Reinforcement learning is a powerful machine learning technique for solving problems in dynamic and adaptive environments. Combined with a simulation or digital twin, reinforcement learning can train models to automate or optimize the efficiency of industrial systems and processes such as robotics, manufacturing, energy, and supply chain.
But what comes after the simulation? Mark Hammond dives into two real-world case studies to show how deep reinforcement learning successfully automated the machine tuning of a Fortune 500 manufacturing system and optimized energy efficiency of a large-scale HVAC system. Mark details the end-to-end process of building, training, and deploying models and examines the business impact of each application.
Some are cognitive scientists; others are computer scientists and engineers. Mark Hammond is a cognitive entrepreneur bringing together both fields along with business acumen. He has a deep passion for understanding how the mind works, combined with an understanding of own human nature, and turns that knowledge into beneficial applied technology. As the founder and CEO of Bonsai, Mark is enabling AI for everyone. Mark has been programming since the first grade and started working at Microsoft as an intern and contractor while still in high school. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.
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