Video Highlights: Deep Reinforcement Learning for Maximizing Profits — with Prof. Barrett Thomas

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Dr. Barrett Thomas, an esteemed Research Professor in at the University of Iowa’s College of Business, to delve deep into Markov decision processes and how they relate to Deep Reinforcement Learning.

Reinforcement Learning: Balancing Exploration and Exploitation

In this contributed article, Anthony Chong, CEO/Co-Founder of IKASI, discusses the three types of machine learning approaches, the benefits and requirements of each, and offer examples of how organizations are applying these tactics to address real world business challenges.

Why Reinforcement Learning Will Save Generative AI

In this contributed article, Kim Stagg, VP of Product for Appen, knows the only way to achieve functional AI models is to use high-quality data in every stage of deployment. As businesses look for pathways to adoption, there must be a significant focus on removing bottlenecks for data quality in order to achieve the highest functioning AI models. This article focuses on what companies looking to adopt this technology need to know about creating generative AI models that are ready for high-precision tasks and real-world applications.

d-Matrix Unlocks New Potential with Reinforcement Learning based Compiler for at Scale Digital In-Memory Compute Platforms

d-Matrix, a leader in high-efficiency AI-compute and inference, announced a collaboration with Microsoft using its low-code reinforcement learning (RL) platform, Project Bonsai, to enable an AI-trained compiler for d-Matrix’s unique digital in memory compute (DIMC) products. The user-friendly Project Bonsai platform accelerates time to value, with a product-ready solution that cuts down on development efforts using an AI-based compiler that leverages ultra-efficient DIMC technology from d-Matrix.

AI Under the Hood: Mixing Things Up – Optimizing Fluid Mixing with Machine Learning

Fluid mixing is an important part of several industrial processes and chemical reactions. However, the process often relies on trial-and-error-based experiments instead of mathematical optimization. While turbulent mixing is effective, it cannot always be sustained and can damage the materials involved. To address this issue, researchers from Japan (Tokyo University of Science) have now proposed an optimization approach to fluid mixing for laminar flows using machine learning, which can be extended to turbulent mixing as well.