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

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

XI. CONCLUSIONS

This roadmap survey has embarked on an exploration of the transformative trends in generative AI research, particularly focusing on speculated advancements like Q* and the progressive strides towards AGI. Our analysis highlights a crucial paradigm shift, driven by innovations such as MoE, multimodal learning, and the pursuit of AGI. These advancements signal a future where AI systems could significantly extend their capabilities in reasoning, contextual understanding, and creative problem-solving. This study reflects on AI’s dual potential to either contribute to or impede global equity and justice. The equitable distribution of AI benefits and its role in decision-making processes raise crucial questions about fairness and inclusivity. It is imperative to thoughtfully integrate AI into societal structures to enhance justice and reduce disparities. Despite these advancements, several open questions and research gaps remain. These include ensuring the ethical alignment of advanced AI systems with human values and societal norms, a challenge compounded by their increasing autonomy. The safety and robustness of AGI systems in diverse environments also remain a significant research gap. Addressing these challenges requires a multidisciplinary approach, incorporating ethical, social, and philosophical perspectives.

Our survey has highlighted key areas for future interdisciplinary research in AI, emphasizing the integration of ethical, sociological, and technical perspectives. This approach will foster collaborative research, bridging the gap between technological advancement and societal needs, ensuring that AI development is aligned with human values and global welfare. The roles of MoE, multimodal, and AGI in reshaping generative AI have been identified as significant, as their advancements can enhance model performance and versatility, and pave the way for future research in areas like ethical AI alignment and AGI. As we forge ahead, the balance between AI advancements and human creativity is not just a goal but a necessity, ensuring AI’s role as a complementary force that amplifies our capacity to innovate and solve complex challenges. Our responsibility is to guide these advancements towards enriching the human experience, aligning technological progress with ethical standards and societal well-being.

DISCLAIMER

The authors hereby declare no conflict of interest.

ABBREVIATIONS

AGI Artificial General Intelligence

AI Artificial Intelligence

AIGC AI-generated content

BERT Bidirectional Encoder Representations from Transformers

CCPA California Consumer Privacy Act

DQN Deep Q-Networks

EU European Union

GAN Generative Adversarial Network

GDPR General Data Protection Regulation

GPT Generative Pre-trained Transformers

GPU Graphics Processing Unit

LIDAR Light Detection and Ranging

LLM Large Language Model

LSTM Long Short-Term Memory

MCTS Monte Carlo Tree Search

ML Machine Learning

MoE Mixture of Experts

NLG Natural Language Generation

NLP Natural Language Processing

NLU Natural Language Understanding

NN Neural Network

PPO Proximal Policy Optimization

RNNs Recurrent Neural Networks

VNN Value Neural Network

VRAM Video Random Access Memory

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