Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References VIII. EMERGENT RESEARCH PRIORITIES IN GENERATIVE AI As we are likely to approach the precipice of a new era marked by the advent of Q*, nudging us closer to the realization of usable AGI, the research landscape in generative AI is undergoing a crucial transformation. A. Emergent Research Priorities in MoE The MoE domain is increasingly focusing on two critical areas: • Multimodal Models in Model Architecture: The integration of MoE and AGI is opening new pathways for research in multimodal models. These developments are enhancing the capability to process and synthesize information from multiple modalities, which is crucial for both specialized and generalized AI systems. • Multimodal Learning in Emerging Trends: MoE is at the forefront of multimodal learning, integrating diverse data types like text, images, and audio for specialized tasks. This trend is directly impacting the enhancement of the field. Furthermore, an analysis of funding trends and investment patterns in AI research could indicate a substantial shift towards areas like multimodal models in MoE. This trend, characterized by increased capital flow into fields involving complex data processing and autonomous systems, is shaping the direction of future research priorities. It underscores the growing interest and investment in the potential of generative AI, influencing both academic and industry-led initiatives. B. Emergent Research Priorities in Multimodality In the realm of multimodality, several areas are identified as emerging research priorities: • MoE in Model Architecture: MoE models are becoming increasingly relevant for handling diverse data types in multimodal contexts. • Transfer Learning in Training Techniques: Transfer learning is emerging as a key research direction, especially for learning between different modalities. • Conversational AI and Creative AI in Application Domains: Both conversational AI and creative AI are expanding in multimodal contexts, encompassing visual, auditory, and other sensory data integration. • Self-Supervised Learning in Advanced Learning: New research directions in self-supervised learning are emerging, focusing on the integration of various data types autonomously. Additionally, the rise of generative AI, particularly in multimodal contexts, can significantly impact educational curricula and skill development. There is a growing need to update academic programs to include comprehensive AI literacy, with a focus on multimodal AI technologies. This evolution in education is aimed at preparing future professionals to effectively engage with and leverage the advancements in AI, equipping them with the necessary skills to navigate its complexities and innovations. C. Emergent Research Priorities in AGI The AGI domain is witnessing a surge in research priorities across multiple areas: • Multimodal Models in Model Architecture: Similar to MoE, multimodal models are crucial in AGI, enabling deeper and more nuanced understanding. • Reinforcement Learning in Training Techniques: Emerging as a key area in AGI, reinforcement learning focuses on developing autonomous systems learning from their environment. • Application Domains: AGI is extending the boundaries of natural language understanding and generation, conversational AI, and creative AI, with a focus on human-like comprehension and creativity. • Bias Mitigation in Compliance and Ethical Considerations: New directions in bias mitigation are focusing on a comprehensive approach to addressing biases across diverse domains in AGI. • Meta-Learning in Advanced Learning: AGI’s pursuit of human-like adaptability is leading to novel research in meta-learning. • Emerging Trends: Multimodal learning, interactive and cooperative AI, and AGI containment strategies are becoming crucial research areas as AGI progresses. In line with these developments in AGI, a noticeable trend in AI research funding and investment patterns is evident. There is a significant inclination towards supporting projects and studies in AGI, particularly in areas such as natural language understanding and generation, and autonomous systems. This funding trend not only mirrors the escalating interest in the capabilities of AGI but also directs the trajectory of future research, shaping both academic exploration and industry driven projects. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References VIII. EMERGENT RESEARCH PRIORITIES IN GENERATIVE AI As we are likely to approach the precipice of a new era marked by the advent of Q*, nudging us closer to the realization of usable AGI, the research landscape in generative AI is undergoing a crucial transformation. A. Emergent Research Priorities in MoE A. Emergent Research Priorities in MoE The MoE domain is increasingly focusing on two critical areas: • Multimodal Models in Model Architecture: The integration of MoE and AGI is opening new pathways for research in multimodal models. These developments are enhancing the capability to process and synthesize information from multiple modalities, which is crucial for both specialized and generalized AI systems. • Multimodal Models in Model Architecture: • Multimodal Learning in Emerging Trends: MoE is at the forefront of multimodal learning, integrating diverse data types like text, images, and audio for specialized tasks. This trend is directly impacting the enhancement of the field. • Multimodal Learning in Emerging Trends: Furthermore, an analysis of funding trends and investment patterns in AI research could indicate a substantial shift towards areas like multimodal models in MoE. This trend, characterized by increased capital flow into fields involving complex data processing and autonomous systems, is shaping the direction of future research priorities. It underscores the growing interest and investment in the potential of generative AI, influencing both academic and industry-led initiatives. B. Emergent Research Priorities in Multimodality B. Emergent Research Priorities in Multimodality In the realm of multimodality, several areas are identified as emerging research priorities: • MoE in Model Architecture: MoE models are becoming increasingly relevant for handling diverse data types in multimodal contexts. • MoE in Model Architecture: • Transfer Learning in Training Techniques: Transfer learning is emerging as a key research direction, especially for learning between different modalities. • Transfer Learning in Training Techniques: • Conversational AI and Creative AI in Application Domains: Both conversational AI and creative AI are expanding in multimodal contexts, encompassing visual, auditory, and other sensory data integration. • Conversational AI and Creative AI in Application Domains: • Self-Supervised Learning in Advanced Learning: New research directions in self-supervised learning are emerging, focusing on the integration of various data types autonomously. • Self-Supervised Learning in Advanced Learning: Additionally, the rise of generative AI, particularly in multimodal contexts, can significantly impact educational curricula and skill development. There is a growing need to update academic programs to include comprehensive AI literacy, with a focus on multimodal AI technologies. This evolution in education is aimed at preparing future professionals to effectively engage with and leverage the advancements in AI, equipping them with the necessary skills to navigate its complexities and innovations. C. Emergent Research Priorities in AGI C. Emergent Research Priorities in AGI The AGI domain is witnessing a surge in research priorities across multiple areas: • Multimodal Models in Model Architecture: Similar to MoE, multimodal models are crucial in AGI, enabling deeper and more nuanced understanding. • Multimodal Models in Model Architecture: • Reinforcement Learning in Training Techniques: Emerging as a key area in AGI, reinforcement learning focuses on developing autonomous systems learning from their environment. • Reinforcement Learning in Training Techniques: • Application Domains: AGI is extending the boundaries of natural language understanding and generation, conversational AI, and creative AI, with a focus on human-like comprehension and creativity. • Application Domains: • Bias Mitigation in Compliance and Ethical Considerations: New directions in bias mitigation are focusing on a comprehensive approach to addressing biases across diverse domains in AGI. • Bias Mitigation in Compliance and Ethical Considerations: • Meta-Learning in Advanced Learning: AGI’s pursuit of human-like adaptability is leading to novel research in meta-learning. • Meta-Learning in Advanced Learning: • Emerging Trends: Multimodal learning, interactive and cooperative AI, and AGI containment strategies are becoming crucial research areas as AGI progresses. • Emerging Trends: In line with these developments in AGI, a noticeable trend in AI research funding and investment patterns is evident. There is a significant inclination towards supporting projects and studies in AGI, particularly in areas such as natural language understanding and generation, and autonomous systems. This funding trend not only mirrors the escalating interest in the capabilities of AGI but also directs the trajectory of future research, shaping both academic exploration and industry driven projects. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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