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Surveying the Evolution and Future Trajectory of Generative AI - Conclusions, References

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 X. IMPACT OF GENERATIVE AI ON PREPRINTS ACROSS DISCIPLINES The challenges detailed in this section are not directly related to the knowledge domains within generative AI, but are fueled by the success of Generative AI, particularly the commercialization of ChatGPT. The proliferation of preprints in the field of AI (Fig. 7), especially in the cs.AI category on platforms like arXiv, has introduced a set of academic challenges that merit careful consideration and strategic response. The rapid commercialization and adoption of tools such as ChatGPT, as evidenced by over 55,700 entries on Google Scholar mentioning “ChatGPT” within just one year of its commercialization, exemplify the accelerated pace at which the field is advancing. This rapid development is not mirrored in the traditional peer-review process, which is considerably slower. The peer-review process now appears to be overwhelmed with manuscripts that are either generated with ChatGPT (or other LLMs), or whose writing processes have been significantly accelerated by such LLMs, contributing to a bottleneck in scholarly communication [325], [326]. This situation is further compounded by the fact that many journals in disciplines outside of computer science are also experiencing longer review times and higher rates of desk rejections. Additionally, the flourishing trend of manuscripts and preprints, either generated by or significantly expedited using tools like ChatGPT, extends beyond computer science into diverse academic disciplines. This trend presents a looming challenge, potentially overwhelming both the traditional peer review process and the flourishing preprint ecosystem with a volume of work that may not always adhere to established academic standards. The sheer volume of preprints has made the task of selecting and scrutinizing research exceedingly demanding. In the current research era, the exploration of scientific literature has become increasingly complex, as knowledge has continued to expand and disseminate exponentially, while concurrently, integrative research efforts attempting to distill these vast literature, attempt to identify and understand a smaller sets of core contributions [327]. Thus, the rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge [328]. Furthermore, this explosion in publication volume poses a distinct challenge for literature reviews and surveys, where the human capacity for manually selecting, understanding, and critically evaluating articles is increasingly strained, potentially leading to gaps in synthesizing comprehensive knowledge landscapes. Although reproduction of results is a theoretical possibility, practical constraints such as the lack of technical expertise, computational resources, or access to proprietary datasets hinder rigorous evaluation. This is concerning, as the inability to thoroughly assess preprint research undermines the foundation of scientific reliability and validity. Furthermore, the peerreview system, a cornerstone of academic rigour, is under the threat of being further overwhelmed [325], [329]. The potential consequences are significant, with unvetted preprints possibly perpetuating biases or errors within the scientific community and beyond. The absence of established retraction mechanisms for preprints, akin to those for published articles, exacerbates the risk of persistent dissemination of flawed research. The academic community is at a crossroads, necessitating an urgent and thoughtful discourse on navigating this emerging “mess” — a situation that risks spiraling out of control if left unaddressed. In this context, the role of peer review becomes increasingly crucial, as it serves as a critical checkpoint for quality and validity, ensuring that the rapid production of AI research is rigorously studied for scientific accuracy and relevance. However, the current modus operandi of traditional peer review does not appear to be sustainable, primarily due to its inability to keep pace with the exponential growth in AI-themed research and Generative-AI-accelerated research submissions, and the increasingly specialized nature of emerging AI topics [325], [326]. This situation is compounded by a finite pool of qualified reviewers, leading to delays, potential biases, and a burden on the scholarly community. This reality demands an exploration of new paradigms for peer review and dissemination of research that can keep pace with swift advancements in AI. Innovative models for community-driven vetting processes, enhanced reproducibility checks, and dynamic frameworks for post-publication scrutiny and correction may be necessary. Efforts to incorporate automated tools and AI-assisted review processes could also be explored to alleviate the strain on human reviewers. In this rapidly evolving landscape, envision a convergence between the traditional peer review system and the flourishing preprint ecosystem, which could involve creating hybrid models (Fig. 8), where preprints undergo a preliminary community-based review, harnessing the collective expertise and rapid feedback of the academic community, similar to product review websites and Twitter [330]. This approach could provide an initial layer of validation, offering additional insights on issues that may be overlooked by a limited number of peer reviewers. The Editors-in-Chief (EICs) could consider the major criticisms and suggestions of an article from the community-based review, ensuring a more thorough and diverse evaluation. Subsequent, more formal peer review processes could then refine and endorse these preprints for academic rigor and quality assurance. This hybrid model would require robust technological support, possibly leveraging AI and machine learning tools to assist in initial screening and identification of suitable reviewers. The aim would be to establish a seamless continuum from rapid dissemination to validated publication, ensuring both the speed of preprints and the credibility of peer-reviewed research. A balanced approach must be struck to harness the benefits of preprints—such as rapid dissemination of findings and open access—while mitigating their drawbacks. The development of new infrastructure and norms could be instrumental in steering the academic community towards a sustainable model that upholds the integrity and trustworthiness of scientific research in the age of Generative AI. 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 X. IMPACT OF GENERATIVE AI ON PREPRINTS ACROSS DISCIPLINES The challenges detailed in this section are not directly related to the knowledge domains within generative AI, but are fueled by the success of Generative AI, particularly the commercialization of ChatGPT. The proliferation of preprints in the field of AI (Fig. 7), especially in the cs.AI category on platforms like arXiv, has introduced a set of academic challenges that merit careful consideration and strategic response. The rapid commercialization and adoption of tools such as ChatGPT, as evidenced by over 55,700 entries on Google Scholar mentioning “ChatGPT” within just one year of its commercialization, exemplify the accelerated pace at which the field is advancing. This rapid development is not mirrored in the traditional peer-review process, which is considerably slower. The peer-review process now appears to be overwhelmed with manuscripts that are either generated with ChatGPT (or other LLMs), or whose writing processes have been significantly accelerated by such LLMs, contributing to a bottleneck in scholarly communication [325], [326]. This situation is further compounded by the fact that many journals in disciplines outside of computer science are also experiencing longer review times and higher rates of desk rejections. Additionally, the flourishing trend of manuscripts and preprints, either generated by or significantly expedited using tools like ChatGPT, extends beyond computer science into diverse academic disciplines. This trend presents a looming challenge, potentially overwhelming both the traditional peer review process and the flourishing preprint ecosystem with a volume of work that may not always adhere to established academic standards. The sheer volume of preprints has made the task of selecting and scrutinizing research exceedingly demanding. In the current research era, the exploration of scientific literature has become increasingly complex, as knowledge has continued to expand and disseminate exponentially, while concurrently, integrative research efforts attempting to distill these vast literature, attempt to identify and understand a smaller sets of core contributions [327]. Thus, the rapid expansion of academic literature across various fields presents a significant challenge for researchers seeking