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Jerome Leudet

Jerome has a strong background in AI. After a master’s in AI and VR, he developed AI for the gaming industry for more than a decade and worked in industrial simulation. With the rise of AI, he had the opportunity to begin a PhD and study how synthetic data could be used for training neural networks. He founded AILiveSim on a mission to create rich, interactive worlds to train and test algorithms.


Data set creation for autonomous machines in an underground environment

Your autonomous development is intrinsically correlated to the data you use for teaching and training. You will need high-volume and/or relevant data for your specific applications. Counting only on real annotated data is limiting, time-consuming and expensive, even more so in complex environments such as ever-changing construction sites or underground tunnels. To complement an existing data set, generating data via simulation with very realistic models and easy access to more configurable parameters becomes a game-changer and can help to shorten development time and cost related to data set acquisition.