Group 1: Core Concepts of Machine Emotion - Machine emotion refers to the external manifestation of human-like emotions by AI systems, relying on emotional intelligence and emotional computing as the main technological pathway [3][5][6] - The concept of machine emotion is interdisciplinary, involving cognitive science, emotional philosophy, psychology, computer science, and sociology [5][6] - Emotional intelligence is the foundational capability for machine emotion, encompassing emotional recognition, expression, experience, and control [6][9] Group 2: Construction and Characteristics of Machine Emotion - Machine emotion is characterized by its constructiveness, mimetic nature, embodiment, and computational aspects, emphasizing the generation logic and operational mechanisms of machine emotion [5][12] - The realization of machine emotion depends on emotional computing, which captures human emotional data through various sensors and builds a personalized computational system for emotional understanding and response [7][12] - Machine emotion is limited in its ability to mimic human emotional recognition and expression but lacks genuine emotional experience and control [12][16] Group 3: Human-Machine Emotional Interaction - Human-machine emotion is fundamentally a projection of human emotions onto machines, lacking true intersubjectivity and emotional sharing [13][15] - The construction of human-machine emotion is influenced by psychological mechanisms, such as anthropomorphism and social cultural factors, which shape human emotional responses to machines [15][16] - The emotional interaction between humans and machines can lead to risks such as emotional deception, emotional monitoring, emotional degradation, and emotional manipulation [17][18] Group 4: Ethical Considerations and Development of Machine Emotion - To mitigate risks associated with machine emotion, it is essential to construct machine emotions that serve human needs and enhance interaction [18][19] - The development of machine emotion should adhere to a limited approach, ensuring the appropriateness of emotional capabilities and avoiding exaggerated claims [19][20] - Transparency, authenticity, and rigor should guide the promotion of machine emotions, ensuring users are aware of the simulated nature of emotional responses [20][21]

Core Viewpoint - The article discusses the insights of Fei-Fei Li, a prominent AI expert, on the development of spatial intelligence and its significance in AI, emphasizing the need for 3D world modeling to enhance AI capabilities [2][5][19]. Group 1: Spatial Intelligence - Spatial intelligence refers to the ability to understand, reason, interact with, and generate 3D worlds, which is fundamental to both human and animal cognition [5][9]. - The development of 3D world models is seen as a critical challenge in AI, with the potential to unlock numerous applications in design, navigation, and augmented reality [6][20]. - Li believes that without spatial intelligence, AI remains incomplete, as it is essential for interaction within the 3D world [9][19]. Group 2: Challenges in AI Development - Data acquisition and processing for creating 3D models pose significant challenges, as the availability of suitable data is not as abundant as in natural language processing [20]. - The complexity of delivering 3D experiences to users is greater than that of language, making productization more challenging [20]. - Li highlights the importance of integrating tactile data into AI systems, which has been underexplored but is crucial for enhancing robotic capabilities [16]. Group 3: Future of AI and Robotics - The future of robotics is envisioned as a coexistence with humans, where robots will take on various forms beyond humanoid shapes, optimizing for specific tasks [15][17]. - Li emphasizes the need for diverse backgrounds in AI teams to tackle the multifaceted challenges of spatial intelligence [32]. - The potential for AI to enhance human creativity in fields like design and content creation is seen as a promising area for future development [17][30]. Group 4: Personal Insights and Career Reflections - Li reflects on her career, particularly the creation of the ImageNet dataset, which played a pivotal role in advancing deep learning and AI [26][27]. - The journey of developing ImageNet involved significant challenges, including data collection and processing, which were crucial for training effective models [23][24]. - Li encourages young researchers to be fearless in their pursuits, emphasizing the importance of creativity and innovation in AI research [30][31].