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 …

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 …

While many people think of abstract ideas regarding artificial general intelligence (AGI), this technology has arrived at an important crossroads today. In fact, scientists stunned by its incredible potential agree to disagree on how the future of AGI should be shaped. Disagreements about the future of technologies, especially the ones

Reinforcement learning (RL) is a trial and error form of learning in which an agent acting in a given environment learns to take optimal actions at every state it encounters in such an environment with the ultimate goal to increase/maximize a numerical reward function.

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit. Introduction This post is for people making technology decisions, by which I mean data science …

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. It also covers using Keras to construct a deep Q-learning network that learns within a simulated …

Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow. Cloud Programming Simplified: A Berkeley View on Serverless Computing (2019) – Serverless computing is very popular nowadays …

Learn about the basic concepts of reinforcement learning and implement a simple RL algorithm called Q-Learning.

NASA [Public domain]In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. In doing so, I’ll demonstrate the application of ML techniques to the manufacturing industry and the role of the Machine Learning Product Manager.Machine learning …

The general trend in machine learning research is to stop fine-tuning models, and instead use a meta-learning algorithm that automatically finds the best architecture and hyperparameters. What about meta-reinforcement learning (meta-RL)? Meta-RL is just meta-learning applied to RL.

Reinforcement Learning: An Introduction by Rich Sutton and Andrew Barto was recently released on October 15, 2018. A free version is available online.

Paco Nathan‘s column covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. Introduction Welcome back to our new monthly series! September has been the busiest part of “Conference Season” with excellent new material to review. Three themes jump out recently. …

Understand the basics of reinforcement learning, what are the different types of reinforcement learning and use it to optimise CTR.