The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the hundreds of millions. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.
Source: threader.app
Uber has been one of the most active contributors to open source machine learning technologies in the last few years. While companies like Google or Facebook have focused their contributions in new deep learning stacks like TensorFlow, Caffe2 or PyTorch, the Uber engineering team has really focused on tools and best practices for building machine learning at scale in the real world. Technologies such as Michelangelo, Horovod, PyML, Pyro are some of examples of Uber’s contributions to the machine learning ecosystem.
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Speaker diarization, the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations, video captioning and more. However, training these systems with supervised learning methods is challenging — unlike standard supervised classification tasks, a robust diarization model requires the ability to associate new individuals with distinct speech segments that weren’t involved in training.
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What are the most interesting and informative YouTube channels about artificial intelligence (AI) and machine learning? Subscribe to these 20 high-quality channels today to stay up to date with the latest AI and machine learning breakthroughs. Siraj Raval:
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