This article discusses conscious investing and its potential in the Middle East, particularly in light of unprecedented market conditions. It argues that investments should align with values and aim for positive global impact, moving beyond solely maximizing shareholder value. Conscious investing can be as profitable as traditional investing while addressing social and environmental challenges. Why it matters: The piece advocates for integrating ethical considerations into investment strategies within the region, which could lead to more sustainable and socially responsible economic development.
KAUST, VentureSouq, startAD, and Tamkeen have partnered to launch the first Conscious Investor Fellowship in the GCC. The six-week virtual program aims to enable regional investors to create sustainable change through high-impact investments. The fellowship will host 25 investors from family offices, corporations, and government entities. Why it matters: The program aims to empower mission-driven investors in the region and accelerate investment in technology-driven startups addressing societal, economic, and environmental challenges.
According to Gulf News, geopolitical tensions between Iran and the US can create opportunities for UAE investors. Market dips caused by such tensions provide a chance to buy stocks at lower prices. The article suggests that investors should focus on fundamentally strong companies during these periods.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.