Artificial Intelligence Across Domains (PUBLIC)

Overview of literature used

• Russell, Stuart J, Peter Norvig, and Ming-Wei Chang. Artificial Intelligence a Modern Approach. Fourth Edition, Global Edition. Harlow, England: Pearson, 2022. Print.
• Sammut, Claude, and Geoffrey I. Webb. Encyclopedia of Machine Learning and Data Mining. 2nd ed. Springer Publishing Company, Incorporated, 2017.
• “What Is a Neuron?,” November 22, 2016. https://qbi.uq.edu.au/brain/brain-anatomy/what-neuron.
• “What Are the Parts of the Nervous System?,” October 1, 2018. https://www.nichd.nih.gov/health/topics/neuro/conditioninfo/parts.
• Ludwig, Parker E., Vamsi Reddy, and Matthew Varacallo. “Neuroanatomy, Neurons.” In StatPearls. Treasure Island (FL): StatPearls Publishing, 2022. http://www.ncbi.nlm.nih.gov/books/NBK441977/.
• Sarker, Iqbal H. “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.” SN Computer Science 2, no. 6 (August 18, 2021): 420. https://doi.org/10.1007/s42979-021-00815-1.
• Alpaydin, Ethem. Machine Learning. The MIT Press, 2016. https://mitpress.mit.edu/9780262529518/machine-learning/.
• Russell, Stuart, and Peter Norvig. Artificial Intelligence, Global Edition : A Modern Approach. Pearson Deutschland, 2021. https://elibrary.pearson.de/book/99.150005/9781292401171.
• Chang, Anthony C. “Chapter 2 - History of Artificial Intelligence.” In Intelligence-Based Medicine, edited by Anthony C. Chang, 23–27. Academic Press, 2020. https://doi.org/10.1016/B978-0-12-823337-5.00002-0.
• Kuipers, Martijn, and Ramjee Prasad. “Journey of Artificial Intelligence.” Wireless Personal Communications 123, no. 4 (April 1, 2022): 3275–90. https://doi.org/10.1007/s11277-021-09288-0.
• Ekmekci, Perihan Elif, and Berna Arda. “History of Artificial Intelligence.” In Artificial Intelligence and Bioethics, edited by Perihan Elif Ekmekci and Berna Arda, 1–15. SpringerBriefs in Ethics. Cham: Springer International Publishing, 2020.https://doi.org/10.1007/978-3-030-52448-7_1.
• Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519
• Theodosiou, Anastasia A., and Robert C. Read. “Artificial Intelligence, Machine Learning and Deep Learning: Potential Resources for the Infection Clinician.” Journal of Infection 87, no. 4 (October 1, 2023): 287–94. https://doi.org/10.1016/j.jinf.2023.07.006.
• Hassija, Vikas, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, and Amir Hussain. “Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence.” Cognitive Computation 16, no. 1 (January 1, 2024): 45–74. https://doi.org/10.1007/s12559-023-10179-8.
• “AI’s Mysterious ‘Black Box’ Problem, Explained | University of Michigan-Dearborn.” Accessed December 15, 2024. https://umdearborn.edu/news/ais-mysterious-black-box-problem-explained.
• Panigutti, Cecilia, Ronan Hamon, Isabelle Hupont, David Fernandez Llorca, Delia Fano Yela, Henrik Junklewitz, Salvatore Scalzo, et al. “The Role of Explainable AI in the Context of the AI Act.” In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 1139–50. FAccT ’23. New York, NY, USA: Association for Computing Machinery, 2023. https://doi.org/10.1145/3593013.3594069.
• Ding, Weiping, Mohamed Abdel-Basset, Hossam Hawash, and Ahmed M. Ali. “Explainability of Artificial Intelligence Methods, Applications and Challenges: A Comprehensive Survey.” Information Sciences 615 (November 1, 2022): 238–92. https://doi.org/10.1016/j.ins.2022.10.013.
• “What Is Black Box AI and How Does It Work? | IBM,” October 29, 2024. https://www.ibm.com/think/topics/black-box-ai.
• University of Toronto. (o. J.). Geoffrey Hinton. Abgerufen am 14. Dezember 2024, von https://www.uc.utoronto.ca/staff-faculty-profile/geoffrey-hinton
• Nobel Prize. (2024). The Nobel Prize in Physics 2024 – Facts: Geoffrey Hinton. Abgerufen am 14. Dezember 2024, von https://www.nobelprize.org/prizes/physics/2024/hinton/facts/
• Technology Review. (2024, 8. Oktober). Geoffrey Hinton just won the Nobel Prize in Physics for his work on machine learning. Abgerufen am 14. Dezember 2024, von https://www.technologyreview.com/2024/10/08/1105221/geoffrey-hinton-just-won-the-nobel-prize-in-physics-for-his-work-on-machine-learning/
• Han, Xu, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, et al. “Pre-Trained Models: Past, Present and Future.” AI Open 2 (January 1, 2021): 225–50. https://doi.org/10.1016/j.aiopen.2021.08.002.