The son of a math teacher and an engineer, Hassan Taher became committed to STEM learning as a young boy, but he also gravitated toward the science fiction of futurists such as Arthur C Clarke and Isaac Asimov. Given these early leanings, it is easy to see how Taher developed into a leading international authority on AI and its wide-ranging and rapidly evolving applications.
While studying computer science at the University of Texas at Dallas, Hassan Taher held active membership in the campus Artificial Intelligence Club. After graduation, he established the consultancy Taher AI Solutions and began publishing a series of influential articles on topics related to AI. Stressing its many challenges as well as its tremendous promise, Taher takes a comprehensive and critical look at AI in many contexts. He has authored three books: AI and Ethics: Navigating the Moral Maze, The Rise of Intelligent Machines, and The Future of Work in an AI-Powered World.
head of Taher AI Solutions
As the head of Taher AI Solutions, Taher has advised clients in areas that range from academic research to product manufacturing. He has also partnered with leading players in the finance and healthcare sectors. Taher’s books and articles have addressed issues of supreme importance in these areas and many others. But, given its potentially catastrophic effects worldwide, climate change and its certainly overshadows them all.
“Climate change is a global issue with profound local impacts. Communities around the world are grappling with its effects, from rising sea levels and extreme weather events to shifts in agricultural productivity,” writes Hassan Taher. “As the urgency to address these challenges grows, so does the need for precise and relevant climate models that can inform local decision-making. Artificial intelligence is emerging as a powerful tool in this regard, enhancing the accuracy and applicability of climate models at the local level.”
As detailed in the July 2023 article “AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change,” AI has driven extraordinary advancements in climate change science, “offering a range of capabilities that can help identify vulnerable areas, simulate future climate scenarios, and assess risks and opportunities for businesses and infrastructure.” While acknowledging many “ethical considerations and potential biases that must be addressed,” this influential article by Harshita Jain, Renu Dhupper, Anamika Shrivastava, Deepak Kumar, and Maya Kumari ultimately concludes that “AI can provide valuable insights that can inform decision-making and help us prepare for the impacts of climate change.”
Difficult to implement in climate change science
Despite this sunny outlook, implementing AI has proven difficult to implement in climate change science due to the technical obstacles of modeling extremely localized weather phenomena. In an interview with the California Institute of Technology (Caltech), NASA senior research scientist Tapio Schneider says, “Our ability to model climate change has been hampered by the enormous amount of computing power required to simulate all facets of climate. To make a global climate model accurate, it needs to capture small-scale processes, such as those controlling droplet formation in clouds, over the entire planet.”
When Schneider made this statement in October 2023, he declared the effective application of AI at this small sale “currently impossible.” But things move fast in the world of AI tech, and new research out of the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT) is challenging the notion that AI is ineffective at highly localized climate modeling. Principal MIT research scientist Sai Ravela, who led the study along with EAPS postdoc Anamitra Saha, declares, “It turns the traditional wisdom on its head.”
In the article “Can machine learning make climate models relevant for local decision-makers?,” SC Online News editor Tyler. O’Neal acknowledges that highly localized AI climate modeling has long been hindered by its inability to address complex physics equations and conservation laws. But Sai Ravela and Anamitra Saha “have proposed a method to make climate models more locally relevant using machine learning.”
Machine learning (ML) allows AI to recognize patterns
A subset of AI, machine learning (ML) allows AI to recognize patterns, make predictions, and essentially learn in much the same way that human beings learn. “In the realm of climate science, ML algorithms can process vast amounts of historical climate data, satellite imagery, and local weather patterns to refine climate models” writes Hassan Taher. “This process not only improves the accuracy of predictions but also enhances the model’s ability to simulate future scenarios under various conditions.”
In fact, Saha and Ravela have revolutionized a process called adversarial learning to drive the science of localized climate modeling. As reported in the June 11, 2024 issue of the MIT News, the researchers used two ML platforms, pitting them against one another to get optimum results without getting bogged down in the minutiae of particle physics. One platform generates data to go into a visual climate model sample, and the other platform judges the sample by comparing it to historical data.
“Using machine learning techniques like adversarial learning is not a new idea in climate modeling; where it currently struggles is its inability to handle large amounts of basic physics, like conservation laws,” reports MIT. “The researchers discovered that simplifying the physics going in and supplementing it with statistics from the historical data was enough to generate the results they needed.”
Potential for AI
Citing the June MIT study among others, Hassan Taher sees tremendous potential for AI in the fight against climate change. Specifically, he details the benefits that AI can offer local decision-makers in terms of preparing for climate change and the disastrous weather events that it might bring. This might mean making strategically planned improvements in areas that range from public infrastructure to agricultural management to public health.
“By enhancing the accuracy and resolution of climate predictions, AI empowers communities to better prepare for and adapt to the impacts of climate change” concludes Taher. “As technology continues to advance, the potential for AI to contribute to sustainable and resilient communities will only grow, offering hope and actionable solutions in the fight against climate change.”
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