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Results for "Raj Reddy"

Turing award winner brings AI expertise to UAE

MBZUAI ·

Turing Award winner Raj Reddy spoke at the MBZUAI Executive Program. Reddy is a Professor of Computer Science and Robotics at Carnegie Mellon University (CMU) and has held leadership roles at CMU, AAAI, and IEEE. His research focuses on AI, human-computer interaction, and technology's societal impact. Why it matters: High-profile speakers at UAE AI programs can help attract talent and investment to the region.

Empowering Large Language Models with Reliable Reasoning

MBZUAI ·

Liangming Pan from UCSB presented research on building reliable generative AI agents by integrating symbolic representations with LLMs. The neuro-symbolic strategy combines the flexibility of language models with precise knowledge representation and verifiable reasoning. The work covers Logic-LM, ProgramFC, and learning from automated feedback, aiming to address LLM limitations in complex reasoning tasks. Why it matters: Improving the reliability of LLMs is crucial for high-stakes applications in finance, medicine, and law within the region and globally.

Neural Models with Symbolic Representations for Perceptuo-Reasoning Tasks

MBZUAI ·

Mausam, head of Yardi School of AI at IIT Delhi and affiliate professor at University of Washington, will discuss Neuro-Symbolic AI. The talk will cover recent research threads with applications in NLP, probabilistic decision-making, and constraint satisfaction. Mausam's research explores neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and intelligent information systems. Why it matters: Neuro-Symbolic AI is gaining importance as it combines the strengths of neural and symbolic approaches, potentially leading to more robust and explainable AI systems.

Web-Based Expert System for Civil Service Regulations: RCSES

arXiv ·

The paper introduces a web-based expert system called RCSES for civil service regulations in Saudi Arabia. The system covers 17 regulations and utilizes XML for knowledge representation and ASP.net for rule-based inference. RCSES was validated by domain experts and technical users, and compared favorably to other web-based expert systems.

KAUST, Intel, and Brightskies collaborate to develop self-driving mobility platform

KAUST ·

KAUST, Intel, and Brightskies have launched REDD, a collaborative self-driving mobility platform, converting a conventional car into a self-driving vehicle with integrated AI software. Brightskies developed the self-driving system, powered by Intel® NUC platforms, utilizing their BrightDrive system. KAUST researchers will use the vehicle to test new techniques, leveraging real-world data to improve self-driving technologies. Why it matters: This partnership advances autonomous vehicle research in Saudi Arabia, aligning with the Kingdom's Vision 2030 by creating a platform for innovation and testing in a real-world environment.