to perform evidence syntheses over the increasingly vast body of available knowledge [328]. Furthermore, this explosion in publication volume poses a distinct challenge for literature reviews and surveys, where the human capacity for manually selecting, understanding, and critically evaluating articles is increasingly strained, potentially leading to gaps in synthesizing comprehensive knowledge landscapes. Although reproduction of results is a theoretical possibility, practical constraints such as the lack of technical expertise, computational resources, or access to proprietary datasets hinder rigorous evaluation. This is concerning, as the inability to thoroughly assess preprint research undermines the foundation of scientific reliability and validity. Furthermore, the peerreview system, a cornerstone of academic rigour, is under the threat of being further overwhelmed [325], [329]. The potential consequences are significant, with unvetted preprints possibly perpetuating biases or errors within the scientific community and beyond. The absence of established retraction mechanisms for preprints, akin to those for published articles, exacerbates the risk of persistent dissemination of flawed research. The academic community is at a crossroads, necessitating an urgent and thoughtful discourse on navigating this emerging “mess” — a situation that risks spiraling out of control if left unaddressed. In this context, the role of peer review becomes increasingly crucial, as it serves as a critical checkpoint for quality and validity, ensuring that the rapid production of AI research is rigorously studied for scientific accuracy and relevance. However, the current modus operandi of traditional peer review does not appear to be sustainable, primarily due to its inability to keep pace with the exponential growth in AI-themed research and Generative-AI-accelerated research submissions, and the increasingly specialized nature of emerging AI topics [325], [326]. This situation is compounded by a finite pool of qualified reviewers, leading to delays, potential biases, and a burden on the scholarly community. This reality demands an exploration of new paradigms for peer review and dissemination of research that can keep pace with swift advancements in AI. Innovative models for community-driven vetting processes, enhanced reproducibility checks, and dynamic frameworks for post-publication scrutiny and correction may be necessary. Efforts to incorporate automated tools and AI-assisted review processes could also be explored to alleviate the strain on human reviewers. In this rapidly evolving landscape, envision a convergence between the traditional peer review system and the flourishing preprint ecosystem, which could involve creating hybrid models (Fig. 8), where preprints undergo a preliminary community-based review, harnessing the collective expertise and rapid feedback of the academic community, similar to product review websites and Twitter [330]. This approach could provide an initial layer of validation, offering additional insights on issues that may be overlooked by a limited number of peer reviewers. The Editors-in-Chief (EICs) could consider the major criticisms and suggestions of an article from the community-based review, ensuring a more thorough and diverse evaluation. Subsequent, more formal peer review processes could then refine and endorse these preprints for academic rigor and quality assurance. This hybrid model would require robust technological support, possibly leveraging AI and machine learning tools to assist in initial screening and identification of suitable reviewers. The aim would be to establish a seamless continuum from rapid dissemination to validated publication, ensuring both the speed of preprints and the credibility of peer-reviewed research. A balanced approach must be struck to harness the benefits of preprints—such as rapid dissemination of findings and open access—while mitigating their drawbacks. The development of new infrastructure and norms could be instrumental in steering the academic community towards a sustainable model that upholds the integrity and trustworthiness of scientific research in the age of Generative AI. 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

Surveying the Evolution and Future Trajectory of Generative AI - Impact on Preprints

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 IX. PRACTICAL IMPLICATIONS AND LIMITATIONS OF GENERATIVE AI TECHNOLOGIES Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. A. Computational Complexity and Real-world Applications of Generative AI Technologies 1) Computational Complexity: Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. • Processing Power Requirements: Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power [321]. The demand for GPUs and TPUs is accentuated, particularly when handling complex computations and large datasets typical in multimodal AI applications. • Memory Usage in AI Modeling: A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements. Unlike computer RAM, VRAM often cannot be expanded easily on many platforms, posing significant constraints. Developing strategies for GPU and VRAM optimization and efficient model scaling is thus crucial for the practical deployment of these AI technologies. • Scalability and Efficiency in AI Deployment: Addressing scalability challenges in generative AI, especially in MoE and AGI contexts, involves optimizing load management and parallel processing techniques. This is vital for their practical application in fields like healthcare, finance, and education. 2) Real-world Application Examples of Generative AI Technologies: The application of generative AI models in real-world scenarios demonstrates their transformative potential and challenges in various sectors. • Healthcare: In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information [322]. • Finance: The use of AI for fraud detection and algorithmic trading in finance underlines its efficiency and accuracy, while at the same time, it raises ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability [323]. • Education: Generative AI’s role in creating personalized learning experiences offers immense benefits in terms of educational accessibility and tailored instruction. However, it poses challenges in equitable access to technology, potential biases in AI-Generated Content (AIGC), and could reduce demand for human educators. Additionally, there’s a growing concern about educators who are against the use of AIGC, fearing it may undermine traditional teaching methodologies and the role of educators. B. Commercial Viability and Industry Solutions in Generative AI Technologies 1) Market Readiness: Assessing the market readiness of generative AI technologies involves analyzing cost, accessibility, deployment challenges, and user adoption trends. • Cost Analysis: The financial aspects of deploying generative AI, including MoE, multimodality, and AGI, are crucial for market adoption. • Accessibility and Deployment: Integration of these technologies into existing systems and the technical expertise required are key factors influencing their adoption. • User Adoption Trends: Understanding current adoption patterns provides insights into market acceptance and the role of user trust and perceived benefits. 2) Existing Industry Solutions: Generative AI is reshaping various industries by offering innovative solutions and altering market dynamics. • Sector-Wise Deployment: The diverse applications of generative AI, from digital content creation to process streamlining, also raise questions about originality and intellectual property rights. • Impact on Market Dynamics: The effect of AI solutions on traditional industry structures and the introduction of novel business models are significant considerations. • Challenges and Constraints: Addressing limitations such as scalability, data management complexity, privacy concerns, and ethical implications is essential for robust governance frameworks. C. Limitations and Future Directions in Generative AI Technologies 1) Technical Limitations: Identifying and addressing technical limitations in generative AI models is crucial for their advancement and reliability. • Contextual Understanding: Enhancing AI’s ability to understand and interpret context, especially in natural language processing and image recognition, is a key area for improvement. • Handling Ambiguous Data: Developing better algorithms for processing ambiguous or incomplete data sets is essential for decision-making accuracy and reliability. • Navigating Human Judgment: Despite generative AI’s accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment. This is especially true in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes. Thus, the effectiveness of generative AI in such scenarios should be realistically assessed. 