Core Viewpoint - The article introduces the concept of "Deep Cognition," a platform designed to enhance cognitive accumulation through interactive AI, transforming the way users engage with knowledge and insights [1][19][60]. Group 1: User Engagement and Cognitive Accumulation - Users often collect articles and papers but rarely revisit them, with statistics showing low revisit rates: an average of 547 articles collected on Zhihu with a 3.2% revisit rate, 284 papers for graduate students with a 12% deep reading rate, and 1,203 items on Xiaohongshu with a 1.8% secondary browsing rate [4][5]. - The platform aims to change this by allowing users to accumulate cognitive assets with each interaction, where every collection contributes to the AI's learning and understanding [7][11]. Group 2: Features of the Deep Cognition Platform - The platform offers features such as cognitive rankings and weekly summaries, showcasing popular cognitive topics and community learning dynamics [12]. - It includes personalized subscription and sharing options, allowing users to tailor their cognitive experience [15]. - The cognitive synthesis feature merges diverse viewpoints to create deeper understanding and insights [33]. Group 3: Technical Foundations and Innovations - The underlying technology is based on the principle of "Interaction as Intelligence," emphasizing the collaborative relationship between humans and AI [23][24]. - The platform's cognitive card generation engine transforms complex research outcomes into structured, visual insights, making them easier to understand [33]. - The cognitive accumulation mechanism uses user behavior data to drive personalized recommendations, ensuring that each learning experience builds on existing knowledge [33]. Group 4: Performance and User Experience - Experiments demonstrate that the introduction of interactive features significantly enhances the quality of reports generated by the system, with an average quality improvement of 63% compared to non-interactive versions [34][39]. - The system outperforms leading commercial deep research systems in user experience metrics, particularly in transparency and fine-grained interaction [36][42]. - The collaborative model shows that expert users achieve a 72.73% accuracy rate when interacting with the system, compared to much lower rates for non-expert users and autonomous AI systems [44][46]. Group 5: Future Implications - The platform signifies a shift from viewing AI as merely an efficiency tool to recognizing it as a cognitive partner, redefining human-AI collaboration [19][60]. - The findings suggest that effective human-AI collaboration requires a flexible control mechanism, allowing users to switch between hands-on and hands-off approaches based on task demands [50][57].

明敏 整理自 凹非寺 量子位 | 公众号 QbitAI 大神Karpathy提出"软件3.0"才两周，"软件3.5"已经诞生了？ 交互即智能。 指AI不再是黑盒工具，而是透明的思维伙伴。用户可以在AI思考的任何节点进行干预，提供战略指导或纠正方向。 也就是说，智能是在人类与AI的不断交互合作中涌现。 Software 3.0作为一个概念，在2024年9月之后已经显得有些过时了 。为什么这么说？ Software 3.0的核心困境源于它诞生时的技术背景。2022年ChatGPT发布时，AI的主要能力还集中在文本生成和简单推理，"自然语言编 程"确实是那个时代的最佳解决方案。但2024年9月之后，我们见证了AI能力的代际跃迁：从GPT-4的生成能力到o1系列的深度推理，从简单 的指令执行到具备元认知意识的思考能力。 最关键的变化是，人类首次能够与AI进行真正的思维层面交流 ——AI不仅理解我们说什么，更能理解我们为什么这么说，甚至能主动寻求认 知层面的协作。这种质的飞跃让传统的"输入prompt→等待处理→接收结果"模式显得笨拙而低效，就像用电报方式进行现代通信一样不合时 宜。 他们认为，2024年9月之后，随着 ...