1. Introduction Welcome to our fascinating science blog, where we delve into the extraordinary world of depth maps and their pivotal role in revolutionizing autonomous driving. Depth maps, captivating images that embody distance information, serve as a crucial tool for understanding the spatial characteristics of a camera-captured scene. …

Part 3: AI ethics and our ML education practices — In the previous post, we have discussed questions around dataset creation and expectations, specifically what should we question when considering a possible problem to be addressed with ML, when we want to create efficient data creation pipeline, what perspective should we take on the power of benchmarking practices. …

Part 2: A more holistic perspective on data creation and expectations — As discussed in the previous post, to design better ML approaches and systems, the sole direction of augmenting and correcting data to attempt to avoid biases seems structurally limited. This question has been the subject of heated debates in the ML community, such as the Twitter feud¹ between Timnit Gebru…

Part 1: Deployment failures and approaches to data — When we approach a Machine Learning (ML) project where we want to solve a specific task with an existing or to-be-designed ML model, we immediately think about data: their availability always, their quantity often, their quality and mode of creation less frequently. If at all, it is only at the…