Core Insights - The 2025 Inclusion Bund Conference in Shanghai focuses on the integration of AI into daily life and work, shifting the discussion from "what can we do with large models" to "how should AI coexist with us" [1] - The conference features prominent thinkers and innovators discussing the future of AI, emphasizing collaboration, innovation, and the need for a new understanding of progress in technology [2] Group 1: AI Paradigm Shift - Richard Sutton, the 2024 Turing Award winner, introduces the concept of the "Experience Era," suggesting that true intelligence lies in the ability to learn quickly rather than just possessing existing knowledge [3] - Sutton's perspective indicates a shift from reliance on static human data to a model that emphasizes experiential learning, which could redefine AI's future development [3] Group 2: Open Resources and Infrastructure - Wang Jian, founder of Alibaba Cloud, highlights the importance of "open resources" in AI development, suggesting that the future of AI will involve sharing data and computational resources to avoid redundant consumption of power [4] - Zhang Hongjiang from Source Code Capital discusses the "scaling law" of large models, predicting a shift towards "agent swarm" systems where models and GPU power become core organizational assets [4] Group 3: Nature of Intelligence - Ma Yi from the University of Hong Kong critiques current large models for lacking true intelligence, advocating for a shift towards AI systems that can self-verify and self-correct [5] - Sun Xuan from the University of Science and Technology of China emphasizes the energy demands of AI, suggesting that nuclear fusion may be the ultimate solution to meet these needs [6] Group 4: Historical Perspective and Ethical Considerations - Yuval Noah Harari warns that speed alone does not equate to progress, stressing the need for societal structures to evolve alongside technological advancements [7] - Harari outlines three lessons for safeguarding AI progress: fostering global cooperation, establishing corrective feedback loops, and preserving human storytelling capabilities [7] Group 5: Industry Practices and Challenges - The conference features discussions on how to translate AI capabilities into tangible business value, with insights from leaders at Ant Group and Xiaomi on their respective strategies [10][11] - Ant Group focuses on specialized services in high-stakes industries like finance and healthcare, while Xiaomi aims to integrate AI into wearable devices to enhance user retention [10][11] Group 6: Global Collaboration and Innovation Ecosystem - The conference marks a historic gathering of major fintech events from Singapore and Hong Kong, underscoring the theme of collaboration in the tech space [12] - Shanghai is positioning itself as a hub for innovation and global dialogue, emphasizing the importance of collaboration and co-creation in the AI landscape [12][13]

Group 1 - The 2025 Inclusion Bund Conference opened in Shanghai, featuring global innovators including Turing Award winner Richard Sutton and Yuval Noah Harari [2][3] - The focus of the conference has shifted from "what can we do with large models" to "how should AI coexist with us and prosper together" [3] - The conference serves as a platform for sharing insights on the future of AI, emphasizing collaboration and innovation in the smart era [3][14] Group 2 - Richard Sutton introduced the concept of the "Experience Era," suggesting that true intelligence lies in the ability to learn quickly rather than just possessing existing knowledge [5] - Wang Jian highlighted the importance of "open resources" in AI development, envisioning a future where AI accompanies humanity into space [6] - Zhang Hongjiang discussed the macro landscape of AI, predicting a shift towards "agent swarm" models and the significance of GPU computing as core assets [6] Group 3 - The conference featured discussions on the essence of intelligence, with calls for AI research to move from a "black box" approach to a "white box" model based on mathematical principles [7] - Sun Xuan emphasized the energy demands of AI, linking its future to advancements in nuclear fusion technology [7] - Yuval Noah Harari provided a historical perspective, stressing the need for global cooperation and the establishment of systems to manage AI's impact on society [8] Group 4 - The conference included roundtable discussions on the practical application of AI, focusing on how to translate AI capabilities into commercial value [13] - Ant Group's CEO highlighted the importance of specialized services in high-stakes industries, while Xiaomi aims to integrate AI into wearable devices for user retention [13] - The discussions underscored the need for a redefined user interaction with AI, whether through comprehensive apps or intelligent hardware [13] Group 5 - The event marked a historic gathering of major fintech events from Singapore and Hong Kong, emphasizing the theme of collaboration [15] - Shanghai aims to position itself as a global connector in technology, fostering an open and diverse innovation ecosystem [15][16] - The conference highlighted a new consensus that the true competitive advantage in AI lies in its ability to learn and adapt to complex real-world scenarios [16]