2) Future Research Directions to Enhance the Practicality of Generative AI: Future research in generative AI should focus on addressing current limitations and expanding its practical applications. • Improved Contextual Understanding: Research should aim at developing models with better contextual awareness, particularly in complex natural language and image processing tasks. • Robust Handling of Ambiguous Data: Investigating techniques for effective processing of ambiguous data is vital for advancing the decision-making capabilities of AI models. • Ethical Integration of AIGC in Legal and Political Arenas: Future research should focus on the ethical integration of AI-generated content into legal and political decision-making processes, which involves developing frameworks that utilize AIGC in a supportive role, ensuring it enhances human judgment and contributes to transparency and fairness [324]. Importantly, researchers should consider the biases and limitations inherent in AI [324], alongside the potential for human fallibility, ethical complexities, and possible corruption in these domains. 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 IX. PRACTICAL IMPLICATIONS AND LIMITATIONS OF GENERATIVE AI TECHNOLOGIES Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. A. Computational Complexity and Real-world Applications of Generative AI Technologies A. Computational Complexity and Real-world Applications of Generative AI Technologies 1) Computational Complexity: Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. 1) Computational Complexity: • Processing Power Requirements: Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power [321]. The demand for GPUs and TPUs is accentuated, particularly when handling complex computations and large datasets typical in multimodal AI applications. • Processing Power Requirements: • Memory Usage in AI Modeling: A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements. Unlike computer RAM, VRAM often cannot be expanded easily on many platforms, posing significant constraints. Developing strategies for GPU and VRAM optimization and efficient model scaling is thus crucial for the practical deployment of these AI technologies. • Memory Usage in AI Modeling: • Scalability and Efficiency in AI Deployment: Addressing scalability challenges in generative AI, especially in MoE and AGI contexts, involves optimizing load management and parallel processing techniques. This is vital for their practical application in fields like healthcare, finance, and education. • Scalability and Efficiency in AI Deployment: 2) Real-world Application Examples of Generative AI Technologies: The application of generative AI models in real-world scenarios demonstrates their transformative potential and challenges in various sectors. 2) Real-world Application Examples of Generative AI Technologies: • Healthcare: In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information [322]. • Healthcare: • Finance: The use of AI for fraud detection and algorithmic trading in finance underlines its efficiency and accuracy, while at the same time, it raises ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability [323]. • Finance: • Education: Generative AI’s role in creating personalized learning experiences offers immense benefits in terms of educational accessibility and tailored instruction. However, it poses challenges in equitable access to technology, potential biases in AI-Generated Content (AIGC), and could reduce demand for human educators. Additionally, there’s a growing concern about educators who are against the use of AIGC, fearing it may undermine traditional teaching methodologies and the role of educators. • Education: B. Commercial Viability and Industry Solutions in Generative AI Technologies B. Commercial Viability and Industry Solutions in Generative AI Technologies 1) Market Readiness: Assessing the market readiness of generative AI technologies involves analyzing cost, accessibility, deployment challenges, and user adoption trends. 1) Market Readiness: • Cost Analysis: The financial aspects of deploying generative AI, including MoE, multimodality, and AGI, are crucial for market adoption. • Cost Analysis: • Accessibility and Deployment: Integration of these technologies into existing systems and the technical expertise required are key factors influencing their adoption. • Accessibility and Deployment: • User Adoption Trends: Understanding current adoption patterns provides insights into market acceptance and the role of user trust and perceived benefits. • User Adoption Trends: 2) Existing Industry Solutions: Generative AI is reshaping various industries by offering innovative solutions and altering market dynamics. 2) Existing Industry Solutions: • Sector-Wise Deployment: The diverse applications of generative AI, from digital content creation to process streamlining, also raise questions about originality and intellectual property rights. • Sector-Wise Deployment: • Impact on Market Dynamics: The effect of AI solutions on traditional industry structures and the introduction of novel business models are significant considerations. • Impact on Market Dynamics: • Challenges and Constraints: Addressing limitations such as scalability, data management complexity, privacy concerns, and ethical implications is essential for robust governance frameworks. • Challenges and Constraints: C. Limitations and Future Directions in Generative AI Technologies C. Limitations and Future Directions in Generative AI Technologies 1) Technical Limitations: Identifying and addressing technical limitations in generative AI models is crucial for their advancement and reliability. 1) Technical Limitations: • Contextual Understanding: Enhancing AI’s ability to understand and interpret context, especially in natural language processing and image recognition, is a key area for improvement. • Contextual Understanding: • Handling Ambiguous Data: Developing better algorithms for processing ambiguous or incomplete data sets is essential for decision-making accuracy and reliability. • Handling Ambiguous Data: • Navigating Human Judgment: Despite generative AI’s accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment. This is especially true in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes. Thus, the effectiveness of generative AI in such scenarios should be realistically assessed. • Navigating Human Judgment: 2) Future Research Directions to Enhance the Practicality of Generative AI: Future research in generative AI should focus on addressing current limitations and expanding its practical applications. 2) Future Research Directions to Enhance the Practicality of Generative AI: • Improved Contextual Understanding: Research should aim at developing models with better contextual awareness, particularly in complex natural language and image processing tasks. • Improved Contextual Understanding: • Robust Handling of Ambiguous Data: Investigating techniques for effective processing of ambiguous data is vital for advancing the decision-making capabilities of AI models. • Robust Handling of Ambiguous Data: • Ethical Integration of AIGC in Legal and Political Arenas: Future research should focus on the ethical integration of AI-generated content into legal and political decision-making processes, which involves developing frameworks that utilize AIGC in a supportive role, ensuring it enhances human judgment and contributes to transparency and fairness [324]. Importantly, researchers should consider the biases and limitations inherent in AI [324], alongside the potential for human fallibility, ethical complexities, and possible corruption in these domains. • Ethical Integration of AIGC in Legal and Political Arenas: 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

Surveying the Evolution and Future Trajectory of Generative AI - Implications and Limitations of AI

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

Surveying the Evolution and Future Trajectory of Generative AI - Research Priorities in AI

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 VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. 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 VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. 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

Surveying the Evolution and Future Trajectory of Generative AI - Analysis on Research Taxonomy

