User:Phzing/Open-source artificial intelligence/Bibliography
You will be compiling your bibliography and creating an outline of the changes you will make in this sandbox.
| Bibliography
As you gather the sources for your Wikipedia contribution, think about the following:
|
Bibliography
- Sources 1-26 contain information used for the History section
- Sources, 8, 14, and 27-30 are used in the Benefits section
- Sources 29, 31-33 are used in the Concerns section
- Sources 24-26, 32, 34-37 are used in the Improving AI models section
Examples:
|
References
Outline of proposed changes
Proposed changes were already drafted into their final state before this assignment was due. Please see my drafting page User:Phzing/Open-source artificial intelligence.
- Add an in-depth History section
- Add Benefits section
- Contribute to Concerns section, adding content about equity, social, and ethical implications
- Add section on Improving AI models with discussion about development malpractices and frameworks for improving models
[1][2][3][4][5][6][7][8][9][10][11][12][13][14] [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]
| Now that you have compiled a bibliography, it's time to plan out how you'll improve your assigned article.
In this section, write up a concise outline of how the sources you've identified will add relevant information to your chosen article. Be sure to discuss what content gap your additions tackle and how these additions will improve the article's quality. Consider other changes you'll make to the article, including possible deletions of irrelevant, outdated, or incorrect information, restructuring of the article to improve its readability or any other change you plan on making. This is your chance to really think about how your proposed additions will improve your chosen article and to vet your sources even further. Note: This is not a draft. This is an outline/plan where you can think about how the sources you've identified will fill in a content gap. |
- ^ "The Evolution of Open Source: From Software to AI : Argano". argano.com. Retrieved 2024-11-24.
- ^ Staff, Kyle Daigle, GitHub (2023-11-08). "Octoverse: The state of open source and rise of AI in 2023". The GitHub Blog. Retrieved 2024-11-24.
{{cite web}}: CS1 maint: multiple names: authors list (link) - ^ "Appendix I: A Short History of AI | One Hundred Year Study on Artificial Intelligence (AI100)". ai100.stanford.edu. Retrieved 2024-11-24.
- ^ Kautz, Henry (2022-03-31). "The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture". AI Magazine. 43 (1): 105–125. doi:10.1002/aaai.12036. ISSN 2371-9621.
- ^ "Symbolic artificial intelligence", Wikipedia, 2024-11-16, retrieved 2024-11-24
- ^ "Why Software Should Be Free - GNU Project - Free Software Foundation". www.gnu.org. Retrieved 2024-11-24.
- ^ "Richard Stallman", Wikipedia, 2024-11-19, retrieved 2024-11-24
- ^ "The Power of Collaboration: How Open-Source Projects are Advancing AI".
- ^ Code, Linux (2024-11-03). "A Brief History of Open Source". TheLinuxCode. Retrieved 2024-11-24.
- ^ Priya (2024-03-28). "The Evolution of Open Source AI Libraries: From Basement Brawls to AI All-Stars". TheGen.AI. Retrieved 2024-11-24.
- ^ "About us". scikit-learn. Retrieved 2024-11-24.
- ^ "Testimonials". scikit-learn. Retrieved 2024-11-24.
- ^ Makkar, Akashdeep (2021-06-09). "What Is Scikit-learn and why use it for machine learning?". Data Courses. Retrieved 2024-11-24.
- ^ Dean, Jeffrey (2022-05-01). "A Golden Decade of Deep Learning: Computing Systems & Applications". Daedalus. 151 (2): 58–74. doi:10.1162/daed_a_01900. ISSN 0011-5266.
- ^ "The democratization of AI: Shaping our collective future".
- ^ Costa, Carlos J.; Aparicio, Manuela; Aparicio, Sofia; Aparicio, Joao Tiago (2024-01). "The Democratization of Artificial Intelligence: Theoretical Framework". Applied Sciences. 14 (18): 8236. doi:10.3390/app14188236. ISSN 2076-3417.
{{cite journal}}: Check date values in:|date=(help)CS1 maint: unflagged free DOI (link) - ^ Singh, Kanwar Bharat; Arat, Mustafa Ali (2019). "Deep Learning in the Automotive Industry: Recent Advances and Application Examples". doi:10.48550/ARXIV.1906.08834.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Sushumna, Aparna (2024-06-10). "Deep Learning in NLP and Image Recognition". 5DataInc. Retrieved 2024-11-25.
- ^ kakkar, Yuvraj (2024-01-23). "Hugging Face 🤗: Revolutionizing AI Collaboration in the Machine Learning Community". Medium. Retrieved 2024-11-25.
- ^ "GPT-3 powers the next generation of apps".
- ^ "Generative AI vs. Large Language Models (LLMs): What's the Difference?". appian.com. Retrieved 2024-11-25.
- ^ Gujar, Praveen. "Council Post: Building Trust In AI: Overcoming Bias, Privacy And Transparency Challenges". Forbes. Retrieved 2024-11-25.
- ^ "Ethical Issues in Open-Source Intelligence | Restackio". www.restack.io. Retrieved 2024-11-25.
- ^ Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2018-10-05). "Model Cards for Model Reporting". arXiv.org. doi:10.48550/arxiv.1810.03993. Retrieved 2024-11-25.
- ^ "Google Model Cards". modelcards.withgoogle.com. Retrieved 2024-11-25.
- ^ Gao, Haoyu; Zahedi, Mansooreh; Treude, Christoph; Rosenstock, Sarita; Cheong, Marc (2024-06-26). "Documenting Ethical Considerations in Open Source AI Models". arXiv.org. doi:10.48550/arxiv.2406.18071. Retrieved 2024-11-25.
- ^ "Democratizing AI | IBM". www.ibm.com. 2024-11-05. Retrieved 2024-11-25.
- ^ Models, A. I. "Open Source AI: A look at Open Models". Open Source AI Models. Retrieved 2024-11-25.
- ^ DiChristofano, Alex; Shuster, Henry; Chandra, Shefali; Patwari, Neal (2023-02-09), Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition, doi:10.48550/arXiv.2208.01157, retrieved 2024-11-27
- ^ Gujar, Praveen. "Council Post: Building Trust In AI: Overcoming Bias, Privacy And Transparency Challenges". Forbes. Retrieved 2024-11-27.
- ^ Jacobs, Abigail Z.; Wallach, Hanna (2021-03-12), Measurement and Fairness, doi:10.48550/arXiv.1912.05511, retrieved 2024-11-27
- ^ "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification" (PDF). Proceedings of Machine Learning Research.
{{cite journal}}: line feed character in|title=at position 54 (help) - ^ Kathikar, Adhishree; Nair, Aishwarya; Lazarine, Ben (2023). "Assessing the Vulnerabilities of the Open-Source Artificial Intelligence (AI) Landscape: A Large-Scale Analysis of the Hugging Face Platform". 2023 IEEE International Conference on Intelligence and Security Informatics (ISI). pp. 1–6. doi:10.1109/ISI58743.2023.10297271. ISBN 979-8-3503-3773-0.
- ^ "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification" (PDF). Proceedings of Machine Learning Research.
{{cite journal}}: line feed character in|title=at position 54 (help) - ^ Gebru, Timnit; Morgenstern, Jamie; Vecchione, Briana; Vaughan, Jennifer Wortman; Wallach, Hanna; Daumé III, Hal; Crawford, Kate (2021-12-01), Datasheets for Datasets, doi:10.48550/arXiv.1803.09010, retrieved 2024-11-25
- ^ White, Matt; Haddad, Ibrahim; Osborne, Cailean; Liu, Xiao-Yang Yanglet; Abdelmonsef, Ahmed; Varghese, Sachin; Hors, Arnaud Le (2024-10-18), The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence, doi:10.48550/arXiv.2403.13784, retrieved 2024-11-25
- ^ Gujar, Praveen. "Council Post: Building Trust In AI: Overcoming Bias, Privacy And Transparency Challenges". Forbes. Retrieved 2024-11-27.
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