This project-based seminar explores the fundamentals and applications of Generative AI across multiple data domains, including text, images, audio, video, structured, and multimodal data. Through a combination of theory and practical experience, students will develop hands-on projects that demonstrate their understanding of generative AI techniques and their applicability to real-world scenarios. The course emphasizes the end-to-end project lifecycle, from literature review and project planning to the implementation, evaluation, and reporting of generative AI systems. Students will present their work twice, providing a platform for peer feedback and iterative improvement, and will submit final project reports.
The course discusses advanced topics in artificial intelligence, some have social and philosophical implications. We start with relatively new ideas such as adversarial attack, adversarial training, and graph neural networks and their use in science. We then present several topics concerning ethics, such as bias, fairness, privacy and transparency. The course will present several definitions of fairness and their corresponding algorithms, protocols for ‘differential privacy’ and other methods for privacy protection. We will then present the notion of explainable A.I., trying to reverse engineer neural networks, either internally by tuning internal components of the network or by a post-hoc analysis of the network outcome. On the opposite side graphical models are completely transparent, we will present basic algorithms to compute marginal probabilities in such networks, and then present large graphical models used in medicine. Next we will present artificial social algorithms, such as affective computing, body motion detection and more. One of the main question in A.I. is the possibility of Artificial General Intelligence. We will discuss the main ideas underneath AGI and some of their criticism. The course ends with the topic of Value Alignment, i.e. imprinting human values in algorithms.