This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative Artificial Intelligence (AI), exploring how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains, including an impact analysis on the generative AI research taxonomy. It assessed the computational challenges, scalability, and real-world implications of these technologies while highlighting their potential in driving significant progress in fields like healthcare, finance, and education. It also addressed the emerging academic challenges posed by the proliferation of both AI-themed and AI-generated preprints, examining their impact on the peer-review process and scholarly communication. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare, and outlined a strategy for future AI research that focuses on a balanced and conscientious use of MoE, multimodality, and AGI in generative AI.

翻译：本综合综述探讨了生成式人工智能（AI）不断演进的格局，特别关注专家混合（MoE）、多模态学习以及向通用人工智能（AGI）推测性进展的变革性影响。它批判性地审视了生成式人工智能的当前状态和未来轨迹，探讨了Google Gemini和预期中的OpenAI Q*项目等创新如何重塑各领域的研究重点和应用，包括对生成式AI研究分类学的影响分析。本文评估了这些技术的计算挑战、可扩展性及现实意义，同时强调了它们在推动医疗、金融和教育等领域取得重大进展的潜力。文章还探讨了人工智能主题及AI生成预印本激增所带来的新兴学术挑战，考察了它们对同行评审过程和学术交流的影响。本研究强调了在人工智能开发中融入伦理和以人为本方法的重要性，确保与社会规范和福利保持一致，并为未来人工智能研究制定了一项战略，侧重于在生成式AI中平衡且审慎地运用MoE、多模态性及通用人工智能。