The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field
Guardat en:
| Publicat a: | Big Data and Cognitive Computing vol. 7, no. 2 (2023), p. 62 |
|---|---|
| Autor principal: | |
| Altres autors: | |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2829699489 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc7020062 |2 doi | |
| 035 | |a 2829699489 | ||
| 045 | 2 | |b d20230101 |b d20231231 | |
| 100 | 1 | |a Hassani, Hossein |u The Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 19395-4697, Iran | |
| 245 | 1 | |a The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field | |
| 260 | |b MDPI AG |c 2023 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a ChatGPT, a conversational AI interface that utilizes natural language processing and machine learning algorithms, is taking the world by storm and is the buzzword across many sectors today. Given the likely impact of this model on data science, through this perspective article, we seek to provide an overview of the potential opportunities and challenges associated with using ChatGPT in data science, provide readers with a snapshot of its advantages, and stimulate interest in its use for data science projects. The paper discusses how ChatGPT can assist data scientists in automating various aspects of their workflow, including data cleaning and preprocessing, model training, and result interpretation. It also highlights how ChatGPT has the potential to provide new insights and improve decision-making processes by analyzing unstructured data. We then examine the advantages of ChatGPT’s architecture, including its ability to be fine-tuned for a wide range of language-related tasks and generate synthetic data. Limitations and issues are also addressed, particularly around concerns about bias and plagiarism when using ChatGPT. Overall, the paper concludes that the benefits outweigh the costs and ChatGPT has the potential to greatly enhance the productivity and accuracy of data science workflows and is likely to become an increasingly important tool for intelligence augmentation in the field of data science. ChatGPT can assist with a wide range of natural language processing tasks in data science, including language translation, sentiment analysis, and text classification. However, while ChatGPT can save time and resources compared to training a model from scratch, and can be fine-tuned for specific use cases, it may not perform well on certain tasks if it has not been specifically trained for them. Additionally, the output of ChatGPT may be difficult to interpret, which could pose challenges for decision-making in data science applications. | |
| 610 | 4 | |a OpenAI | |
| 653 | |a Language | ||
| 653 | |a Text categorization | ||
| 653 | |a Scientists | ||
| 653 | |a Deep learning | ||
| 653 | |a Trends | ||
| 653 | |a Chatbots | ||
| 653 | |a Machine learning | ||
| 653 | |a Workflow | ||
| 653 | |a Data science | ||
| 653 | |a Plagiarism | ||
| 653 | |a Privacy | ||
| 653 | |a Decision making | ||
| 653 | |a Language translation | ||
| 653 | |a Algorithms | ||
| 653 | |a Training | ||
| 653 | |a Big Data | ||
| 653 | |a Decision analysis | ||
| 653 | |a Data mining | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Neural networks | ||
| 653 | |a Natural language processing | ||
| 653 | |a Unstructured data | ||
| 653 | |a Synthetic data | ||
| 653 | |a Conversation | ||
| 653 | |a Conversational artificial intelligence | ||
| 653 | |a Classification | ||
| 653 | |a Cleaning | ||
| 653 | |a Data | ||
| 653 | |a Language acquisition | ||
| 653 | |a Data processing | ||
| 653 | |a Interfaces | ||
| 653 | |a Intelligence | ||
| 653 | |a Productivity | ||
| 653 | |a Translation | ||
| 700 | 1 | |a Silva, Emmanuel Sirmal |u Fashion Business School, London College of Fashion, University of the Arts London, London W1G 0BJ, UK | |
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 7, no. 2 (2023), p. 62 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2829699489/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2829699489/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2829699489/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |