为什么推荐你看看刘嘉老师的观点？ 刘嘉老师是清华大学心理与认知科学系主任、清华大学基础科学讲席教授、智源人工智能研究院首席科学家。 而且，和深度学习之父Geoffrey Hinton一样，刘嘉教授深耕心理学、脑科学与人工智能交汇领域。他在混沌多次授课，足 够底层，足够系统，跨学科的认知和精彩的表达，每次都给同学带来新的启发。 刘嘉老师说："当前，我们有幸在人生中经历这样一个范式转换的时刻，它不仅简单的是一个软件给你带来便利，而是让你 感受到文明的更迭，并且成为其中一部分的动力之所在。" 01 人类皇冠上的最后一颗明珠，我们人类最后的尊严就集中在这一区域，也就是我们称之为AGI所在的地方。它的展现其实 非常简单，可以跟朋友聊天、带家人去海边玩，开车四处游荡等等，这就是我们每个人每天做的事情，看上去平平无奇， 但它有两个非常了不起的特点。 第二阶段，以ChatGPT为代表的大语言模型 + Autonomous Agents，我们不仅可以问它问题，还能让它去执行任务； 第三阶段，以ChatGPT为代表的大语言模型+ Generative Agents，我们不再具体告诉它干什么，而是告诉它我们的目标。 03 第一个特 ...

Core Viewpoint - The article emphasizes the transformative potential of Artificial General Intelligence (AGI) and its implications for humanity, highlighting the evolution from traditional AI to more advanced autonomous and generative agents that can perform tasks and make decisions based on goals rather than explicit instructions [1][8][15]. AGI Evolution - The evolution of AGI is outlined in three stages: 1. The first stage involves large language models like ChatGPT that provide answers to questions [8]. 2. The second stage combines these models with Autonomous Agents that can execute tasks based on user queries [9]. 3. The third stage introduces Generative Agents that can autonomously determine actions based on given goals, representing a significant leap in AI capabilities [11][15]. Characteristics of Generative Agents - Generative Agents are described as intelligent entities with desires, beliefs, and intentions, capable of independent action [12]. - They must possess multiple skills, handle various situations, and interact authentically with the world, indicating a need for embodiment and real-world engagement [13][14]. Consciousness and Self-Identity - The emergence of self-identity ("I") among agents leads to a new level of intelligence, where agents can engage in complex interactions and exhibit consciousness [14][28]. - This development is seen as a precursor to a significant cognitive revolution, where AGI could surpass human intelligence [28][30]. Future Scenarios - Three potential futures are proposed regarding the relationship between humans and AGI: 1. Friendly Autonomous Agents that perform tasks without fatigue [32]. 2. A merger of human and machine, allowing for digital immortality [32]. 3. A scenario where AI could pose existential threats to humanity, akin to historical extinction events [32]. Call to Action - The article encourages engagement with the ongoing AI revolution, suggesting that individuals and organizations should prepare for the changes and opportunities presented by AGI [34][35].

We're dealing with magic here that we I think probably all would have thought was impossible 5 years ago or certainly 10 years ago. This is the most amazing technology ever and it's moving really fast and yet we're still like really disappointed. Like it's not moving fast enough and like it's like maybe right on the verge of stalling out. We should both be like hyper excited but also on the verge of like slitting our wrists cuz like you know the gravy train is coming to an end, >> right? >> It is faster but ...

Core Insights - OpenAI has launched its first AI browser, Atlas, aiming to redefine user interaction with the internet by placing AI at the core of the browsing experience [1][5][14] - Despite the innovative branding, Atlas shows limited differentiation from existing browsers like Comet and Opera Neon, lacking significant breakthroughs in design and functionality [1][2][4] Technical Implementation - AI browsers primarily utilize two technical paths: visual recognition and DOM parsing, with Atlas favoring the latter, achieving a task success rate of 89.1% and reducing costs by 90% [2] - Atlas's design closely resembles existing MCP browsers, with features like the "Ask ChatGPT" sidebar being similar to competitors' offerings [2][3] Feature Comparison - Atlas's split-screen browsing experience is not new, as Comet had already implemented this feature in 2024, allowing for simultaneous analysis of multiple tabs [3] - Atlas's agent mode requires user authorization for task execution, mirroring Opera Neon’s functionality but lacking features like reusable "Cards" for common tasks [3][4] Limitations and Challenges - Atlas's core agent functionality is only available to paid users, while competitors like Comet offer free access with usage limits [4] - Atlas currently supports only macOS, whereas Comet has broader platform support, including Windows and Linux [4] - Atlas does not support all Chrome extensions, limiting user experience for those reliant on specific tools [21][22] Market Context - The browser market is highly competitive, with Chrome maintaining a dominant position due to its extensive ecosystem and integration with Google services [21][22] - OpenAI's strategy to position Atlas as a primary internet gateway could enhance user engagement and create new revenue streams, particularly in advertising [15][26] Future Outlook - OpenAI aims to expand Atlas to additional platforms and enhance its agent mode functionality, viewing the browser as a key interface for AGI [14][15] - The emergence of AI browsers signifies a shift in internet interaction, moving from traditional search engines to AI-driven solutions that fulfill user tasks more proactively [26]