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 VI. PROJECTED CAPABILITIES OF AGI AGI stands as a transformative leap in AI, endeavoring to mirror human cognitive abilities in a software paradigm (Fig. 6). AGI’s evolution is marked by advanced self-learning capabilities, utilizing policy neural networks and sophisticated reinforcement learning techniques for autonomous adaptation. The integration of algorithms like Tree/Graph of Thought with these networks suggests a future where AGI can independently acquire and apply knowledge across diverse domains. A. Revolution in Autonomous Learning AGI is anticipated to revolutionize self-learning and exploration [282], [307], [283], [32]. By incorporating methods like PPO, AGI models are positioned to achieve a level of autonomous learning and problem-solving that exceeds the current AI models’ dependence on training data, indicating a potential paradigm shift towards reducing the need for frequent retraining and facilitating dynamic adaptation in response to evolving scenarios [181], [308]. B. Broadening of Cognitive Abilities Envisaged to integrate various architectures, AGI could promise a level of general intelligence that replicates the multifaceted nature of human cognition [282], [309]. The universal adapter approach, mirroring models like GPT and BERT, could facilitate rapid assimilation of diverse information, positioning AGI as a system capable of performing tasks across multiple domains with an adaptability akin to human intellect [282], [310]. While AGI’s full capabilities remain speculative, current trends suggest its potential application in advanced healthcare diagnostics, which is evidenced by recent breakthroughs in AIdriven predictive medicine models, indicating AGI’s potential to revolutionize medical diagnosis and treatment. C. Elevating Understanding and Interaction AGI is projected to achieve an unparalleled understanding of human language and socio-emotional subtleties, leveraging algorithms like those in transformer architectures, which would enable AGI to engage in complex, empathetic, and contextually aware interactions, suggesting potential applications that revolutionize how AI systems communicate and interact [282], [307], [311]. D. Advanced Common Sense Reasoning Symbolic AI and probabilistic reasoning, integrated into AGI, could imbue these systems with an innate grasp of common sense, to bridge the gap between artificial and natural intelligence, enabling AGI to navigate and respond effectively to real-world scenarios with reasoning aligned closely with human thought processes [282], [312], [313]. E. Holistic Integration of Knowledge AGI’s potential in integrating extensive real-world knowledge, guided by formal verification systems, hints at future capabilities where AGI’s outputs are not only accurate but ethically grounded, suggesting AGI’s ability for responsible interaction with real-world complexities [282], [311]. The projected capabilities of AGI extend to addressing significant global challenges, such as climate change, in which AGI’s advanced data analysis and predictive modeling can play a better and more crucial role in environmental monitoring, forecasting climate patterns, and devising sustainable solutions, contributing significantly to global ecological efforts [282], [283], [32]. F. Challenges and Opportunities in AGI Development The development of AGI encompasses both challenges and opportunities. While AGI promises productivity boosts in creative fields and innovations in cross-modal generation techniques, substantial challenges like data bias, computational efficiency, and ethical implications persist [15], [32]. These challenges necessitate a balanced approach in AGI development, focusing on data curation, efficient systems, and societal impacts [309]. In the context of AGI development, experts from various domains caution against overestimating current AI capabilities and highlight the gap between the theoretical framework of AGI and the practical realities of today’s AI [314], [32]. The envisioned autonomy and cognitive abilities of AGI separate it from current AI models, suggesting a future where AI systems could perform tasks across various domains without human intervention [282]. This development trajectory underscores the importance of ethical considerations and technological breakthroughs in AGI’s journey towards becoming a transformative force in society [15], [32]. While projecting the timeline for achieving true AGI remains speculative, recognizing potential roadblocks is crucial, such as the current limitations in computational power, and the complexity of replicating human-like cognitive abilities. These emphasize the need for sustained research and ethical considerations in the pursuit of AGI, ensuring responsible and conscientious development. 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 VI. PROJECTED CAPABILITIES OF AGI AGI stands as a transformative leap in AI, endeavoring to mirror human cognitive abilities in a software paradigm (Fig. 6). AGI’s evolution is marked by advanced self-learning capabilities, utilizing policy neural networks and sophisticated reinforcement learning techniques for autonomous adaptation. The integration of algorithms like Tree/Graph of Thought with these networks suggests a future where AGI can independently acquire and apply knowledge across diverse domains. A. Revolution in Autonomous Learning A. Revolution in Autonomous Learning AGI is anticipated to revolutionize self-learning and exploration [282], [307], [283], [32]. By incorporating methods like PPO, AGI models are positioned to achieve a level of autonomous learning and problem-solving that exceeds the current AI models’ dependence on training data, indicating a potential paradigm shift towards reducing the need for frequent retraining and facilitating dynamic adaptation in response to evolving scenarios [181], [308]. B. Broadening of Cognitive Abilities B. Broadening of Cognitive Abilities Envisaged to integrate various architectures, AGI could promise a level of general intelligence that replicates the multifaceted nature of human cognition [282], [309]. The universal adapter approach, mirroring models like GPT and BERT, could facilitate rapid assimilation of diverse information, positioning AGI as a system capable of performing tasks across multiple domains with an adaptability akin to human intellect [282], [310]. While AGI’s full capabilities remain speculative, current trends suggest its potential application in advanced healthcare diagnostics, which is evidenced by recent breakthroughs in AIdriven predictive medicine models, indicating AGI’s potential to revolutionize medical diagnosis and treatment. C. Elevating Understanding and Interaction C. Elevating Understanding and Interaction AGI is projected to achieve an unparalleled understanding of human language and socio-emotional subtleties, leveraging algorithms like those in transformer architectures, which would enable AGI to engage in complex, empathetic, and contextually aware interactions, suggesting potential applications that revolutionize how AI systems communicate and interact [282], [307], [311]. D. Advanced Common Sense Reasoning D. Advanced Common Sense Reasoning Symbolic AI and probabilistic reasoning, integrated into AGI, could imbue these systems with an innate grasp of common sense, to bridge the gap between artificial and natural intelligence, enabling AGI to navigate and respond effectively to real-world scenarios with reasoning aligned closely with human thought processes [282], [312], [313]. E. Holistic Integration of Knowledge E. Holistic Integration of Knowledge AGI’s potential in integrating extensive real-world knowledge, guided by formal verification systems, hints at future capabilities where AGI’s outputs are not only accurate but ethically grounded, suggesting AGI’s ability for responsible interaction with real-world complexities [282], [311]. The projected capabilities of AGI extend to addressing significant global challenges, such as climate change, in which AGI’s advanced data analysis and predictive modeling can play a better and more crucial role in environmental monitoring, forecasting climate patterns, and devising sustainable solutions, contributing significantly to global ecological efforts [282], [283], [32]. F. Challenges and Opportunities in AGI Development F. Challenges and Opportunities in AGI Development The development of AGI encompasses both challenges and opportunities. While AGI promises productivity boosts in creative fields and innovations in cross-modal generation techniques, substantial challenges like data bias, computational efficiency, and ethical implications persist [15], [32]. These challenges necessitate a balanced approach in AGI development, focusing on data curation, efficient systems, and societal impacts [309]. In the context of AGI development, experts from various domains caution against overestimating current AI capabilities and highlight the gap between the theoretical framework of AGI and the practical realities of today’s AI [314], [32]. The envisioned autonomy and cognitive abilities of AGI separate it from current AI models, suggesting a future where AI systems could perform tasks across various domains without human intervention [282]. This development trajectory underscores the importance of ethical considerations and technological breakthroughs in AGI’s journey towards becoming a transformative force in society [15], [32]. While projecting the timeline for achieving true AGI remains speculative, recognizing potential roadblocks is crucial, such as the current limitations in computational power, and the complexity of replicating human-like cognitive abilities. These emphasize the need for sustained research and ethical considerations in the pursuit of AGI, ensuring responsible and conscientious development. 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

