A computer science vision involves computing devices becoming proactive assistants, enhancing various aspects of life through user digitization. Current devices provide coarse digital representations of users, but there's significant potential for improvement. Karan, a Ph.D. candidate at CMU, develops technologies for consumer devices to capture richer user representations without sacrificing practicality. Why it matters: Advancements in user digitization can lead to improved extended reality experiences, health tracking, and more productive work environments, enhancing the utility of consumer devices.
Researchers studied user lifetime prediction in the location-based social network Jodel within Saudi Arabia, leveraging its disjoint communities. Machine learning models, particularly Random Forest, were trained to predict user lifetime as a regression and classification problem. A single countrywide model generalizes well and performs similarly to community-specific models.
This paper presents six experiments evaluating personalization and user tracking in web search engine results. The experiments involve comparing search results based on VPN location (including UAE vs others), logged-in status, network type, search engine, browser, and trained Google accounts. The study measures total hits, first hit, and correlation between hits to identify patterns of personalization. Why it matters: The findings shed light on the extent of filter bubble effects and potential biases in search results for users in the UAE and globally.
Christian Montag from Ulm University gave a talk about assessing attitudes towards AI, covering the IMPACT framework (Modality, Person, Area, Country/Culture, and Transparency). He discussed how factors like age, gender, personality, and culture relate to attitudes toward AI, and how those attitudes link to trust in automation and specific AI models like ChatGPT and Ernie Bot. Montag's research explores the intersection of psychology, neuroscience, behavioral economics, and computer science, focusing on the impact of AI on the human mind. Why it matters: Understanding public perception of AI is crucial for responsible development and deployment, especially in the Arab world where cultural and demographic factors can significantly shape attitudes.
KAUST's Image and Video Understanding Lab is developing machine learning algorithms for computer vision and object tracking, with applications in video content search and UAV navigation. Their algorithms can detect specific activities in videos, helping platforms detect unwanted content and deliver relevant ads. The object tracking algorithm is also used to empower UAVs, enabling them to follow objects autonomously. Why it matters: This research enhances video content analysis and UAV capabilities, positioning KAUST as a leader in computer vision and AI applications within the region.
This paper introduces a mutually-regularized dual collaborative variational auto-encoder (MD-CVAE) for recommendation systems, addressing the limitations of user-oriented auto-encoders (UAEs) in handling sparse ratings and new items. MD-CVAE integrates item content and user ratings within a variational framework, regularizing UAE weights with item content to avoid non-optimal convergence. A symmetric inference strategy eliminates the need for retraining when introducing new items, enhancing efficiency in dynamic recommendation scenarios. Why it matters: The MD-CVAE approach offers a practical solution for improving recommendation accuracy and efficiency, especially in scenarios with data sparsity and frequent item updates, relevant to e-commerce and content platforms in the Middle East.
KAUST Discovery Professor Jesper Tegnér collaborated with UK researchers to develop algorithms explaining decision-making in insects and rats. Assoc. Prof. Robert Hoehndorf's lab introduced a tool for identifying genetic variants linked to rare diseases based on patient symptoms. KAUST scientists also studied monkeypox infection of human skin using stem cells and marine microbiome adaptation to thermal changes. Why it matters: These diverse research projects highlight KAUST's contributions to computational biology, virology, and marine science, advancing knowledge with implications for healthcare and environmental challenges.
MBZUAI held its inaugural Human-Computer Interaction (HCI) Symposium in Abu Dhabi, focusing on the human and societal impacts of AI. The event, led by Professor Elizabeth Churchill, featured workshops and keynotes from figures like Google's Matias Duarte. Participants collaborated to address critical design aspects of human-AI interaction and co-author a book. Why it matters: The symposium highlights the increasing importance of human-centered design in AI development, ensuring AI tools are useful, desirable, and beneficial for society in the GCC region and beyond.