The Data Exchange Podcast: Chris Nicholson on applications of reinforcement learning to business process simulation and optimization.
Subscribe: iTunes, Android, Spotify, Stitcher, Google, and RSS.
In this episode of the Data Exchange I speak with Chris Nicholson, founder and CEO of Pathmind1, a startup applying deep reinforcement learning (DRL) to simulation problems. In a recent post I highlighted two areas where companies can begin to add DRL to their suite of tools: personalization and recommendation engines, and simulation software. My interest in the interplay between DRL and simulation software began when I came across the work of Pathmind in this area.
Our conversation focused on deep reinforcement learning and its applications:
- We began with the basics: what is reinforcement learning and why should businesses pay attention to it?
- We discussed enterprise applications of DRL, with particular emphasis in areas where Chris and Pathmind have been focused of late: Business Process Simulation and Optimization.
- Pathmind have been early adopters of Ray and of RLlib, a popular open-source library for reinforcement learning built on top of Ray. I asked Chris why they chose to build on top of RLlib.
Subscribe to our Newsletter:
We also publish a popular newsletter where we share highlights from recent episodes, trends in AI / machine learning / data, and a collection of recommendations.
Related content:
- Enterprise Applications of Reinforcement Learning: Recommenders and Simulation Modeling
- Dean Wampler: “Scalable Machine Learning, Scalable Python, For Everyone”
- Key AI and Data Trends for 2020”
- Rajat Monga: “The evolution of TensorFlow and of machine learning infrastructure”
- Edmon Begoli: “Hyperscaling natural language processing”
- Edo Liberty: “How deep learning is being used for search and information retrieval”
[1] I am an advisor to Pathmind.
[Image: Direct Numerical Simulations and Robust Predictions of Cloud Cavitation Collapse by Jonas Sukys.]