Jordanian Phd Graduate Pioneers Breakthrough Method To Enhance AI Efficiency

(MENAFN- Jordan Times) ABU DHABI - Hilal Quabeh, one of the first three PhD graduates from Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), aims to raise the bar in AI by helping neural networks learn smarter, according to a statement for The Jordan Times.

It is no secret that AI is data hungry and requires powerful computing resources to operate. This places limitations on the ability of AI to fulfil its potential in certain situations, such as when smaller devices with limited power and processing capacity are being used, or when AI is needed in remote areas with limited connectivity. Examples could include the use of portable CT scanners in hospital and clinics, security cameras, monitoring equipment and drones in remote areas.

This is a challenge that Quabeh, who was among the first three PhD students to graduate from the world's first AI research university in Abu Dhabi on June 6th, was keen to take on.

He earned a PhD in Machine Learning as part of the university's largest ever cohort, 101 graduates from 24 countries who are now equipped to accept the responsibility that comes with the stewardship of something so powerful and transformative.

Quabeh's research focused on techniques designed to improve the efficiency with which machine learning algorithms learn, with minimal or no less of efficacy. It is a tough field of study within AI, but one that will become increasingly important as AI grows and expands into ever more areas and runs on a full range of devices, the statement said.

“There are many areas of machine learning that I find interesting, but I was keen to tackle this challenge because, if not addressed, it could have a detrimental effect on sectors including healthcare, transport and logistics and agriculture - particularly in remote areas - in the future, and I believe that the benefits of AI should be available to everyone,” Quabeh said.

Quabeh's research focused on ML methods known as pairwise learning, and multiinstances learning, and how to make them operate effectively even in lower resource environments.“Essentially, I looked at how machines can learn from different types of examples that are designed to enhance critical problem-solving and to improve their ability to spot nuanced details that they might ordinarily miss, and how to achieve this with limited information and resources,” he said.

Pairwise learning can be useful in applications such as anomaly detection and fraud prevention, and information retrieval and ranking, while multiinstance learning can be applied to tasks such as drug discovery and anomaly detection.

While doing research for his PhD, Quabeh also had the opportunity to work on developing ML models to help reduce the energy consumption required by ML in certain applications, and he applied his learning to a high-tech drone project by another Abu Dhabi-based entity.

Quabeh et al recently published a paper on a particularly complex area of AI research called spiking neural networks, which takes its inspiration from neuroscience, at the prestigious International Conference on Learning Representations conference Vienne-Austria 2024, according to the statement.

The paper demonstrated an advancement in the field.“It was a great experience to publish a paper on an area that has the potential to bring about real improvements to the efficiency of AI systems and to see all the hard work pay off,” Quabeh said.

Looking ahead, Quabeh is keen to continue working on theoretical machine learning challenges and would like to continue conducting research at MBZUAI.

He has been highly impressed by the university, its facilities, the calibre of the faculty, and the many friends he has made from around the world including France, Montenegro, India and China.

This year's class joins the university's growing alumni network of 111 AI leaders who are shaping the evolution of technology and AI across multiple sectors in the UAE and globally, the statement said.

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