Surveying the Evolution and Future Trajectory of Generative AI - Projected Capabilities of AGI

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 V. SPECULATED CAPABILITIES OF Q* In the burgeoning realm of AI, the anticipated Q* project stands as a beacon of potential breakthroughs, heralding advancements that could redefine the landscape of AI capabilities (Fig. 5). A. Enhanced General Intelligence Q*’s development in the arena of general intelligence represents a paradigm shift from specialized to holistic AI, indicating a broadening of the model’s cognitive abilities akin to human intelligence. This advanced form of general intelligence involves integrating diverse neural network architectures and machine learning techniques, enabling the AI to process and synthesize multifaceted information seamlessly. The universal adapter approach, mirroring models like T0, could endow Q* with the capability to rapidly assimilate knowledge from various domains. This method allows Q* to learn adaptable module plugins, enhancing its ability to tackle new data types while preserving existing skills, leading to an AI model that combines narrow specializations into a comprehensive, adaptive, and versatile reasoning system. The corresponding quasi-mathematical formulation can be expressed as: Such advancements in AI suggest the emergence of an intelligence that not only parallels but potentially exceeds human cognitive flexibility, with far-reaching implications in facilitating cross-disciplinary innovations and complex problem-solving. The speculated capabilities of Q* bring forth complex ethical implications and governance challenges. As AI systems approach higher levels of autonomy and decision-making, it is crucial to establish robust ethical frameworks and governance structures to ensure responsible and transparent AI development. This involves mitigating potential risks associated with advanced AI capabilities, emphasizing the need for comprehensive and dynamic ethical guidelines that evolve in tandem with AI advancements. B. Advanced Self-Learning and Exploration In the realm of advanced AI development, Q* is anticipated to represent a significant evolution in self-learning and exploration capabilities. It is speculated to utilize sophisticated Policy Neural Networks (NNs), similar to those in AlphaGo, but with substantial enhancements to handle the complexities of language and reasoning tasks. These networks are expected to employ advanced reinforcement learning techniques like Proximal Policy Optimization (PPO), which stabilizes policy updates and improves sample efficiency, a crucial factor in autonomous learning. The integration of these NNs with cutting-edge search algorithms, potentially including novel iterations of Tree or Graph of Thought, is predicted to enable Q* to autonomously navigate and assimilate complex information. This approach might be augmented with graph neural networks to bolster meta-learning capacities, allowing Q* to rapidly adapt to new tasks and environments while retaining previously acquired knowledge. The corresponding quasi-mathematical formulation can be represented as: ASLE(Q∗) = RL(P NN, SA) × GNN Where: • ASLE: “Advanced Self-Learning and Exploration” • RL: to reinforcement learning algorithms, particularly Proximal Policy Optimization (PPO). • PNN: Policy Neural Networks, adapted for language and reasoning tasks. • SA: sophisticated search algorithms, like Tree or Graph of Thought. • GNN: the incorporation of Graph Neural Networks for meta-learning. • ×: the cross-functional enhancement of RL with GNN. Such capabilities indicate a model not limited to understanding existing data but equipped to actively seek and synthesize new knowledge, effectively adapting to evolving scenarios without the need for frequent retraining. This signifies a leap beyond current AI models, embedding a level of autonomy and efficiency previously unattained. C. Superior Human-Level Understanding Q*’s aspiration to achieve superior human-level understanding is speculated to hinge on an advanced integration of multiple neural networks, including a Value Neural Network (VNN), paralleling the evaluative components found in systems like AlphaGo. This network would extend beyond assessing accuracy and relevance in language and reasoning processes, delving into the subtleties of human communication. The model’s deep comprehension capabilities may be enhanced by advanced natural language processing algorithms and techniques, such as those found in transformer architectures like DeBERTa. These algorithms would empower Q* to interpret not just the text but also the nuanced socio-emotional aspects such as intent, emotion, and underlying meanings. Incorporating sentiment analysis and natural language inference, Q* could navigate layers of socio-emotional insights, including empathy, sarcasm, and attitude. The corresponding quasi-mathematical formulation can be expressed as: Where: • SHLU: “Superior Human-Level Understanding”. • VNN: the Value Neural Network, similar to evaluative components in systems like AlphaGo. • NLP: a set of advanced NLP algorithms. • ⊕: the combination of VNN evaluation with NLP algorithms. • alg: individual algorithms within the NLP set. This level of understanding, surpassing current language models, would position Q* to excel in empathetic, context aware interactions, thus enabling a new echelon of personalization and user engagement in AI applications. D. Advanced Common Sense Reasoning Q*’s anticipated development in advanced common sense reasoning is predicted to integrate sophisticated logic and decision-making algorithms, potentially combining elements of symbolic AI and probabilistic reasoning. This integration aims to endow Q* with an intuitive grasp of everyday logic and an understanding akin to human common sense, thus bridging a significant gap between artificial and natural intelligence. Enhancements in Q*’s reasoning abilities might involve graphstructured world knowledge, incorporating physics and social engines similar to those in models like CogSKR. This approach, grounded in physical reality, is expected to capture and interpret the everyday logic often absent in contemporary AI systems. By leveraging large-scale knowledge bases and semantic networks, Q* could effectively navigate and respond to complex social and practical scenarios, aligning its inferences and decisions more closely with human experiences and expectations. The corresponding quasi-mathematical formulation can be represented as: Where: • ACSR: “Advanced Common Sense Reasoning”. • LogicAI and ProbAI: symbolic AI and probabilistic reasoning components, respectively. • WorldK: the integration of graph-structured world knowledge. • ⊙: the integrated operation of these elements for common sense reasoning. E. Extensive Real-World Knowledge Integration edge is speculated to involve the use of advanced formal verification systems, which would provide a robust basis for validating its logical and factual reasoning. This method, when coupled with sophisticated neural network architectures and dynamic learning algorithms, would enable Q* to engage deeply with the complexities of the real world, transcending conventional AI limitations. Additionally, Q* might employ mathematical theorem proving techniques for validation, ensuring that its reasoning and outputs are not only accurate but also ethically grounded. The incorporation of Ethics classifiers in this process further strengthens its capacity to deliver reliable and responsible understanding and interaction with real-world scenarios. The corresponding quasi-mathematical formulation can be represented as: Where: • ERWKI: “Extensive Real-World Knowledge Integration”. • F V S: Formal Verification Systems. • NN: neural network architectures. • LT P: mathematical theorem proving for logical and factual validation. • EC: the incorporation of Ethics classifiers. • ⊗: the comprehensive integration for knowledge synthesis and ethical alignment. Furthermore, the speculated capabilities of Q* have the potential to significantly reshape the job market and labor dynamics. With its advanced functionalities, Q* could automate complex tasks, leading to a shift in job requirements and the emergence of new skill demands. This necessitates a reevaluation of workforce strategies and educational paradigms, aligning them with the evolving technological landscape and ensuring that the workforce is equipped to interact with and complement these advanced AI systems. 