在X上转发了前者的帖文并表示:"我听说有人指控 马斯克通过操纵X来谋取个人及公司利益,并损害其竞争对手和他不喜欢的人的利益,这一指控令人震 惊。我希望有人能对此展开调查,我和许多人都想知道究竟发生了什么。OpenAI将专注于打造卓越的 产品。" 据央视报道,在截至8月11日美国地区的苹果应用商店内的生产力软件排行中,OpenAI的ChatGPT排第 一,xAI的Grok排第二。 早前,马斯克去年先后在州和联邦两级法院起诉OpenAI,指控后者违背非营利承诺,转向商业化路 线,并申请法庭阻止OpenAI转制。马斯克还多次公开批评Sam Altman。 xAI公司是马斯克于2023年创办的人工智能初 ...
智谱推出全球100B级最强开源多模态模型GLM-4.5V:获41个榜单SOTA
IPO早知道· 2025-08-12 01:52
Core Viewpoint - The article discusses the launch of GLM-4.5V, a state-of-the-art open-source visual reasoning model by Zhipu, which is a significant step towards achieving Artificial General Intelligence (AGI) [3][4]. Group 1: Model Overview - GLM-4.5V features a total of 106 billion parameters, with 12 billion activation parameters, and is designed for multi-modal reasoning, which is essential for AGI [3][4]. - The model builds on the previous GLM-4.1V-Thinking, showcasing enhanced performance across various visual tasks, including image, video, and document understanding [4][6]. Group 2: Performance Metrics - In 41 public multi-modal benchmarks, GLM-4.5V achieved state-of-the-art (SOTA) performance, outperforming other models in tasks such as general visual question answering (VQA) and visual grounding [5][6]. - Specific performance metrics include a general VQA score of 88.2 on MMBench v1.1 and 91.3 on RefCOCO-avg for visual grounding tasks [5]. Group 3: Technical Features - The model incorporates a visual encoder, MLP adapter, and language decoder, supporting 64K multi-modal long contexts and enhancing video processing efficiency through 3D convolution [6][8]. - It utilizes a three-stage training strategy: pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL), which collectively improve its multi-modal understanding and reasoning capabilities [8]. Group 4: Practical Applications - Zhipu has developed a desktop assistant application that leverages GLM-4.5V for real-time screen capture and processing various visual reasoning tasks, enhancing user interaction and productivity [8][9]. - The company aims to empower developers through model open-sourcing and API services, encouraging innovative applications of multi-modal models [9].
用时间积累换突破——月之暗面专注通用人工智能领域
Jing Ji Ri Bao· 2025-08-11 22:12
Core Insights - Moonshot AI, based in Beijing, is gaining attention for its open-source model Kimi K2, which ranked fifth globally upon its launch in July 2023 [1] - The company's mission is to explore the limits of intelligence and make AI universally accessible [1] Company Overview - Founded in April 2023 by a team with extensive experience in natural language processing (NLP), Moonshot AI aims to discover transformative possibilities in artificial intelligence [1] - The company has approximately 300 employees, with a significant portion being young talent from the '90s generation [2] Product Development - Kimi K2, a trillion-parameter model, has a unique capability to handle long texts, supporting up to 200,000 Chinese characters [2][5] - The Kimi intelligent assistant was launched in October 2023, followed by several product releases, including Kimi browser assistant and Kimi-Researcher [2] Technical Innovations - Kimi K2's architecture allows for complex tasks at a lower cost, with only 32 billion active parameters [3] - The model has excelled in various benchmarks, particularly in programming, tool usage, and mathematical reasoning [6] User Engagement - Kimi K2's long-text capability has led to a significant increase in user adoption, with user numbers growing from hundreds of thousands to tens of millions in 2024 [5] - The model is designed to be user-friendly, allowing non-programmers to utilize its capabilities effectively [7] Future Aspirations - Moonshot AI aims to create a general-purpose AI that surpasses human intelligence, focusing on developing versatile skills that can enhance each other [8] - The company emphasizes the importance of building a strong foundational model before releasing products, ensuring robust performance and capabilities [8]
