Trinity

Iyer, C. V. Krishnakumar; Hou, Feili; Wang, Henry; Wang, Yonghong; Oh, Kay; Ganguli, Swetava; Pandey, Vipul

doi:10.1145/3486635.3491072

Cited by 19 publications

(5 citation statements)

References 5 publications

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“…No-code AI platforms [614] may also assist in democratizing AI initiatives [615], by providing a user-friendly interface that allows users to build and deploy ML models without any coding experience [616]. No-code AI can be used to leverage machine learning operations (MLOps) [617], to ensure models are deployed and managed effectively in production.…”

Section: B Ethical Considerationsmentioning

confidence: 99%

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

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<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. Large language models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, and question-answering. This survey paper provides a comprehensive overview of LLMs, including their history, architecture, training methods, applications, and challenges. The paper begins by discussing the fundamental concepts of generative AI and the architecture of generative pre- trained transformers (GPT). It then provides an overview of the history of LLMs, their evolution over time, and the different training methods that have been used to train them. The paper then discusses the wide range of applications of LLMs, including medical, education, finance, and engineering. It also discusses how LLMs are shaping the future of AI and how they can be used to solve real-world problems. The paper then discusses the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, and computational resource requirements. It also highlights techniques for enhancing the robustness and controllability of LLMs, and addressing bias, fairness, and generation quality issues. Finally, the paper concludes by highlighting the future of LLM research and the challenges that need to be addressed in order to make LLMs more reliable and useful. This survey paper is intended to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of LLMs, their evolution, applications, and challenges. By consolidating the state-of-the-art knowledge in the field, this survey serves as a valuable resource for further advancements in the development and utilization of LLMs for a wide range of real-world applications. The GitHub repo for this project is available at https://github.com/anas-zafar/LLM-Survey</p>

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Section: B Ethical Considerationsmentioning

confidence: 99%

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

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show abstract

“…Acceleration of the development cycle [1], [5], [6], [10], [11], [13], [14], [19], [26], [27], [29], [32], [35], [40]- [42] BE2 Involvement of citizen developers [1], [5], [6], [10]- [12], [14], [19], [26], [35], [38], [41]- [43] BE3 Decreased costs [1], [5], [6], [11], [14], [19], [27], [32], [35], [40], [44]…”

Section: Be1mentioning

confidence: 99%

Exploring Low-Code Development: A Comprehensive Literature Review

Rokis,

Kirikova

2023

CSIMQ

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Low-code development provides the ability to create software using visual application development tools, predefined components, and automation, minimizing the reliance on manual coding. Furthermore, it enables individuals with limited programming expertise to actively contribute to software development in collaboration with information technology (IT) professionals, improving the alignment between customer or business requirements and the developed solution. This approach speeds up the development process, fosters closer collaboration between software development and business teams, and enhances the organization’s ability to respond to business and market demands or changes. In this article, existing knowledge of low-code development from multiple perspectives is summarized, including the definitions, used tools, applied development lifecycles, application areas, potential benefits, challenges, and, consequently, relevant development and delivery principles.

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“…Then, users can export the model as a snippet of JavaScript code to be employed in any project. Iyer et al propose Trinity, a web interface to analyze spatial data [4], automatically creating binary and multi-classes classificators. Data are pre-processed and prepared for CNNbased learning, and visualizations are returned to users.…”

Section: Interactive Machine Learningmentioning

confidence: 99%

“…Data Science tasks are often challenging also for both novice and experienced data scientists. Every dataset is different and requires a tailored sequence of operations to extract useful knowledge [4]. Researchers may adopt a non-optimal analysis pipeline or struggle with unappropriated tools or methods, often obtaining inaccurate if not erroneous results.…”

Section: Introductionmentioning

confidence: 99%

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

et al. 2023

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Data Science is increasingly applied for solving real-life problems, in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market. Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this scenario, we present DSBot, an assistant that can analyze the user data and produce answers by mastering several Data Science techniques. DSBot understands the research question with the help of conversation interaction, produces a data science pipeline and automatically executes the pipeline in order to generate analysis. The strength of DSBot lies in the design of a rich domain specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology provided by a conversational agent. We empirically evaluated the translation capabilities and the autoML performances of the system. In the translation task, it obtains a BLEU score of 0.8. In prediction tasks, DSBot outperforms TPOT, an autoML tool, in 19 datasets out of 30.

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Trinity

Cited by 19 publications

References 5 publications

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Exploring Low-Code Development: A Comprehensive Literature Review

Ask Your Data—Supporting Data Science Processes by Combining AutoML and Conversational Interfaces

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