My new-fangled research contributions to Advancements in Artificial Intelligence

Polymer informatics with TransBERT. a Prediction pipeline.

Artificial Intelligence (AI) research is witnessing groundbreaking advancements, particularly in sustainability and efficiency. My Three recent research papers, “EcoGen: Fusing Generative AI and Edge Intelligence for Sustainable Scalability”, “Enhancing Neural Language Models: A Comprehensive Approach with Tensorized Transformer and Over-Parameterization,” and “ TransBERT Polymer Informatics: A Fusion of Transformer Language Modeling and Machine-Driven Chemistry for Accelerated Property Predictions” present innovative contributions at the intersection of AI and its practical applications. Let’s delve into these pioneering works.

EcoGen: Fusing Generative AI and Edge Intelligence for Sustainable Scalability

The proliferation of Generative Artificial Intelligence (GenAI) has ushered in an era of unprecedented data creation, leading to challenges in computing and communication infrastructures. The EcoGen framework emerges as a solution by integrating GenAI with edge-cloud computing, aiming for sustainable scalability.

Key Contributions:

1. Collaborative Cloud-Edge-End Intelligence Framework: EcoGen facilitates bidirectional knowledge flow between GenAI and Edge Intelligence (EI), leveraging data-free knowledge relays to buffer contradictions. This approach enables virtuous-cycle model fine-tuning and task inference.
2. Energy Efficiency and Environmental Sustainability: The paper thoroughly examines the energy efficiency and environmental implications of deploying Generative AI systems, particularly in edge computing. It proposes strategies to optimize energy consumption and reduce the carbon footprint, contributing to a more sustainable AI ecosystem.
3. Experimental Validation: Experimental results demonstrate the effectiveness of EcoGen in achieving seamless fusion and collaborative evolution between GenAI and EI, highlighting its potential for real-world scalability and sustainability.

Access this research paper here: https://www.researchgate.net/publication/378292004_EcoGen_Fusing_Generative_AI_and_Edge_Intelligence_for_Sustainable_Scalability?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInBvc2l0aW9uIjoicGFnZUNvbnRlbnQifX0

Enhancing Neural Language Models: A Comprehensive Approach with Tensorized Transformer and Over-Parameterization

The second research paper focuses on enhancing the performance and efficiency of neural language models, addressing resource limitations and scalability challenges.

Key Contributions:

1. Multi-linear Attention with Block-Term Tensor Decomposition (BTD): Introducing multi-linear attention with BTD, this approach achieves significant parameter compression while improving performance on language modeling tasks compared to traditional Transformer models.
2. TensorCoder for Dimension-Wise Attention: TensorCoder addresses the quadratic complexity of scaled dot-product attention in Transformers, making it suitable for long sequence tasks and reducing computational complexity while maintaining or surpassing performance.
3. Optimized Pre-trained Language Models (PLMs): The paper proposes a matrix product operator for over-parameterization during fine-tuning of PLMs. This approach significantly enhances the fine-tuning performance of small PLMs, enabling them to outperform larger counterparts with three times the parameters.

Access this research paper here: https://www.researchgate.net/publication/378157352_Enhancing_Neural_Language_Models_A_Comprehensive_Approach_with_Tensorized_Transformer_and_Over-Parameterization?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InByb2ZpbGUiLCJwYWdlIjoicHJvZmlsZSIsInBvc2l0aW9uIjoicGFnZUNvbnRlbnQifX0

TransBERT Polymer Informatics: A Fusion of Transformer Language Modeling and Machine-driven Chemistry for Accelerated Property Predictions.

Key Contributions:

1. Transformer-based Language Modeling for Polymer Sequences: TransPolymer utilizes self-attention mechanisms to predict polymer properties, demonstrating the effectiveness of Transformer architectures in modeling complex polymer sequences.
2. Fully Automated Polymer Informatics Pipeline: polyBERT operates as a chemical linguist, treating polymer structures as a unique language. By integrating chemical fingerprinting and multitask learning, polyBERT achieves remarkable speed and accuracy in identifying polymer candidates tailored to specific applications.
3. Synergistic Fusion: The combination of TransPolymer and polyBERT results in a robust computational tool poised to propel polymer design and structure-property relationship understanding. The fusion of Transformer language modeling and machine-driven informatics offers unparalleled efficiency in polymer property prediction.

Access this research paper here: https://www.researchgate.net/publication/376414197_TransBERT_Polymer_Informatics_A_Fusion_of_Transformer_Language_Modeling_and_Machine-Driven_Chemistry_for_Accelerated_Property_Predictions

Conclusion

These research contributions underscore the evolving landscape of AI, with a focus on sustainability, scalability, and efficiency. The EcoGen framework presents a holistic approach to integrating Generative AI with edge computing, fostering sustainable practices in AI deployment. On the other hand, advancements in neural language models through efficient attention mechanisms and over-parameterization demonstrate the potential for scaling AI models without compromising performance. Together, these works pave the way for a more sustainable and efficient AI ecosystem, driving innovation and addressing real-world challenges. The TransBERT framework represents a significant advancement in polymer informatics, offering unprecedented speed and accuracy in property predictions. By harnessing the strengths of Transformer models and machine-driven chemistry, TransBERT holds immense promise for scalable deployment in cloud infrastructures and various applications in material science. This synergistic approach contributes significantly to the advancement of polymer science and informatics, paving the way for innovative solutions in material design and characterization.

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