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 V. SPECULATED CAPABILITIES OF Q* In the burgeoning realm of AI, the anticipated Q* project stands as a beacon of potential breakthroughs, heralding advancements that could redefine the landscape of AI capabilities (Fig. 5). A. Enhanced General Intelligence A. Enhanced General Intelligence Q*’s development in the arena of general intelligence represents a paradigm shift from specialized to holistic AI, indicating a broadening of the model’s cognitive abilities akin to human intelligence. This advanced form of general intelligence involves integrating diverse neural network architectures and machine learning techniques, enabling the AI to process and synthesize multifaceted information seamlessly. The universal adapter approach, mirroring models like T0, could endow Q* with the capability to rapidly assimilate knowledge from various domains. This method allows Q* to learn adaptable module plugins, enhancing its ability to tackle new data types while preserving existing skills, leading to an AI model that combines narrow specializations into a comprehensive, adaptive, and versatile reasoning system. The corresponding quasi-mathematical formulation can be expressed as: Such advancements in AI suggest the emergence of an intelligence that not only parallels but potentially exceeds human cognitive flexibility, with far-reaching implications in facilitating cross-disciplinary innovations and complex problem-solving. The speculated capabilities of Q* bring forth complex ethical implications and governance challenges. As AI systems approach higher levels of autonomy and decision-making, it is crucial to establish robust ethical frameworks and governance structures to ensure responsible and transparent AI development. This involves mitigating potential risks associated with advanced AI capabilities, emphasizing the need for comprehensive and dynamic ethical guidelines that evolve in tandem with AI advancements. B. Advanced Self-Learning and Exploration B. Advanced Self-Learning and Exploration In the realm of advanced AI development, Q* is anticipated to represent a significant evolution in self-learning and exploration capabilities. It is speculated to utilize sophisticated Policy Neural Networks (NNs), similar to those in AlphaGo, but with substantial enhancements to handle the complexities of language and reasoning tasks. These networks are expected to employ advanced reinforcement learning techniques like Proximal Policy Optimization (PPO), which stabilizes policy updates and improves sample efficiency, a crucial factor in autonomous learning. The integration of these NNs with cutting-edge search algorithms, potentially including novel iterations of Tree or Graph of Thought, is predicted to enable Q* to autonomously navigate and assimilate complex information. This approach might be augmented with graph neural networks to bolster meta-learning capacities, allowing Q* to rapidly adapt to new tasks and environments while retaining previously acquired knowledge. The corresponding quasi-mathematical formulation can be represented as: ASLE(Q∗) = RL(P NN, SA) × GNN Where: • ASLE : “Advanced Self-Learning and Exploration” ASLE • RL : to reinforcement learning algorithms, particularly Proximal Policy Optimization (PPO). RL • PNN : Policy Neural Networks, adapted for language and reasoning tasks. • PNN • SA : sophisticated search algorithms, like Tree or Graph of Thought. • SA • GNN : the incorporation of Graph Neural Networks for meta-learning. • GNN • ×: the cross-functional enhancement of RL with GNN. Such capabilities indicate a model not limited to understanding existing data but equipped to actively seek and synthesize new knowledge, effectively adapting to evolving scenarios without the need for frequent retraining. This signifies a leap beyond current AI models, embedding a level of autonomy and efficiency previously unattained. C. Superior Human-Level Understanding C. Superior Human-Level Understanding Q*’s aspiration to achieve superior human-level understanding is speculated to hinge on an advanced integration of multiple neural networks, including a Value Neural Network (VNN), paralleling the evaluative components found in systems like AlphaGo. This network would extend beyond assessing accuracy and relevance in language and reasoning processes, delving into the subtleties of human communication. The model’s deep comprehension capabilities may be enhanced by advanced natural language processing algorithms and techniques, such as those found in transformer architectures like DeBERTa. These algorithms would empower Q* to interpret not just the text but also the nuanced socio-emotional aspects such as intent, emotion, and underlying meanings. Incorporating sentiment analysis and natural language inference, Q* could navigate layers of socio-emotional insights, including empathy, sarcasm, and attitude. The corresponding quasi-mathematical formulation can be expressed as: Where: • SHLU : “Superior Human-Level Understanding”. SHLU • VNN : the Value Neural Network, similar to evaluative components in systems like AlphaGo. VNN • NLP : a set of advanced NLP algorithms. NLP • ⊕: the combination of VNN evaluation with NLP algorithms. • alg : individual algorithms within the NLP set. alg This level of understanding, surpassing current language models, would position Q* to excel in empathetic, context aware interactions, thus enabling a new echelon of personalization and user engagement in AI applications. D. Advanced Common Sense Reasoning D. Advanced Common Sense Reasoning Q*’s anticipated development in advanced common sense reasoning is predicted to integrate sophisticated logic and decision-making algorithms, potentially combining elements of symbolic AI and probabilistic reasoning. This integration aims to endow Q* with an intuitive grasp of everyday logic and an understanding akin to human common sense, thus bridging a significant gap between artificial and natural intelligence. Enhancements in Q*’s reasoning abilities might involve graphstructured world knowledge, incorporating physics and social engines similar to those in models like CogSKR. This approach, grounded in physical reality, is expected to capture and interpret the everyday logic often absent in contemporary AI systems. By leveraging large-scale knowledge bases and semantic networks, Q* could effectively navigate and respond to complex social and practical scenarios, aligning its inferences and decisions more closely with human experiences and expectations. The corresponding quasi-mathematical formulation can be represented as: Where: • ACSR : “Advanced Common Sense Reasoning”. • ACSR • LogicAI and ProbAI : symbolic AI and probabilistic reasoning components, respectively. • LogicAI ProbAI • WorldK : the integration of graph-structured world knowledge. • WorldK • ⊙: the integrated operation of these elements for common sense reasoning. E. Extensive Real-World Knowledge Integration E. Extensive Real-World Knowledge Integration edge is speculated to involve the use of advanced formal verification systems, which would provide a robust basis for validating its logical and factual reasoning. This method, when coupled with sophisticated neural network architectures and dynamic learning algorithms, would enable Q* to engage deeply with the complexities of the real world, transcending conventional AI limitations. Additionally, Q* might employ mathematical theorem proving techniques for validation, ensuring that its reasoning and outputs are not only accurate but also ethically grounded. The incorporation of Ethics classifiers in this process further strengthens its capacity to deliver reliable and responsible understanding and interaction with real-world scenarios. The corresponding quasi-mathematical formulation can be represented as: Where: • ERWKI : “Extensive Real-World Knowledge Integration”. • ERWKI • F V S: Formal Verification Systems. • F V S: • NN : neural network architectures. • NN • LT P : mathematical theorem proving for logical and factual validation. • LT P • EC : the incorporation of Ethics classifiers. • EC • ⊗: the comprehensive integration for knowledge synthesis and ethical alignment. Furthermore, the speculated capabilities of Q* have the potential to significantly reshape the job market and labor dynamics. With its advanced functionalities, Q* could automate complex tasks, leading to a shift in job requirements and the emergence of new skill demands. This necessitates a reevaluation of workforce strategies and educational paradigms, aligning them with the evolving technological landscape and ensuring that the workforce is equipped to interact with and complement these advanced AI systems. 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

Surveying the Evolution and Future Trajectory of Generative AI - Speculated Capabilities of Q*

