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.

这篇综述论文全面调查了生成人工智能领域的发展现状，特别关注了专家混合模型、多模态学习以及人工通用智能的变革性影响。它批判性地研究了生成人工智能方面当前的状况和未来的发展轨迹，探讨了像谷歌的 Gemini 和 OpenAI Q * 等创新如何重新塑造跨领域的研究优先事项和应用，包括对生成人工智能研究分类学的影响分析。该论文评估了这些技术在计算挑战、可扩展性和现实世界应用方面的问题，并强调了它们在医疗、金融和教育等领域取得重大进展的潜力。它还讨论了人工智能主题和人工智能生成预印本的普及对同行评议过程和学术交流的影响，指出了在人工智能发展中纳入道德和以人为本的方法的重要性，确保与社会规范和福祉的一致，并概述了一个关注专家混合模型、多模态学习和人工通用智能在生成人工智能中平衡和慎重使用的未来研究策略。

从 Google Gemini 到 OpenAI Q*(Q-Star): 改变生成性人工智能（AI）研究领域的调查

From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the  Generative Artificial Intelligence (AI) Research Landscape

To achieve successful deployment of AI research, it is crucial to understand
the demands of the industry. In this paper, we present the results of a survey
conducted with professional audio engineers, in order to determine research
priorities and define various research tasks. We also summarize the current
challenges in audio quality and controllability based on the survey. Our
analysis emphasizes that the availability of datasets is currently the main
bottleneck for achieving high-quality audio generation. Finally, we suggest
potential solutions for some revealed issues with empirical evidence.

本文通过对专业音频工程师的调查，确定研究优先事项和定义各种研究任务，总结了当前音频质量和可控性方面的挑战，并强调了数据集的可用性目前是实现高质量音频生成的主要瓶颈。最后，我们根据经验证据提出了解决一些问题的潜在解决方案。

以需求驱动的方式理解生成音频人工智能

A Demand-Driven Perspective on Generative Audio AI

One way of carving up the broad "AI ethics and society" research space that
has emerged in recent years is to distinguish between "near-term" and
"long-term" research. While such ways of breaking down the research space can
be useful, we put forward several concerns about the near/long-term distinction
gaining too much prominence in how research questions and priorities are
framed. We highlight some ambiguities and inconsistencies in how the
distinction is used, and argue that while there are differing priorities within
this broad research community, these differences are not well-captured by the
near/long-term distinction. We unpack the near/long-term distinction into four
different dimensions, and propose some ways that researchers can communicate
more clearly about their work and priorities using these dimensions. We suggest
that moving towards a more nuanced conversation about research priorities can
help establish new opportunities for collaboration, aid the development of more
consistent and coherent research agendas, and enable identification of
previously neglected research areas.

区分 “近期” 和 “长期” 研究在人工智能伦理和社会研究领域中已经具有广泛应用和重要性，但是文章对该分类方式的模棱两可和不一致性提出了一些担忧。为了建立新的合作机会、发展更一致的研究议程并确定以前忽视的研究领域，作者建议通过 4 个维度进行更加清晰的交流。