Group 1: Conference Overview - The 2025 Inclusion Bund Conference opened on September 11, featuring global innovators including Turing Award winner Richard Sutton and historian Yuval Noah Harari [1][2] - The focus of the conference shifted from "what can we do with large models" to "how should AI coexist with us and prosper together" [1][2] - The conference serves as a platform for sharing insights on the future of AI and its integration into various industries [1][2] Group 2: Key Insights from Speakers - Richard Sutton announced the end of the "Human Data Era" and introduced the concept of the "Experience Era," emphasizing the importance of learning new knowledge quickly rather than relying on existing knowledge [3][4] - Wang Jian highlighted the shift from "code open source" to "resource open," advocating for the opening of data and computational resources to drive AI development [4] - Zhang Hongjiang discussed the "scaling law" of large models and predicted the emergence of the "agent swarm" era, where models and GPU computing power become core organizational assets [4] Group 3: Challenges and Opportunities in AI - The conference featured discussions on how to define "progress" in the context of rapid technological advancement, with a focus on collaboration and the need for a supportive societal framework [2][6] - The second roundtable addressed the challenge of translating AI capabilities into tangible business value, emphasizing the importance of organizational culture in successful AI transformation [9][10] - The dialogue included perspectives from various sectors, such as finance and healthcare, on how to leverage AI for specialized service value and user retention [9][10] Group 4: Future Directions and Global Collaboration - The conference underscored the need for global cooperation in AI development, advocating for a verified global commitment rather than a race for speed [6][10] - Shanghai aims to position itself as a hub for innovation and collaboration in the global technology landscape, fostering an open and diverse innovation ecosystem [10][11] - The emergence of a new consensus around AI's role in society suggests a shift towards a more rational "deep cultivation" phase, focusing on continuous learning and adaptation [10][11]

Group 1: AI and Innovation - The 2025 Inclusion·Bund Conference in Shanghai focused on "Reshaping Innovation Growth," featuring discussions on AI as a key theme, with over 40 forums and a significant technology exhibition [1] - Richard Sutton, the 2024 Turing Award winner, emphasized that humanity is entering a new "Era of Experience," where AI's replacement is inevitable, and the data era is nearing its end [3][4] - Sutton highlighted that the core of intelligence lies in experience, which involves observation, action, and reward, and pointed out the need for continual learning and meta-learning technologies to unlock AI's full potential [3] Group 2: Industry Perspectives - Wang Jian, founder of Alibaba Cloud, stated that open data and computing resources are essential for advancing AI, marking a shift from code open-sourcing to resource sharing [5][6] - Wang also introduced the concept of "computing satellites," which will leverage AI in space exploration, indicating a new frontier for AI applications beyond traditional devices [6] - Wang Xingxing, CEO of Yushu Technology, expressed optimism about the AI era, noting that small organizations will increasingly have explosive growth potential, despite existing challenges in data quality and model algorithms [7][8] Group 3: Organizational Challenges - McKinsey's China Chairman, Li Yili, identified organizational culture as the biggest bottleneck in AI development, advocating for CEO-led transformations focused on profitability rather than just application scenarios [8][9] - Li outlined three stages of globalization for Chinese enterprises, emphasizing the need for a global perspective and diverse collaboration models to enhance growth opportunities [10] Group 4: Energy and AI - Professor Sun Xuan from the University of Science and Technology of China proposed that nuclear fusion is the key to meeting the energy demands of AI, with 1 gram of fusion fuel equating to the energy of 8 tons of oil [11][12] - Sun highlighted the significant energy gap that AI could create, predicting that AI's energy consumption could exceed 20% of the Earth's total energy supply in the future [11] - The fusion industry is seeing increased investment, with a total of $7.1 billion raised globally, indicating a growing interest in commercializing fusion technology [12]

Core Insights - Artificial intelligence is entering an "experience era," where continuous learning will be central to its development, surpassing previous capabilities [2] - The expansion of infrastructure is facilitating the industrial scaling of AI, leading to a new "agent economy" characterized by interactions among numerous intelligent agents [3] - The rise of AI is significantly increasing global energy consumption, necessitating advancements in nuclear fusion as a sustainable energy source for future AI technologies [4] Group 1: AI Development and Learning - Richard Sutton, the Turing Award winner, emphasizes that the current machine learning methods are reaching their limits in transferring human knowledge, necessitating a new data source generated through direct interaction with the environment [2] - Sutton argues that fears surrounding AI, such as bias and job loss, are exaggerated, and that decentralized collaboration will drive human prosperity alongside AI [2] Group 2: Infrastructure and Economic Transformation - Zhang Hongjiang highlights the ongoing relevance of the "scaling law" for large models, indicating that the interaction among intelligent agents will profoundly reshape economic structures [3] - The concept of an "agent economy" is introduced, where organizations will need to enhance computational power and data richness to leverage the capabilities of intelligent agents [3] Group 3: Energy Consumption and Nuclear Fusion - Sun Xuan points out that AI currently consumes 1.5% of the Earth's electricity, with projections suggesting it could rise to over 20%, creating a significant energy gap [4] - Nuclear fusion is presented as a solution to meet the future energy demands of AI, with its high energy density being a key advantage [4] - Despite the challenges in achieving nuclear fusion, advancements in AI technology are seen as pivotal in moving towards commercial viability in this field [4]

Group 1 - The core theme of the articles revolves around the evolving relationship between AI and humanity, emphasizing the need for collaboration and the ethical implications of AI's integration into society [1][2][20] - Richard Sutton, a prominent figure in AI, argues that fears surrounding AI, such as job loss and existential threats, are often exaggerated and driven by vested interests [2][20] - The development of AI is seen as a pathway to human prosperity through decentralized collaboration, with a focus on the importance of cooperation in achieving meaningful advancements [2][18] Group 2 - Current AI capabilities are still in their infancy, with significant challenges remaining in making AI truly functional in practical applications [4][6] - The integration of AI with robotics is creating a new industry focused on embodied intelligence, which aims to enable robots to perform tasks autonomously [4][6] - The potential for AI to revolutionize industries is acknowledged, but experts caution that the technology is not yet ready for widespread deployment [4][6] Group 3 - The future of AI is closely tied to advancements in energy solutions, particularly nuclear fusion, which is viewed as a critical technology for sustainable development [13][15] - Investment in nuclear fusion has surged, with significant funding from major tech companies, indicating a growing consensus on its importance for future energy needs [13][15] - The challenges of achieving controlled nuclear fusion are acknowledged, with ongoing research focused on overcoming technical hurdles [13][15] Group 4 - The AI industry is transitioning from a focus on human data to an "experience era," where AI learns from direct interactions with the environment [17][20] - Sutton emphasizes the need for new data sources and learning methods to unlock AI's full potential, highlighting the importance of continual and meta-learning [17][20] - The concept of "intelligent agents" is emerging as a dominant application of AI, suggesting a shift towards an "agent economy" that could transform organizational structures [20] Group 5 - China's AI development strategy outlines clear goals for the integration of AI across various sectors by 2027, with a focus on widespread adoption and economic growth [21][20] - Shanghai is positioning itself as a hub for AI innovation, with significant financial support for companies in the AI space, including funding for computational resources and model development [21][20] - The articles suggest that the AI landscape is becoming increasingly defined, with a roadmap for future advancements and applications [20][21]

Group 1 - The core theme of the 2025 Inclusion Bund Conference is the exploration of AI's integration into daily life and its coexistence with humanity, shifting from "what can we do with large models" to "how should AI coexist with us" [1][3] - The conference features prominent figures in AI and technology, including Turing Award winner Richard Sutton and historian Yuval Noah Harari, discussing the future of AI and its implications for society [1][3][19] - The event showcases a 10,000 square meter technology exhibition and a 5,000 square meter tech market, highlighting practical applications of AI, such as humanoid robots and AI health assistants [3][19] Group 2 - Richard Sutton introduces the concept of the "Experience Era," suggesting that true intelligence lies in the ability to learn quickly from new experiences rather than relying solely on existing knowledge [4][5] - Wang Jian emphasizes the importance of "open resources" in AI development, advocating for the sharing of data and computational resources to foster innovation [7] - Zhang Hongjiang discusses the "scaling law" in AI, predicting a shift towards "agent swarm" models where AI and GPU capabilities become core organizational assets [9] Group 3 - Yuval Noah Harari warns that technological speed does not equate to progress, stressing the need for societal structures to adapt to new technologies [11][12] - The conference includes discussions on the practical challenges of AI implementation, focusing on how to translate AI capabilities into tangible business value [16][18] - The collaboration between major Asian fintech events at the conference signifies a growing trend towards global cooperation in technology [19][20] Group 4 - The conference reflects a new consensus that the true competitive advantage in AI lies in its ability to continuously learn and adapt to complex real-world scenarios [20][21] - Shanghai aims to position itself as a global hub for innovation, facilitating collaboration and knowledge exchange in the tech industry [19][21]

