MEET2026挤爆了，AI圈今年最该听的20+场演讲&对谈都在这

Core Insights - The MEET2026 Smart Future Conference highlighted the rapid evolution of AI technologies, emphasizing the transition towards AGI and the emergence of intelligent agents as a pivotal development in the industry [1][3][5]. Group 1: Conference Overview - The MEET2026 conference, hosted by Quantum Bit, attracted nearly 1,500 attendees and over 3.5 million online viewers, showcasing significant interest from various sectors including academia, industry, and investment [5]. - Key themes discussed included the integration of AI across different modalities and the importance of collaboration between industry and academia to drive innovation [3][5]. Group 2: Keynote Highlights - Zhang Yaqin from Tsinghua University emphasized the trend of AI integration across information, physical, and biological intelligence, predicting that foundational models will consolidate into fewer than ten globally [8]. - Wang Ying from Baidu discussed the evolution of generative AI into intelligent agents, which will replace many existing SaaS and apps, marking a shift towards an "agent internet" [8][9]. - Wang Zhongyuan from Beijing Academy of Artificial Intelligence noted that the current AI wave is a critical turning point, moving from weak AI to general AI, with a focus on multi-modal learning [11]. Group 3: Industry Applications - Dennis Yue from Google Cloud highlighted the importance of AI agents in automating processes and creating new business models, emphasizing the need for seamless integration across various platforms [21]. - Zhao Ce from Zhuosheng Technology discussed the necessity of creating a closed-loop commercial model that integrates models, terminals, and data to achieve effective AI deployment in industries [25]. - Yang Haibo from Guanglun Intelligent stressed the importance of simulation infrastructure in bridging the gap between simulated and real-world applications, which is crucial for the success of physical AI [49]. Group 4: Challenges and Future Directions - The industry faces challenges such as cognitive biases and user experience gaps in AI product usage, which need to be addressed to empower users effectively [9]. - The need for a robust ecosystem to support AI applications was emphasized, with calls for collaboration among enterprises to build open-source platforms [39]. - The discussions pointed towards a future where AI agents will need to demonstrate controllability and interpretability, ensuring they can work alongside humans effectively [57].