Michael I. Jordan, a UC Berkeley Distinguished Professor and influential figure in machine learning and AI, has been appointed laureate professor at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He will also serve as honorary director of MBZUAI's new Laureate Faculty Program. The program aims to attract leading researchers to MBZUAI by providing resources and minimizing administrative burdens. Why it matters: This appointment strengthens MBZUAI's reputation and enhances the UAE's AI ecosystem by bringing in a world-renowned AI expert to foster research and innovation.
Michael I. Jordan, a renowned AI researcher from UC Berkeley, has joined Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) as a laureate professor and honorary director of the Laureate Faculty Program. MBZUAI President Eric Xing highlighted Jordan's significant influence in machine learning, noting his role as a mentor. Jordan aims to guide AI researchers and advise the university as it seeks to become a global leader in AI. Why it matters: This appointment strengthens MBZUAI's position as a prominent AI research institution in the Middle East by attracting top-tier international talent and fostering a conducive environment for cutting-edge research.
UC Berkeley professor Michael I. Jordan will lead a session on AI, Machine Learning and Economy as part of the MBZUAI Executive Program. The program is headed by MBZUAI President Eric Xing and includes 42 participants from ministerial leadership and top industry executives. The 12-week program aims to support the UAE's AI leadership mission through education, capacity building, innovation, and R&D. Why it matters: The involvement of a prominent academic figure like Jordan highlights the UAE's commitment to attracting global expertise in AI education and solidifying its position as an AI hub.
MBZUAI and UC Berkeley held a joint workshop on machine learning, featuring discussions on online learning, fair allocation in dynamic mechanism design, and causal inference. Michael I. Jordan, Laureate Professor and Honorary Program Director at MBZUAI, highlighted the institute's rapid growth during his visit. Researchers explored methods for enhancing the properties of large, complex models, such as calibration, fairness, and robustness. Why it matters: Such collaborations advance AI research and foster knowledge exchange between leading global experts and regional institutions like MBZUAI.
This article previews a presentation by Kevin Murphy (Google Brain) at MBZUAI on a unified perspective of machine learning, based on his book "Probabilistic Machine Learning: Advanced Topics". The presentation will cover the "4 pillars of ML": predictions, decisions, discovery and generation. Murphy will summarize recent methods and his own contributions in each of these tasks. Why it matters: Hosting prominent international AI researchers strengthens MBZUAI's position as a global hub for AI research and education.
MBZUAI launched the AI Quorum, a winter series from October 2022 to March 2023, to stimulate AI research. The first session, led by Professor Michael Jordan, focused on collaborative learning with around 20 research experts. Discussions covered the use of edge devices like cell phones and hospitals providing data to build large models, as well as risks like free-riding and adversarial attacks. Why it matters: The AI Quorum initiative positions MBZUAI as a hub for global AI collaboration, addressing key challenges and opportunities in collaborative learning for real-world applications.
Saber Salehkaleybar from EPFL presented a talk on causal discovery, focusing on learning causal relationships from observational data and through interventions. He discussed an approximation algorithm for experiment design under budget constraints, with applications in gene-regulatory networks. The talk also covered improvements to reduce the computational complexity of experiment design algorithms. Why it matters: Causal AI systems can lead to more intelligent decision-making in various fields.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.