**Explores transformational trends in generative AI research, with a particular focus on things like Q* and progressive AGI. The analysis highlights a key paradigm shift, which is driven by innovations like MOE, multimodal learning, and the pursuit of AGI. These advancements herald a future where AI systems can significantly expand their capabilities in reasoning, situational understanding, and creative problem-solving.
* Considers the potential of AI to both promote and hinder global equity and justice. The equitable distribution of AI benefits and its role in the decision-making process raises key questions about equity and inclusion. It is imperative to intelligently integrate AI into the fabric of society to promote justice and reduce disparities. Despite these advances, there are still several open questions and research gaps. This includes ensuring that advanced AI systems are ethically aligned with human values and social norms, a challenge exacerbated by the increasing autonomy of AI.
The safety and robustness of AGI systems in complex environments is also a significant research gap. Addressing these challenges requires a multidisciplinary approach, combining ethical, social, and philosophical perspectives. **Key areas for future interdisciplinary research in AI are highlighted, especially the combination of ethical, sociological, and technological perspectives. This approach will foster collaborative research, bridge the gap between technological progress and societal needs, and ensure that AI development is aligned with human values and global well-being. MOE, multi-modal, and AGIThe role in reshaping generative AI has been confirmed to be important, as their advancements can enhance the performance and versatility of models and pave the way for future research areas such as ethical AI conditioning and AGI. As we continue to move forward, the balance between AI advancement and human creativity is not just a goal, but a necessity to ensure that AI's role is to act as a complementary force to enhance our ability to innovate and solve complex challenges. It is our responsibility to steer these advances in the direction of enriching the human experience, aligning technological advances with ethical standards and social well-being.
The historical origins of AI can be traced back to Alan Turing's "imitation game", early computational theories, and the development of the first generation of neural networks and machine learning, which laid the foundation for today's advanced models. As can be seen from key moments such as the rise of deep learning and reinforcement learning, this development has played a crucial role in shaping the trends of contemporary AI, including hybrid expert (MOE) models and multimodal AI systems, demonstrating the dynamic and evolving nature of the field. These advancements bear witness to the dynamic and continuously evolving nature of AI technology.
With the advent of large language models (LLMs), especially ChatGPT developed by OpenAI and Gemini recently launched by Google, the development of artificial intelligence (AI) has reached a critical turning point. Not only has this technology revolutionized industry and academia, but it has also reignited the conversation about AI awareness and its potential threat to humanity. The development of compelling advanced AI systems, including Anthropic's Claude and now Gemini, has reshaped the research landscape compared to earlier models such as GPT-3 and Google's own Lamda. Gemini's ability to learn from two-way conversations and its "spike-plate" attention approach, which allows it to focus on relevant contextual parts in multiple rounds of conversations, represents a major leap forward in developing models that can better handle multi-domain conversational applications. These LLM innovations, including the hybrid expert approach employed by Gemini, herald the evolution of models that can handle a wide range of inputs and facilitate a multimodal approach. Against this backdrop, speculation has surfaced about an OpenAI project called Q* (Q-Star), which allegedly combines the power of LLMs with sophisticated algorithms such as Q-Learning and A* (A-Star Algorithm) to further facilitate a dynamic research environment.
Changes in AI research trends
As the LLM field has evolved, a large number of studies have surfaced, represented by innovations such as Gemini and Q*, with the aim of charting future research paths that vary from identifying emerging trends to highlighting areas of rapid progress. The dichotomy between established methods and early adoptions is evident, and with the advent of Gemini, the "hot topics" in LLM research are increasingly leaning towards multimodal functions and conversation-driven learning. The dissemination of preprints accelerates knowledge sharing, but it also carries the risk of reducing academic scrutiny. The inherent bias noted by Retraction Watch and concerns about plagiarism and counterfeiting pose substantial obstacles. As a result, academia is at a crossover point where there is a need for a unified push to refine research directions in line with the rapid development of the field, which seems to be traced in part by the changes in the popularity of different research keywords over time. The commercial success of GPT and ChatGPT has a significant impact on the release of generative models. As shown in Figure 1, the rise and fall of certain keywords appears to be associated with major industry milestones, such as the release of the "Transformer" model in 2017, the GPT model in 2018, and the commercial ChatGPT-3 in December 20225。For example, the search spike associated with "deep learning" coincided with breakthroughs in neural network applications, while interest in "natural language processing" surged as models such as GPT and LLAMA redefined the possibilities of language understanding and generation.
Figure 1: Number of search results on Google Scholar by year for different keywords.
Despite some fluctuations, the continued focus on "ethics" in AI research reflects a continuing and deep-seated focus on the ethical dimension of AI, emphasizing that ethical considerations are not just a reactive measure, but an active and ongoing conversation in AI discussions. It is interesting to speculate from an academic perspective whether these trends indicate a causal relationship between technological advances driving research priorities, or whether the growing number of research itself is driving technological development.
