Chemistry laboratories generate a significant amount of data. However, some of it is still in paper format and is difficult to access in its entirety. Three scientists from EPFL present a modular open science platform to manage the large amounts of data produced in chemistry research. Their study entitled " Making the collective knowledge of chemistry open and machine-readable" waspublished in Nature Chemistry. Managing data in modern chemistry is challenging.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Many systems like autonomous vehicle fleets and drone swarms can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks, which deal with how multiple machines can learn to collaborate, coordinate, compete, and collectively learn. It's been shown that machine learning algorithms -- particularly reinforcement learning algorithms -- are well-suited to MARL tasks. But it's often challenging to efficiently scale them up to hundreds or even thousands of machines. One solution is a technique called centralized training and decentralized execution (CTDE), which allows an algorithm to train using data from multiple machines but make predictions for each machine individually (e.g., like when a driverless car should turn left).