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 IV. INNOVATIVE HORIZON OF MOE The MoE model architecture represents a pioneering advancement in transformer-based language models, offering unparalleled scalability and efficiency (Fig. 4). As evidenced by recent models like the 1.6 trillion parameter Switch Transformer [285] and the 8x7B parameter Mixtra [286], MoEbased designs are rapidly redefining the frontiers of model scale and performance across diverse language tasks. A. Core Concept and Structure MoE models represent a significant innovation in neural network design, offering enhanced scalability and efficiency in training and inference [287], [288], [110]. At their core, MoE models utilize a sparsity-driven architecture by replacing dense layers with sparse MoE layers comprising multiple expert networks, where each expert is dedicated to a specific subset of the training data or task, and a trainable gating mechanism dynamically allocates input tokens to these experts, thereby optimizing computational resources and effectively adapting to the task’s complexity [94], [21], [110]. MoE models demonstrate a substantial advantage in terms of pretraining speed, outperforming dense models [94], [287]. However, they face challenges in fine-tuning and require substantial memory for inference due to the necessity of loading all experts into Video Random Access Memory (VRAM) [289], [290], [110]. The structure of MoE involves alternating transformer layers with router layers containing gating networks for expert routing, leading to an architecture that allows significant parameter scaling and advanced specialization in problem-solving [291], [21]. A distinguishing characteristic of MoE models is their flexibility in managing large datasets, capable of amplifying model capacity by over a thousand times while only experiencing minor reductions in computational efficiency [289], [292]. The Sparsely-Gated Mixture-of-Experts Layer, a key component of these models, comprises numerous simple feed-forward expert networks and a trainable gating network responsible for expert selection, which can facilitate the dynamic and sparse activation of experts for each input instance, maintaining high computational efficiency [293], [294], [110]. Recent advancements in MoE models, such as those in the Switch Transformer, have highlighted the significant benefits of intelligent routing, when the router’s ability to intelligently route tokens to appropriate experts confers considerable advantages to MoE models, allowing them to scale up model sizes while keeping compute time constant [295], [296], [297]. Experimental evidence suggests that routers learn to route inputs according to data clusters, demonstrating their potential in real-world applications [295], [289]. The core concept and structure of MoE models lie in their dynamic routing and specialization capabilities, offering promising avenues for scaling up neural networks and enhancing their efficiency and adaptability in various tasks, but the robustness of the router must be protected against adversarial attacks [289], [298]. B. Training and Inference Efficiency MoE models, notably Mixtral 8x7B, are renowned for their superior pretraining speed compared to dense models, yet they face hurdles in fine-tuning and demand considerable VRAM for inference, owing to the requirement of loading all experts [289], [290], [110]. Recent advancements in MoE architecture have resulted in notable training cost efficiencies, especially in encoder-decoder models, with evidence showing cost savings of up to fivefold in certain contexts when compared to dense models [21], [289], [298], [287]. Innovations like DeepSpeedMoE [287] offered new architectural designs and model compression, decreasing the MoE model size by approximately 3.7x and optimizing inference to achieve up to 7.3x better latency and cost efficiency. The progression in distributed MoE training and inference, notably with innovations like Lina [299], has effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, which not only improves all-to-all communication and training step time, but also optimizes resource scheduling during inference, leading to a substantial reduction in training step time by up to 1.73 times and lowering the 95th percentile inference time by an average of 1.63 times compared to existing systems. These developments have marked a crucial shift in the large model landscape, from dense to sparse MoE models, expanding the potential applications of AI by training higher-quality models with fewer resources. C. Load Balancing and Router Optimization Effective load balancing is essential in MoE models to guarantee a uniform distribution of computational load among experts, with the router network in MoE layers, responsible for selecting the appropriate experts for processing specific tokens, playing a pivotal role in achieving this balance, which is fundamental to the stability and overall performance of MoE models [293], [289], [288], [300], [110]. Developments in router Zloss regularization techniques plays a crucial role in addressing expert imbalance in MoE models by fine-tuning the gating mechanism, ensuring a more equitable workload distribution across experts and fostering a stable training environment, thereby enhancing model performance and reducing training time and computational overhead [301], [302]. Concurrently, the integration of expert capacity management strategies, emerges as a crucial approach in MoE models to regulate the processing abilities of individual experts by setting thresholds on the number of tokens each can handle, effectively averting bottlenecks and ensuring a more efficient and streamlined model operation, leading to improved training processes and heightened performance during complex computational tasks [293], [303], [289]. D. Parallelism and Serving Techniques Recent developments in MoE models highlighted their efficiency in parallelism and serving techniques, significantly influencing large-scale neural networks. DeepSpeed-MoE, for instance, introduces advanced parallelism modes like data parallelism, tensor-slicing for non-expert parameters, and expert parallelism for expert parameters, enhancing model efficiency, as their approach optimizes both latency and throughput in MoE model inference, offering scalable solutions in production environments using multiple Graphics Processing Unit (GPU) devices [287]. MoE models, versatile in applications like multilingual tasks and coding, demonstrated impressive capabilities in handling complex tasks due to their ensemblelike structure within a single framework [304], [305], [306]. Notably, models like Mixtral and Switch Transformer, with over 1.6 trillion parameters, achieved computational efficiency equivalent to a 10 billion-parameter dense model, because they benefited from the sublinear scaling of MoE compute versus model size, leading to substantial accuracy gains within fixed compute budgets [21], [289], [287], [110]. Moreover, DeepSpeed-MoE included model compression techniques, reducing model size by up to 3.7x while maintaining accuracy, and an end-to-end MoE training and inference solution, part of the DeepSpeed library, which was instrumental in serving large-scale MoE models with enhanced speed and costefficiency [287]. These innovations open new directions in AI, shifting from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources become more widely achievable. E. Future Directions and Applications Emerging research on MoE architectures could focus on advancing sparse fine-tuning techniques, exploring instruction tuning methods, and improving routing algorithms to fully utilize performance and efficiency gains. As models scale over one billion parameters, MoE represents a paradigm shift for vastly expanding capabilities across scientific, medical, creative, and real-world applications. Frontier work could also aim to refine auto-tuning of hyperparameters during finetuning to optimize accuracy, calibration, and safety. MoE research continues to push model scale limits while maintaining specialization for transfer learning. Adaptive sparse access allows coordinating thousands of experts to cooperate on tasks ranging from reasoning to open domain dialogue. Continued analysis of routing mechanisms seeks to balance load across experts and minimize redundant computation. As the AI community further investigates MoE methods at scale, these models hold promise for new breakthroughs in language, code generation, reasoning, and multimodal applications. There is great interest in evaluating implications across education, healthcare, financial analysis, and other fields. Outcomes may yield insights not only into model optimization but also for understanding principles behind combinatorial generalization. 