Practical AI Meet your Practical AI hosts
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Jul 2, 2018 Introductions to speakers with varied journeys into AI, from physics to avionics to startups. A look at the resurgence of deep learning and how compute, data, and algorithms changed the field. Discussion of AI as a new software paradigm and plans for practical tutorials, explainers, ethics, infrastructure, and community-driven resources.
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Family Origins Sparked A Lifelong AI Passion
- Chris Benson grew up around AI through his parents' work on the F-22 avionics failure and his father's neural-net research that followed.
- That family exposure sparked a long-term passion that later became his career pivot into deep learning and AI tools.
Physics Background Led To A Data Science Conversion
- Daniel Whitenack started in physics doing first-principles atomic and molecular research before encountering ML approaches that replaced analytic solutions.
- That shift felt ironic as ML methods learned functionals that his PhD work aimed to derive analytically, which pulled him toward data science roles.
AI Is A Moving Definition Not A Fixed Technology
- Chris Benson frames AI as a moving target: historically symbolic AI, now largely deep learning, and likely something different in the future.
- He treats AI as a variable whose value evolves with technology, not a fixed definition tied to one method.