Scraping Bits #78 - Eito Miyamura: GPU-EVM, Game-Based Reinforcement Learning, Parallel Simulations
In this episode of Scraping Bits, we dive deep into the fascinating world of game-based reinforcement learning, parallel simulations, and the groundbreaking GPU-EVM with our special guest, Eito Miyamura. Eito, a distinguished Oxford alumnus with expertise in machine learning and reinforcement learning, shares insights from his latest project, GPU-EVM, which is touted as the most performant Ethereum Virtual Machine currently available.
We start by exploring the concept of GPU-EVM, developed by GatlingX, which leverages parallel execution on various Ethereum apps to enhance the training of AI agents. Eito explains how this technology uses graphical processing units (GPUs) to execute operations in parallel, dramatically increasing efficiency and processing speed.
Next, we shift our focus to the application of reinforcement learning in gaming. Eito discusses how this technology is not just revolutionizing game design but also has significant implications for real-world applications. We cover the basics of reinforcement learning, its integration into commercial games, and how it's used to train AI to adapt and learn from dynamic environments.
The conversation then turns to parallel simulations, a technique that allows massive simulations to be executed concurrently, scaling from desktops to clusters and the cloud. Eito shares his insights on how parallel simulations are implemented in tools like MATLAB and Simulink, and their benefits for speeding up workflows and enhancing the capability of simulations.
Join us as we unravel how these advanced technologies are setting the stage for future innovations in both the gaming industry and broader computational fields. Whether you're a tech enthusiast, a professional in the field of AI and machine learning, or someone curious about the future of technology, this episode is packed with enlightening discussions that you won't want to miss.
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