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 IV. INNOVATIVE HORIZON OF MOE The MoE model architecture represents a pioneering advancement in transformer-based language models, offering unparalleled scalability and efficiency (Fig. 4). As evidenced by recent models like the 1.6 trillion parameter Switch Transformer [285] and the 8x7B parameter Mixtra [286], MoEbased designs are rapidly redefining the frontiers of model scale and performance across diverse language tasks. A. Core Concept and Structure A. Core Concept and Structure MoE models represent a significant innovation in neural network design, offering enhanced scalability and efficiency in training and inference [287], [288], [110]. At their core, MoE models utilize a sparsity-driven architecture by replacing dense layers with sparse MoE layers comprising multiple expert networks, where each expert is dedicated to a specific subset of the training data or task, and a trainable gating mechanism dynamically allocates input tokens to these experts, thereby optimizing computational resources and effectively adapting to the task’s complexity [94], [21], [110]. MoE models demonstrate a substantial advantage in terms of pretraining speed, outperforming dense models [94], [287]. However, they face challenges in fine-tuning and require substantial memory for inference due to the necessity of loading all experts into Video Random Access Memory (VRAM) [289], [290], [110]. The structure of MoE involves alternating transformer layers with router layers containing gating networks for expert routing, leading to an architecture that allows significant parameter scaling and advanced specialization in problem-solving [291], [21]. A distinguishing characteristic of MoE models is their flexibility in managing large datasets, capable of amplifying model capacity by over a thousand times while only experiencing minor reductions in computational efficiency [289], [292]. The Sparsely-Gated Mixture-of-Experts Layer, a key component of these models, comprises numerous simple feed-forward expert networks and a trainable gating network responsible for expert selection, which can facilitate the dynamic and sparse activation of experts for each input instance, maintaining high computational efficiency [293], [294], [110]. Recent advancements in MoE models, such as those in the Switch Transformer, have highlighted the significant benefits of intelligent routing, when the router’s ability to intelligently route tokens to appropriate experts confers considerable advantages to MoE models, allowing them to scale up model sizes while keeping compute time constant [295], [296], [297]. Experimental evidence suggests that routers learn to route inputs according to data clusters, demonstrating their potential in real-world applications [295], [289]. The core concept and structure of MoE models lie in their dynamic routing and specialization capabilities, offering promising avenues for scaling up neural networks and enhancing their efficiency and adaptability in various tasks, but the robustness of the router must be protected against adversarial attacks [289], [298]. B. Training and Inference Efficiency B. Training and Inference Efficiency MoE models, notably Mixtral 8x7B, are renowned for their superior pretraining speed compared to dense models, yet they face hurdles in fine-tuning and demand considerable VRAM for inference, owing to the requirement of loading all experts [289], [290], [110]. Recent advancements in MoE architecture have resulted in notable training cost efficiencies, especially in encoder-decoder models, with evidence showing cost savings of up to fivefold in certain contexts when compared to dense models [21], [289], [298], [287]. Innovations like DeepSpeedMoE [287] offered new architectural designs and model compression, decreasing the MoE model size by approximately 3.7x and optimizing inference to achieve up to 7.3x better latency and cost efficiency. The progression in distributed MoE training and inference, notably with innovations like Lina [299], has effectively tackled the all-to-all communication bottleneck by enhancing tensor partitioning, which not only improves all-to-all communication and training step time, but also optimizes resource scheduling during inference, leading to a substantial reduction in training step time by up to 1.73 times and lowering the 95th percentile inference time by an average of 1.63 times compared to existing systems. These developments have marked a crucial shift in the large model landscape, from dense to sparse MoE models, expanding the potential applications of AI by training higher-quality models with fewer resources. C. Load Balancing and Router Optimization C. Load Balancing and Router Optimization Effective load balancing is essential in MoE models to guarantee a uniform distribution of computational load among experts, with the router network in MoE layers, responsible for selecting the appropriate experts for processing specific tokens, playing a pivotal role in achieving this balance, which is fundamental to the stability and overall performance of MoE models [293], [289], [288], [300], [110]. Developments in router Zloss regularization techniques plays a crucial role in addressing expert imbalance in MoE models by fine-tuning the gating mechanism, ensuring a more equitable workload distribution across experts and fostering a stable training environment, thereby enhancing model performance and reducing training time and computational overhead [301], [302]. Concurrently, the integration of expert capacity management strategies, emerges as a crucial approach in MoE models to regulate the processing abilities of individual experts by setting thresholds on the number of tokens each can handle, effectively averting bottlenecks and ensuring a more efficient and streamlined model operation, leading to improved training processes and heightened performance during complex computational tasks [293], [303], [289]. D. Parallelism and Serving Techniques D. Parallelism and Serving Techniques Recent developments in MoE models highlighted their efficiency in parallelism and serving techniques, significantly influencing large-scale neural networks. DeepSpeed-MoE, for instance, introduces advanced parallelism modes like data parallelism, tensor-slicing for non-expert parameters, and expert parallelism for expert parameters, enhancing model efficiency, as their approach optimizes both latency and throughput in MoE model inference, offering scalable solutions in production environments using multiple Graphics Processing Unit (GPU) devices [287]. MoE models, versatile in applications like multilingual tasks and coding, demonstrated impressive capabilities in handling complex tasks due to their ensemblelike structure within a single framework [304], [305], [306]. Notably, models like Mixtral and Switch Transformer, with over 1.6 trillion parameters, achieved computational efficiency equivalent to a 10 billion-parameter dense model, because they benefited from the sublinear scaling of MoE compute versus model size, leading to substantial accuracy gains within fixed compute budgets [21], [289], [287], [110]. Moreover, DeepSpeed-MoE included model compression techniques, reducing model size by up to 3.7x while maintaining accuracy, and an end-to-end MoE training and inference solution, part of the DeepSpeed library, which was instrumental in serving large-scale MoE models with enhanced speed and costefficiency [287]. These innovations open new directions in AI, shifting from dense to sparse MoE models, where training and deploying higher-quality models with fewer resources become more widely achievable. E. Future Directions and Applications E. Future Directions and Applications Emerging research on MoE architectures could focus on advancing sparse fine-tuning techniques, exploring instruction tuning methods, and improving routing algorithms to fully utilize performance and efficiency gains. As models scale over one billion parameters, MoE represents a paradigm shift for vastly expanding capabilities across scientific, medical, creative, and real-world applications. Frontier work could also aim to refine auto-tuning of hyperparameters during finetuning to optimize accuracy, calibration, and safety. MoE research continues to push model scale limits while maintaining specialization for transfer learning. Adaptive sparse access allows coordinating thousands of experts to cooperate on tasks ranging from reasoning to open domain dialogue. Continued analysis of routing mechanisms seeks to balance load across experts and minimize redundant computation. As the AI community further investigates MoE methods at scale, these models hold promise for new breakthroughs in language, code generation, reasoning, and multimodal applications. There is great interest in evaluating implications across education, healthcare, financial analysis, and other fields. Outcomes may yield insights not only into model optimization but also for understanding principles behind combinatorial generalization. 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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