One of the biggest challenges in AI is how to translate advances in the lab into large-scale applications. This challenge sits at the intersection of AI and systems engineering and requires an integrated understanding of all of the components that make up a large machine learning-based system, including computation, storage, communications, and algorithms. Casimir Wierzynski reviews current trends in the field and shares case studies to illustrate why codesigning these components in concert will be critical for building the AI systems of the future.
Casimir Wierzynski is a senior director in the Artificial Intelligence Product Group at Intel, where he leads research efforts to identify, synthesize, and incubate emerging technologies that will enable the next generation of AI systems. Previously, Cas led research teams in neuromorphic computing, learning and AI planning, and autonomous robotics at Qualcomm and was a vice president at Goldman Sachs, where he traded fixed income and credit derivatives. Driven by his passion for AI and the brain, Cas left finance to get a PhD in computation and neural systems from Caltech, where he used large-scale neural recordings to study the relationship between memory consolidation and sleep. Cas also holds a BS and MS in electrical engineering from MIT (he completed his master’s thesis at AT&T Bell Labs) and a BA in mathematics from Cambridge University, where he was a British Marshall Scholar.
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