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

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.