Core Insights - OpenAI has launched its first AI browser, Atlas, aiming to redefine user interaction with the internet by placing AI at the core of the browsing experience [1][2] - Atlas is positioned as a significant shift in OpenAI's identity, moving from being a provider of foundational AI tools to a more integrated user interface [2] Technical Implementation - Current AI browsers primarily utilize two technological paths: visual recognition and DOM parsing, with Atlas favoring the latter, achieving a task success rate of 89.1% and reducing costs by 90% [4][5] - Despite its technological foundation, Atlas shows little innovation compared to existing browsers like Comet and Opera Neon, with similar features and functionalities [3][5][6] Feature Comparison - Atlas offers content summarization and split-screen browsing, but these features are not unique and are available in competitors like Comet and Opera Neon [6][9] - Atlas's agent functionality requires user authorization for task execution, mirroring features found in Opera Neon, but lacks additional capabilities such as reusable "Cards" for common tasks [6][9] Security and Limitations - Atlas faces the same security challenges as other browsers, requiring manual intervention for sensitive operations like password entry and payment confirmations [7][16] - Technical issues, such as access blocks and operational bugs, indicate that Atlas still requires significant refinement [20][50] Market Position and Competition - OpenAI's strategy with Atlas aims to establish a new entry point for users into the internet, potentially increasing user engagement and monetization opportunities [28][29] - The competition in the AI browser space is not only technological but also revolves around ecosystem development, with the MCP protocol facilitating integration across various tools [31][33] Future Outlook - OpenAI's short-term goals include expanding Atlas to Windows, iOS, and Android platforms, enhancing agent functionality, and building a developer ecosystem for third-party AI applications [24][36] - The long-term vision for browsers like Atlas is to evolve into intelligent agents capable of understanding user intent and executing complex tasks seamlessly [56]

RT Ben Goodger (@bengoodger)When I joined @OpenAI last year all I had was an idea: that putting the world’s best AI assistant at the heart of your browsing experience would transform the way we get stuff done online.Since then we have been able to build an incredible team, and today we’re thrilled to release an all-new product: ChatGPT Atlas - a new web browser designed for the AI era - an era that will be shaped by more human natural language interaction, agents and ultimately AGI.Atlas has ChatGPT as its ...

Where are you on the relationship with China. >> I wish we had a better relationship with China. Uh, absolutely.I think um I think it's really unfortunate um that you know two of the most important two most important powers in the world are at loggerheads and I just think um you know we should we should make peace with China. I think that would be very very good. That would be uh that would be an incredible white swan.Let's put it that Let me ask you a slightly different question about maybe capitalism on a ...

Group 1 - Andrej Karpathy emphasizes that the maturity of AI agents will take another ten years, stating that current agents like Claude and Codex are not yet capable of being employed for tasks [2][4][5] - He critiques the current state of AI learning, arguing that reinforcement learning is inadequate and that true learning should resemble human cognitive processes, which involve reflection and growth rather than mere trial and error [11][12][22] - Karpathy suggests that future breakthroughs in AI will require a shift from knowledge accumulation to self-growth capabilities and a reconstruction of cognitive structures [4][5][22] Group 2 - The current limitations of large language models (LLMs) in coding tasks are highlighted, with Karpathy noting that they struggle with structured and nuanced engineering design [6][7][9] - He categorizes human interaction with code into three types, emphasizing that LLMs are not yet capable of functioning as true collaborators in software development [7][9][10] - Karpathy believes that while LLMs can assist in certain coding tasks, they are not yet capable of writing or improving their own code effectively [9][10][11] Group 3 - Karpathy discusses the importance of a reflective mechanism in AI learning, suggesting that models should learn to review and reflect on their processes rather than solely focusing on outcomes [18][19][20] - He introduces the concept of "cognitive core," advocating for models to retain essential thinking and planning abilities while discarding unnecessary knowledge [32][36] - The potential for a smaller, more efficient model with only a billion parameters is proposed, arguing that high-quality data can lead to effective cognitive capabilities without the need for massive models [34][36] Group 4 - Karpathy asserts that AGI (Artificial General Intelligence) will gradually integrate into the economy rather than causing a sudden disruption, focusing on digital knowledge work as its initial application area [38][39][40] - He predicts that the future of work will involve a collaborative structure where agents perform 80% of tasks under human supervision for the remaining 20% [40][41] - The deployment of AGI will be a gradual process, starting with structured tasks like programming and customer service before expanding to more complex roles [48][49][50] Group 5 - The challenges of achieving fully autonomous driving are discussed, with Karpathy stating that it is a high-stakes task that cannot afford errors, unlike other AI applications [59][60] - He emphasizes that the successful implementation of autonomous driving requires not just technological advancements but also a supportive societal framework [61][62] - The transition to widespread autonomous driving will be a slow and incremental process, beginning with specific use cases and gradually expanding [63]

Core Viewpoint - DeepSeek has introduced a groundbreaking model called DeepSeek-OCR, which utilizes a novel approach of "contextual optical compression" to efficiently process long texts by compressing textual information into visual tokens, significantly reducing computational costs while maintaining high accuracy in document parsing [5][13][14]. Summary by Sections Model Overview - DeepSeek-OCR is designed to tackle the computational challenges associated with processing long texts, achieving a high accuracy of 97% when the compression ratio is below 10 times, and maintaining around 60% accuracy even at a 20 times compression ratio [6][15]. - The model has gained significant attention, quickly accumulating 3.3K stars on GitHub and ranking second on HuggingFace's hot list [7]. Technical Innovations - The model comprises two core components: the DeepEncoder, which converts images into highly compressed visual tokens, and the DeepSeek3B-MoE-A570M decoder, which reconstructs text from these tokens [19][20]. - The DeepEncoder employs a serial design that processes high-resolution images in three stages: local feature extraction, token compression, and global understanding, allowing it to produce a minimal number of high-density visual tokens [21][22]. Performance Metrics - DeepSeek-OCR outperforms existing models by using only 100 visual tokens to exceed the performance of GOT-OCR2.0, which uses 256 tokens per page [18][19]. - The model supports various input modes, allowing it to adapt its compression strength based on specific tasks, ranging from "Tiny" (64 tokens) to "Gundam" (up to 800 tokens) [23][25]. Future Implications - The research suggests that the unified approach of visual and textual processing may be a pathway toward achieving Artificial General Intelligence (AGI) [11]. - The team has also proposed a concept of simulating human memory's forgetting mechanism through optical compression, potentially enabling models to allocate computational resources dynamically based on the context's temporal relevance [34][37][38].

Core Insights - Andrej Karpathy discusses the future of AGI and AI over the next decade, emphasizing that current "agents" are still in their early stages and require significant development [1][3][4] - He predicts that the core architecture of AI will likely remain similar to Transformer models, albeit with some evolution [8][10] Group 1: Current State of AI - Karpathy expresses skepticism about the notion of an "agent era," suggesting it should be termed "the decade of agents" as they still need about ten years of research to become truly functional [4][5] - He identifies key issues with current agents, including lack of intelligence, weak multimodal capabilities, and inability to operate computers autonomously [4][5] - The cognitive limitations of these agents stem from their inability to learn continuously, which Karpathy believes will take approximately ten years to address [5][6] Group 2: AI Architecture and Learning - Karpathy predicts that the fundamental architecture of AI will still be based on Transformer models in the next decade, although it may evolve [8][10] - He emphasizes the importance of algorithm, data, hardware, and software system advancements, stating that all are equally crucial for progress [12] - The best way to learn about AI, according to Karpathy, is through hands-on experience in building systems rather than theoretical approaches [12] Group 3: Limitations of Current Models - Karpathy critiques current large models for their fundamental cognitive limitations, noting that they often require manual coding rather than relying solely on AI assistance [13][18] - He categorizes coding approaches into three types: fully manual, manual with auto-completion, and fully AI-driven, with the latter being less effective for complex tasks [15][18] - The industry is moving too quickly, sometimes producing subpar results while pretending to achieve significant advancements [19] Group 4: Reinforcement Learning Challenges - Karpathy acknowledges that while reinforcement learning is not perfect, it remains the best solution compared to previous methods [22] - He highlights the challenges of reinforcement learning, including the complexity of problem-solving and the unreliability of evaluation models [23][24] - Future improvements may require higher-level "meta-learning" or synthetic data mechanisms, but no successful large-scale implementations exist yet [26] Group 5: Human vs. Machine Learning - Karpathy contrasts human learning, which involves reflection and integration of knowledge, with the current models that lack such processes [28][30] - He argues that true intelligence lies in understanding and generalization rather than mere memory retention [30] - The future of AI should focus on reducing mechanical memory and enhancing cognitive processes similar to human learning [30] Group 6: AI's Role in Society - Karpathy views AI as an extension of computation and believes that AGI will be capable of performing any economically valuable task [31] - He emphasizes the importance of AI complementing human work rather than replacing it, suggesting a collaborative approach [34][36] - The emergence of superintelligence is seen as a natural extension of societal automation, leading to a world where understanding and control may diminish [37][38]