EXECUTIVE INSIGHT
During a climate of rapidly growing AI , there have been global calls for increased regulation of the technology .
With the EU having reached a provisional agreement on its AI Act , and the United States imposing regulations on technology companies , regulatory guidelines – intended to lead to safer models , less biases and an overall improved AI ethics landscape – are being discussed more frequently .
From working at the likes of NASA and Samsung in business strategy , research and AI , SVP at Searce Patrick Bangert offers us insight into his role in data and analytics , into the future of AI regulation and how businesses can ensure safe and responsible uses of the technology .
Q . TELL US ABOUT YOUR CAREER BACKGROUND AND YOUR CURRENT ROLE AT SEARCE
» Starting out as a theoretical physics student and then doing my PhD in mathematics at University College London , I was always fascinated by how the world works . Mathematics has an uncanny ability to describe what happens , what will happen , and how to proactively influence that . It ’ s mathe-magical . I pursued this path as professor for applied mathematics in Germany until I figured out the academic environment would not let me apply these methods to real life .
That ’ s when I founded my own company , Algorithmica Technologies , in 2005 to bring AI methods to the process industry – the industry that starts with mining and oil and gas , continues with chemistry and petrochemistry , and culminates in power generation . These industries are asset heavy in the sense
that the costs are dominated by physical equipment , facing primary challenges in the maintenance and control of machinery . I continued to publish scientific papers and three books on these subjects , which kept me busy around the world until I exited the business in 2020 . In the meantime , I relocated to the San Francisco Bay Area .
During this period , Samsung approached me to spearhead their AI division based in San Jose . Over the course of three enriching years , I led projects on various AI topics predominantly focused on unstructured data , particularly images . Our team successfully created a platform designed to handle large-scale model training efficiently . This technological leap
18 March 2024