*also** the far-reaching social and economic impact of AI advancements. Examines how AI technology is reshaping industries, changing employment patterns, and impacting socioeconomic structures. This analysis highlights the opportunities and challenges that AI presents in the modern world, highlighting its role in driving innovation and economic growth, while also considering ethical implications and potential for social disruption. Future research may lead to more definitive insights, yet the synchronized interaction between innovation and academic curiosity remains a hallmark of AI advancement.
At the same time, Arxiv on Computer Science Artificial Intelligence (CSAI) category has increased exponentially, as shown in Figure 2, which seems to indicate a paradigm shift in research dissemination within the AI community. While rapid dissemination of discoveries allows for rapid knowledge exchange, it also raises concerns about information validation. The proliferation of preprints can lead to the dissemination of unverified or biased information, as these studies do not go through the rigorous process of scrutiny and potential retraction found in peer-reviewed publications. This trend highlights the need for careful consideration and critique by the academic community, especially given the potential for such uncensored research to be cited and its findings disseminated.
Figure 2: arxivorg on csThe number of annual preprints under the AI category.
Goals
*'s impetus was the official unveiling of Gemini and speculative discussions around the Q* project, which led to a timely examination of the current state of generative AI research. Specific contributions are made to understand how hybrid expert (MOE), multimodality, and artificial general intelligence (AGI) are impacting generative AI models, and to provide detailed analysis and future direction for each of these three key areas. The goal is not to permanently speculate on undisclosed Q-STAR initiatives, but to critically assess the potential of existing research topics to become obsolete or irrelevant, while delving into emerging prospects in the rapidly changing LLM landscape. Reminiscent of the outdated nature of crypto-centric or file-entropy-based ransomware detectors, they have been obsolete by ransomware's collective shift to data theft tactics that exploit a variety of attack vectors, reducing contemporary research on crypto-ransomware to latecomer status.
Figure 3: Key development timelines in the evolution of language models.
Table I: Comprehensive classification of current generative AI and LLM research.
Advances in AI are not only expected to enhance language analysis and knowledge synthesis capabilities, but are also expected to pioneer in areas such as hybrid expert (MOE), multimodality, and artificial general intelligence (AGI), and in many areas have heralded the obsolescence of statistical-based natural language processing technologies. Still, the permanent imperative that AI aligns with human ethics and values remains a fundamental principle, and the speculative Q-STAR initiative provides an unprecedented opportunity to spark discussion about how this advancement could reconfigure the LLM research landscape. In this context, Dr. Jim Fan's (Senior Research Scientist and Head of AI** at NVIDIA) insights on Q*, particularly on the convergence of learning and search algorithms, provide valuable insight into the underlying technical construct and capabilities of such a commitment.
Figure 4: MOE Innovation Concept Map.
Figure 5: Conceptual diagram of the inferred Q-function*
Figure 6: Conceptual diagram of the expected AGI functionality.
*The research methodology involved a structured literature search using keywords such as "large language model" and "generative AI". **Use filters in several scholarly databases (e.g., IEEE XPLORE, Scopus, ACM Digital Library, ScienceDirect, Web of Science, and Proquest Central) to custom-identify relevant articles published between 2017 (release of the "Transformer" model) and 2023 (time of writing). Aiming to dissect the technological impact of Gemini and Q*, how they (and similar technologies that are now inevitably emerging) are changing the trajectory of research and revealing new horizons in the field of AI. In doing so, three emerging areas of research have been identifiedMOE, multimodality, and AGI- They will profoundly reshape the landscape of generative AI research. Adopt an investigative approach to systematically develop a research roadmap that synthesizes and analyzes current and emerging trends in generative AI.
Table II: Criteria used to analyze the impact of generative AI research.
Table III: The impact of MOE, multimodality, and AGI on generative AI research.
Figure 7: ArxivAnnual preprint submissions for different categories on org.
Figure 8: Possible convergence between traditional peer review and preprint ecosystems.
The main contributions of *are as follows:
1) A detailed examination of the development of the generative AI landscape, highlighting the advancements and innovations of technologies such as Gemini and Q*, and their broad impact in the AI space.
and 2) analyze the transformative effects of Advanced generative AI systems on academic research, and explore how these developments are changing researchers**, setting new trends, and potentially leading to the obsolescence of traditional methods.
and 3) a comprehensive assessment of the ethical, social, and technological challenges arising from the integration of generative AI into academia, highlighting the critical need to reconcile these technologies with ethical norms, ensure data privacy, and develop a comprehensive governance framework.
*Title: From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape
*Links: