Roundup Here's your roundup of machine-learning news from this week, beyond what we've already covered. Axon AI Ethics board A group of civil rights groups and technology researchers has written a letter to Axon, a company that uses AI to analyze video footage aimed at law enforcement. Axon recently announced it had set up an AI ethics board to guide its products and services. In response, the letter urges the company to not develop real-time facial recognition for police body cameras to prevent misidentifying civilians as criminals, to ethically reviewing all its other products, and to reach out to "survivors of law enforcement harm and violence" for advice. You can read the letter here.

Google's Tensor Processing Unit (TPU), a custom-developed accelerator for deep learning, offers a fast and cost-efficient alternative to training deep learning models in the cloud: it is capable of training a ResNet-50 model on ImageNet in 12.5 hours -- for an equivalent of $81 of TPU compute time. At RiseML, we believe that machine learning engineers shouldn't have to worry about infrastructure. Recently, Google Kubernetes Engine (GKE), the managed Kubernetes offering by Google, started providing alpha level support for provisioning TPUs. Each TPU's lifetime is automatically bound to the lifetime of its job, so you only pay for your actual use. The combination of GKE and RiseML offers a hassle-free machine learning infrastructure that is easy-to use, highly scalable, and cost-efficient.

When training a neural network, one typically strives to make the GPU the bottleneck. All data should be read from disk, pre-processed, and transferred to the GPU fast enough so that the GPU is busy 100% of the time computing the next improved version of the model. An increasing trend we see at RiseML is that pre-processing and especially reading the training data from disk becomes the bottleneck. This is caused by multiple factors, including faster GPUs, more efficient model architectures, and larger datasets, especially for video and image processing. As a result, the GPUs sit idle a lot of time, waiting for the next batch of data to work on.