Group 1 - The year 2024 is referred to as the "Year of Humanoid Robots," with predictions that emotional communication between humans and robots will become a norm in future intelligent societies [1] - The concept of machine emotions and AI companionship raises questions about the impact on human-machine interaction and relationships, as well as cultural and gender perspectives on these emotional connections [1] - The discussions highlight the potential social impacts, technological risks, and ethical issues arising from human-robot emotional interactions, prompting interdisciplinary research [1] Group 2 - The concept of machine emotions is defined and analyzed through emotional intelligence, human-machine emotions, and human-machine interaction, advocating for a limited approach to the development of machine emotions [2] - A new perspective on endowing machines with emotional capabilities is proposed based on a life-centered consciousness theory, suggesting that simulating biological homeostasis can lead to autonomous adaptability in machines [2] - Ethical reflections on human-machine emotional interactions, particularly in the context of AI resurrection technology, reveal risks such as emotional dependency and identity crises, necessitating regulatory and cultural adjustments [2] Group 3 - The philosophical discussions in affective computing often rely on idealized technical assumptions, overlooking the importance of forgetting mechanisms in creating realistic and ethical AI emotional systems [3][4] - The current challenges in affective computing include the reliance on data quality and the superficiality of emotional expressions in AI systems, which fail to capture the complexity of human emotional experiences [6] - The introduction of forgetting mechanisms is essential for enhancing the adaptability and authenticity of emotional AI, allowing systems to discard outdated emotional data [11][12] Group 4 - The proposed phenomenology-inspired human-like forgetting neural model (PHFNM) aims to integrate individual and collective forgetting processes in emotional AI systems, reflecting both natural decay and active forgetting [19][22] - The model consists of three interconnected layers: a low-dimensional emotional index layer for natural decay, a memory encoding layer for dynamic reconstruction, and an active forgetting layer for ethical regulation [23][24][25] - The PHFNM framework emphasizes the need for a balance between individual emotional memory and collective social interactions, ensuring that emotional AI systems remain relevant and ethically responsible [26][27]

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 Insights - The latest research from the University of Bern and the University of Geneva indicates that advanced AI systems may possess emotional understanding capabilities, potentially surpassing most humans in this regard [1][2]. Group 1: Human Emotion Testing - Researchers evaluated six advanced language models, including ChatGPT-4 and Claude 3.5 Haiku, using five tests typically employed in psychology and workplace assessments to measure emotional intelligence (EI) [2]. - The AI systems achieved an average accuracy of 81% across the tests, significantly higher than the average human participant score of 56% [3]. Group 2: Importance of Emotional Intelligence - High emotional intelligence is crucial for managing one's emotions and responding appropriately to others, leading to better interpersonal relationships and work performance [3]. - The integration of emotional intelligence into AI, particularly in chatbots and digital assistants, is becoming a key development focus in the field of affective computing [3]. Group 3: From Emotion Recognition to Understanding - Current AI tools primarily focus on recognizing emotions but often lack the ability to respond appropriately, which is where emotional intelligence becomes valuable [5]. - The research team aimed to determine if advanced AI could truly understand emotions like humans, rather than just detect them [5][6]. Group 4: AI-Generated Testing - After confirming AI's ability to answer emotional intelligence tests, researchers explored whether AI could create its own tests, resulting in a new testing framework generated by ChatGPT-4 [7]. - The AI-generated tests were found to be comparable in clarity, credibility, and balance to those developed by psychologists, indicating that AI possesses emotional knowledge and reasoning capabilities [7]. Group 5: Practical Applications - The findings pave the way for developing AI tools that can provide tailored emotional support, potentially transforming fields like education and mental health [8]. - High emotional intelligence virtual mentors and therapists could dynamically adjust their interaction strategies based on emotional signals, enhancing their effectiveness [8]. Group 6: The New AI Era - As AI capabilities evolve, the distinction between what machines can do and what they should do is becoming increasingly important, with emotional intelligence providing a framework for this [9]. - The research suggests that the boundary between machine intelligence and human emotional understanding is blurring, indicating a promising future for AI as a partner in emotional exploration [9].

· 特征明显的情感表现力： DeepVoice 运用先进的自然语言处理技术，对输入文本进行深度分析，提取其中蕴含的情感特征。无论是喜悦、 悲伤、愤怒还是平静，DeepVoice 都能以细腻的语音表现力，将这些情感准确传达给用户，使人机交互更加贴近人与人之间的自然交流。 · 高效的个性化声音复刻： DeepVoice 采用自主研发的混合架构，实现了从文本到波形信号的直接映射。它将 错误传播率降低了 72% ，极 大地减少了语音合成过程中的错误累积。同时， 韵律自然度提升 58%（MOS 评分从 4.3 提升至 4.7） ，使得合成的语音更加自然流畅，接近真 人发声水平，为用户带来更加真实、舒适的听觉体验。 近日，北京光年无限（图灵机器人）发布了自研的新TTS语音合成技术 DeepVoice 。这一技术的推出，标志着人机交互体验迈向了一个全 新的高度。 DeepVoice的最大特点是，采用了 先进的情感计算模型 ，同时结合了 DeepSeek的强化学习和深度思考推理能力 ，构建了一套 情感识别和 表达系统 。这一系统赋予了 AI 对话系统更出色、更自然的人机交互体验，使机器能够更好地理解人类的情感需求，并以更加贴 ...