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

• URL: http://arxiv.org/abs/2312.10868v1
• Date: Mon, 18 Dec 2023 01:11:39 GMT
• Title: From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape
• Authors: Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, Malka N. Halgamuge
• Abstract summary: The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
• Score: 5.852005817069381
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives [10.16399860867284]
  The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP) This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications.
  arXiv  Detail & Related papers  (2024-07-20T18:48:35Z)
• Responsible AI: Portraits with Intelligent Bibliometrics [30.51687434548628]
  This study defined responsible AI and identified its core principles. Empirically, this study investigated 17,799 research articles contributed by the AI community since 2015. An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in responsible AI.
  arXiv  Detail & Related papers  (2024-05-05T08:40:22Z)
• Advancing Explainable AI Toward Human-Like Intelligence: Forging the Path to Artificial Brain [0.7770029179741429]
  The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed.
  arXiv  Detail & Related papers  (2024-02-07T14:09:11Z)
• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• AI for social science and social science of AI: A Survey [47.5235291525383]
  Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research.
  arXiv  Detail & Related papers  (2024-01-22T10:57:09Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.