The burden of the pandemic has forced seismic changes in human behavior, causing a major transformation in two specific machine learning models. The first is canonical machine learning (CML), representing traditional approaches in pattern recognition, derived from highly structured and labeled data through computational statistics. The second is reinforcement machine learning (RML), which deploys a fundamentally different modeling paradigm as it self-adjusts individual actions to optimize a collective outcome, and operates much more autonomously than CML. We’ll discuss the evolution of both CML and RML in three critical time periods: 1) the time before the pandemic (BP), 2) the time during the pandemic (DP) and 3) the time after the pandemic (AP).
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- Before the pandemic, companies were successfully transforming their industries through CML, showing a thriving union between science and industry.
- The pandemic obliterated millions of ML models, forcing every sector to bet on a natural realignment or manually drive mission-critical insights.
- Because RML is more robust at predicting behavior, those that adopt this model have a higher chance of outlasting the pandemic and their competition.
- Charlie Burgoyne, Founder & CEO, Valkyrie
Natalie Barreiro, Pr, Valkyrie
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