智谱宣布开源视觉推理模型GLM-4.5V正式上线并开源
Feng Huang Wang· 2025-08-11 14:14
GLM-4.5V基于智谱新一代旗舰文本基座模型GLM-4.5-Air,延续GLM-4.1V-Thinking 技术路线,在41个 公开视觉多模态榜单中综合效果达到同级别开源模型SOTA性能,涵盖图像、视频、文档理解以及GUI Agent等常见任务。比如,GLM-4.5V能够根据用户提问,精准识别、分析、定位目标物体并输出其坐标 框。 据介绍,多模态推理被视为通向通用人工智能(AGI)的关键能力之一,让AI能够像人类一样综合感 知、理解与决策。其中,视觉-语言模型(Vision-Language Model, VLM)是实现多模态推理的核心基 础。 凤凰网科技讯 8月11日,智谱AI推出全球100B级效果最佳的开源视觉推理模型 GLM-4.5V(总参数 106B,激活参数 12B),并同步在魔搭社区与Hugging Face开源。此外,API调用价格低至输入2元/M tokens,输出6元/M tokens。 ...
AI真能让企业脱胎换骨?混沌AI院产品升级重磅发布
混沌学园· 2025-08-11 12:04
Core Viewpoint - The article discusses the emergence of "AI Business Studies" as a response to the confusion faced by business leaders regarding how to effectively translate AI technology into commercial value, especially in the context of rapid advancements in AI technologies like ChatGPT5 [1][6][11]. Summary by Sections AI Business Studies Introduction - "AI Business Studies" aims to transform AI from a novelty into a practical tool that addresses cost, efficiency, and growth challenges in business [6][7]. - The concept arises from a deep understanding of real business needs, emphasizing actionable methodologies over abstract technological concepts [9]. Transition from Technology to Business Necessity - The AI sector has moved past mere technological showcases and is now focused on practical applications across various industries [6]. - Despite the proliferation of AI tools, many businesses and individuals have yet to fully realize the benefits of AI [6]. GPT5 Release Insights - The release of ChatGPT5 marks significant technological advancements, including improved problem-solving capabilities and a reduction in factual inaccuracies [14][20]. - GPT5's ability to automatically switch between models based on user needs enhances efficiency by at least 40% [19]. - The model's programming capabilities allow for rapid development of applications, significantly reducing development time and costs [20][21]. AGI and Business Opportunities - The article discusses the concept of General Artificial Intelligence (AGI) and its relevance to business, emphasizing that AGI should be measured by its performance in real-world job scenarios rather than theoretical benchmarks [26][27]. - AGI is viewed as a collective of specialized AI systems rather than a single omnipotent entity [30]. Barriers to AI Implementation in Enterprises - Companies face three main barriers to AI adoption: lack of awareness among leaders, absence of practical methodologies, and a shortage of skilled personnel to execute AI projects [33][40]. - The article outlines a three-step method for AI implementation, focusing on breaking down business goals into actionable tasks, matching AI tools to these tasks, and establishing performance metrics [38]. Chaotic AI Institute's Approach - The Chaotic AI Institute's second phase emphasizes a structured approach to AI education, focusing on building a comprehensive framework that spans various business functions and roles [45][46]. - The institute promotes a team-based model for AI project execution, enhancing the likelihood of successful implementation [51]. Community and Resource Sharing - The article highlights the importance of community and resource sharing in accelerating AI adoption, with the Chaotic AI Institute fostering a network of over 500 companies for collaboration and innovation [58][60]. Course Structure and Learning Outcomes - The six-month course at the Chaotic AI Institute is designed to guide participants through a structured learning process, ensuring they leave with actionable AI projects and methodologies [63][67].