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

Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters, Malka N. Halgamuge

TL;DR

The paper surveys the evolving generative AI landscape shaped by MoE, multimodality, and the pursuit of AGI, with Gemini and the speculative Q* as focal exemplars. It provides a taxonomy of current AI research, analyzes MoE and multimodality innovations, and outlines speculative capabilities and governance challenges for Q* and AGI. It also develops an impact-analysis framework to assess how these advances reshape model architectures, training methods, and applications, and discusses practical constraints, ethical considerations, and the surge of AI preprints. The work highlights emergent priorities and calls for interdisciplinary collaboration to align rapid AI progress with societal welfare and responsible governance.

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.

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

TL;DR

The paper surveys the evolving generative AI landscape shaped by MoE, multimodality, and the pursuit of AGI, with Gemini and the speculative Q* as focal exemplars. It provides a taxonomy of current AI research, analyzes MoE and multimodality innovations, and outlines speculative capabilities and governance challenges for Q* and AGI. It also develops an impact-analysis framework to assess how these advances reshape model architectures, training methods, and applications, and discusses practical constraints, ethical considerations, and the surge of AI preprints. The work highlights emergent priorities and calls for interdisciplinary collaboration to align rapid AI progress with societal welfare and responsible governance.

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.
Paper Structure (67 sections, 5 equations, 8 figures, 3 tables)

This paper contains 67 sections, 5 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Changing AI Research Popularity
  3. Objectives
  4. Background: Evolution of Generative AI
  5. The Evolution of Language Models
  6. Language Models as Precursors
  7. Large Language Models: Technical Advancement and Commercial Success
  8. Fine-tuning, Hallucination Reduction, and Alignment in LLMs
  9. Mixture of Experts: A Paradigm Shift
  10. Multimodal AI and the Future of Interaction
  11. Gemini: Redefining Benchmarks in Multimodality
  12. Technical Challenges in Multimodal Systems
  13. Multimodal AI: Beyond Text in Ethical and Social Contexts
  14. Speculative Advances and Chronological Trends
  15. From AlphaGo's Groundtruth to Q-Star's Exploration
  16. ...and 52 more sections

Figures (8)

  • Figure 1: Number of search results on Google Scholar with different keywords by year
  • Figure 2: Annual number of preprints posted under the cs.AI category on arXiv.org
  • Figure 3: Timeline of Key Developments in Language Model Evolution
  • Figure 4: Conceptual Diagram of MoE's Innovation
  • Figure 5: Conceptual Diagram of Speculated Q* Capabilities
  • ...and 3 more figures