Core Insights - The 2025 Inclusion Bund Conference in Shanghai focused on the development path of artificial intelligence (AI), discussing its current status, challenges, and future vision [1] AI Development - AI is transitioning from a data-driven paradigm to an experience-driven one, as proposed by Turing Award winner Richard Sutton, indicating a new phase in AI development [2] - The "scale law" continues to dominate AI development, with the emergence of reasoning models shaping a new curve called the "reasoning scale law" [4] - Major U.S. tech companies are expected to spend over $300 billion on AI-related capital expenditures by 2025, indicating a large-scale construction boom in the AI data center industry [4] - The concept of an "intelligent agent economy" is emerging, where numerous intelligent agents interact, execute tasks, and exchange data [4] - Open resources are becoming a key variable in AI competition, with a shift from code openness to resource openness [4][7] AI Challenges - Energy demand is a hard constraint for AI development, with AI currently consuming 1.5% of global electricity, potentially rising to 20% [5] - There is a significant gap in the practical application of AI, with challenges in high-quality data availability and model alignment with robotic control modalities [6] - Ethical and social governance challenges are increasingly prominent, with concerns about decision-making being transferred from humans to algorithms [6] - Organizational management needs to be restructured to adapt to the rapid development of AI technology [6] AI Future - The ultimate goal of AI is linked to energy, with nuclear fusion being highlighted as a breakthrough opportunity [8] - Continuous learning and meta-learning technologies are essential for unlocking the full potential of AI [8] - Collaboration and empathy are crucial for measuring progress in a rapidly evolving technological society [8] - The launch of 12 satellites with an 8B AI model marks a significant opportunity for AI in space [8][9] - The future of AI will require a collaborative approach involving technological breakthroughs, energy support, ethical norms, and social governance [10]

Group 1 - The core viewpoint of the article emphasizes the transition from the "human data era" to the "experience era" in artificial intelligence, highlighting the need for AI systems to evolve beyond reliance on human knowledge and labels [2][4] - AI systems currently lack the ability for continuous learning and generating new knowledge, which is essential for true intelligence [2][3] - The concept of "decentralized collaboration" is proposed as a means to achieve coexistence and mutual benefit in AI development, contrasting with centralized control approaches [3][4] Group 2 - The article outlines a philosophical perspective on the evolution of the universe, categorizing it into four eras, with humanity entering the "design era" where machines designed by humans become dominant [4] - It is argued that AI is a natural extension of human understanding and evolution, rather than a threat, and that the replacement of human roles by AI is inevitable [4] - The need for a shift in governance from fear-based control to collaboration and trust in AI development is emphasized [3][4]

Core Insights - The article emphasizes the transition of artificial intelligence from a "data-intensive" to an "experience-intensive" era, where true intelligence is derived from active exploration and experience accumulation in real environments [10][11][50]. - The introduction of the AgentGym-RL framework represents a significant advancement in training autonomous LLM agents for multi-turn decision-making, addressing the limitations of existing models that rely on single-turn tasks and lack diverse interaction mechanisms [12][50]. Group 1: Framework and Methodology - AgentGym-RL is the first end-to-end framework for LLM agents that does not require supervised fine-tuning, supports interactive multi-turn training, and has been validated in various real-world scenarios [3][15]. - The framework integrates multiple environments and rich trajectory data, simplifying complex environment configurations into modular operations, thus facilitating effective experience-driven learning [13][19]. - The ScalingInter-RL method introduces a progressive interaction round expansion strategy, allowing agents to gradually adapt to environments and optimize their interaction patterns, balancing exploration and exploitation [4][23][25]. Group 2: Performance and Results - The research team achieved remarkable results with a 7B parameter model, which demonstrated complex task handling skills such as understanding task objectives and planning multi-step operations after extensive interaction training [5][29]. - In various testing environments, the model not only surpassed large open-source models over 100B in size but also matched the performance of top commercial models like OpenAI o3 and Google Gemini 2.5 Pro [5][29]. - The ScalingInter-RL model achieved an overall accuracy of 26.00% in web navigation tasks, significantly outperforming GPT-4o's 16.00% and matching the performance of DeepSeek-R1-0528 and Gemini-2.5-Pro [29][30]. Group 3: Future Directions - Future research will focus on upgrading general capabilities to enable agents to make efficient decisions in new environments and with unknown tools [51]. - The team aims to expand into more complex scenarios that closely resemble the physical world, such as robotic operations and real-world planning [52]. - There is an intention to explore multi-agent collaboration training models to unlock more complex group decision-making capabilities [52].