A research talk was given on privacy and security issues in speech processing, highlighting the unique privacy challenges due to the biometric information embedded in speech. The talk covered the legal landscape, proposed solutions like cryptographic and hashing-based methods, and adversarial processing techniques. Dr. Bhiksha Raj from Carnegie Mellon University, an expert in speech and audio processing, delivered the talk. Why it matters: As speech-based interfaces become more prevalent in the Middle East, understanding and addressing the associated privacy risks is crucial for ethical AI development and deployment.
A PhD candidate from the University of Waterloo presented on threats from large machine learning systems at MBZUAI. The talk covered data privacy during inference and the misuse of ML systems to generate deepfakes. The speaker also analyzed differential privacy and watermarking as potential solutions. Why it matters: Understanding and mitigating the risks of large ML systems is crucial for responsible AI development and deployment in the region.
Pedro J. Moreno, former head of ASR R&D at Google, presented a talk at MBZUAI on the past, present, and future of speech technologies. The talk covered the evolution of speech tech, his career contributions including work on Google Voice search, and the impact of LLMs on speech science. He also discussed the interplay between foundational and applied research and preparing the next generation of scientists. Why it matters: The talk provides insights into the trajectory of speech technologies from a leading researcher, highlighting future directions and the ethical considerations surrounding AI's impact on society.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
A talk will present two projects related to the use of NLP for estimating a client’s depression severity and well-being. The first project examines emotional coherence between the subjective experience of emotions and emotion expression in therapy using transformer-based emotion recognition models. The second project proposes a semantic pipeline to study depression severity in individuals based on their social media posts by exploring different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. Why it matters: This research explores how NLP techniques can be applied to mental health assessment, potentially offering new tools for diagnosis and treatment monitoring.