Recently, Amazon introduced a feature that allowed Alexa to hear certain types of sounds, called Custom Sound Detection. Now, it's adding two new specific Alexa sound detectors for "water running" and "appliance beeping" that can be used to set up routines or reminders. It also rolled a number of other new features for things like prescription refills, ultrasound motion detection and more. It was already possible to have Alexa identify those two specific sounds, but the new update means you won't have to bother training it. With the new features, you can use the Alexa app to send a notification when the washer beeps to indicate your laundry is done.

In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific metrics for model selection and validation of the results. The use case is taken from the food packaging industry, where processes are affected by regular but short breakdowns causing interruptions in the production process. Fast production rates make it hard for machine operators to identify the source and thus the cause of the breakdown. Self learning assistance systems can help them finding the root cause of the problem and assist the machine operator in applying lasting solutions. These learning systems need to be trained to identify reoccurring problems using data analytics. Training is not easy as the process is too fast to be manually monitored to add specific classifications on the single data points.

In light of recent world events it may be time to consider that letting people run countries based on their ability to get elected may not be the best way to do things. Perhaps it's time to let computers decide who is best equipped to be in charge of these enormous economies and bureaucracies. Handing the choice over to an unthinking machine algorithm may seem like a crazy alternative, but would it be any worse than the way we do things now? Humans have experimented with many different ways to select leaders, including elections, heredity, and military coups. But nobody has ever trusted the running of a country to a machine before.

A key front for ethical questions in artificial intelligence, and computer science more generally, is teaching students how to engage with the questions they will face in their professional careers based on the tools and technologies we teach them.  In past work (and current teaching) we have advocated for the use of science fiction as an appropriate tool which enables AI researchers to engage students and the public on the current state and potential impacts of AI. We present teaching suggestions for E.M. Forster's 1909 story, "The Machine Stops," to teach topics in computer ethics.  In particular, we use the story to examine ethical issues related to being constantly available for remote contact, physically isolated, and dependent on a machine --- all without mentioning computer games or other media to which students have strong emotional associations. We give a high-level view of common ethical theories and indicate how they inform the questions raised by the story and afford a structure for thinking about how to address them.