Unsurprisingly, recruiters who are keen to circumvent human prejudices with regards to factors like race and gender are slowly utilizing this software. But is it really the case that smart AI like this can be ethically neutral?
Source: venturebeat.com
Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ [1], implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.
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This post is a tour around the PyTorch codebase, it is meant to be a guide for the architectural design of PyTorch and its internals. My main goal is to provide something useful for those who are interested in understanding what happens beyond the user-facing API and show something new beyond what was already covered in other tutorials.
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Simulink provides a great training environment for DRL as it allows 3rd parties like Bonsai to integrate and control simulation models from the outside. This ability is one of the basic requirements for simulation platforms to be feasible for Deep Reinforcement Learning using Bonsai AI. More requirements can be found here.
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