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

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dc.citation.issue2
dc.citation.volume13
dc.contributor.authorMcIntosh TR
dc.contributor.authorSusnjak T
dc.contributor.authorLiu T
dc.contributor.authorWatters P
dc.contributor.authorXu D
dc.contributor.authorLiu D
dc.contributor.authorHalgamuge MN
dc.contributor.editorMladenov V
dc.date.accessioned2025-03-10T02:27:11Z
dc.date.available2025-03-10T02:27:11Z
dc.date.issued2025-02-01
dc.description.abstractThis comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the recent technological breakthroughs and the gathering advancements toward possible Artificial General Intelligence (AGI). It critically examined the current state and future trajectory of generative AI, exploring how innovations in developing actionable and multimodal AI agents with the ability scale their “thinking” in solving complex reasoning tasks are reshaping research priorities and applications across various domains, while the survey also offers an impact analysis on the generative AI research taxonomy. This work has 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. Our study 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 generative AI as its capabilities continue to scale.
dc.description.confidentialfalse
dc.edition.editionFebruary 2025
dc.identifier.citationMcIntosh TR, Susnjak T, Liu T, Watters P, Xu D, Liu D, Halgamuge MN. (2025). From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape. Technologies. 13. 2.
dc.identifier.doi10.3390/technologies13020051
dc.identifier.eissn2227-7080
dc.identifier.elements-typejournal-article
dc.identifier.number51
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72607
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2227-7080/13/2/51
dc.relation.isPartOfTechnologies
dc.rights(c) 2025 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAI ethics
dc.subjectartificial general intelligence (AGI)
dc.subjectartificial intelligence (AI)
dc.subjectGemini
dc.subjectgenerative AI
dc.subjectmixture of experts (MoE)
dc.subjectmultimodality
dc.subjectQ* (Q-star)
dc.subjecttest-time compute
dc.subjectagentic AI
dc.subjectresearch impact analysis
dc.titleFrom Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape
dc.typeJournal article
pubs.elements-id499973
pubs.organisational-groupOther
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