Google devices and apps have experienced serious outages that lasted for more than 12 hours and affected millions of users. The issue affected Google Home and Google Home Mini – speakers that respond to voice commands – as well as Chromecast – a device that plugs into a television and allows people to watch video content. Users were angry at both the length of the outage and the lack of information from Google about it, once it had been identified. Google has not given a reason why these devices went down, only apologising for the service problems and identifying a fix for the issues. The bug meant that when some Google Home owners asked a question of their speaker, it responded: "There was a glitch, try again in a few seconds." If they tried to reset the device, it would sometimes fail to reboot.

Two years ago, Sanjaya Wijeratne--a computer science PhD student at Wright State University--noticed something odd in his research. He was studying the communication of gang members on Twitter. Among the grandstanding about drugs and money, he found gang members repeatedly dropping the emoji in their tweets. Wijeratne had been working on separate research relating to word-sense disambiguation, a field of computational linguistics that looks at how words take on multiple meanings. The use of jumped out as a brand new problem.

Big banks and financial companies have started to offer banking through virtual assistants -- Amazon's Alexa, Apple's Siri, and Google's Assistant -- in a way that will allow customers to check their balances, pay bills and, in the near future, send money just with their voice. And with the rapid adoption of Zelle, a bank-to-bank transfer system, it soon could be possible to send money to friends or family instantly with voice commands. But the potential to do such sensitive tasks through a smart speaker raises security concerns. Virtual assistants and smart speakers are still relatively new technologies, and potentially susceptible to being exploited by cyber criminals. Regional banking giant U.S. Bank is the first bank to be on all three services -- Alexa, Siri and Assistant.

Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.

This August, some of the world's best professional gamers will travel to Vancouver to fight for millions of dollars in the world's most valuable esports competition. They'll be joined by a team of five artificial intelligence bots backed by Elon Musk, trying to set a new marker for the power of machine learning. The bots were developed by OpenAI, an independent research institute the Tesla CEO cofounded in 2015 to advance AI and prevent the technology from turning dangerous. Vancouver is hosting the annual world championship of Dota 2, one of the internet's most-watched videogames. The prize purse is more than $15 million and growing, exceeding the $11 million at stake at golf's Masters.

The 300,000-square-foot (27,000-square-meter) warehouse in the western Indianapolis suburb of Plainfield will be stocked with 5,000 different items before it opens next month, the Indianapolis Business Journal reported . The warehouse is managed by a system of robots, tote bins, electronic controls and software.

In 2015, a worried father asked Rhema Vaithianathan a question that still weighs on her mind. A small crowd had gathered in a basement room in Pittsburgh, Pennsylvania, to hear her explain how software might tackle child abuse. Each day, the area's hotline receives dozens of calls from people who suspect that a child is in danger; some of these are then flagged by call-centre staff for investigation. But the system does not catch all cases of abuse. Vaithianathan and her colleagues had just won a half-million-dollar contract to build an algorithm to help. Vaithianathan, a health economist who co-directs the Centre for Social Data Analytics at the Auckland University of Technology in New Zealand, told the crowd how the algorithm might work. For example, a tool trained on reams of data -- including family backgrounds and criminal records -- could generate risk scores when calls come in. That could help call screeners to flag which families to investigate.

Amazon staff have called on founder and chief executive Jeff Bezos to stop sales of AWS Rekognition facial recognition tech to U.S. law enforcement. Amazon employees have written a letter to CEO Jeff Bezos in which they ask the company to stop selling its facial recognition tool to American law enforcement. The tech giant's sales to U.S. cops was revealed by an American Civil Liberties Union (ACLU) investigation earlier this month, as it emerged Amazon Web Services' Rekognition tool was shipped to police in Florida and Oregon. The cost of the tool was also revealed to be remarkably low, as evidenced by a Forbes test of the product, in which a facial recognition project was set up for free across the publication's Jersey City and London offices. In a letter posted to an internal forum, first revealed by The Hill and published in full by Gizmodo, some employees expressed the same concerns as the ACLU about the power of Amazon's Rekognition being abused by American officers.

The "safety" driver behind the wheel of a self-driving Uber that hit and killed a pedestrian was streaming the television show The Voice on her phone at the time of the crash, police have said. The collision that killed Elaine Herzberg, 49, who was crossing the road at night in Tempe, Arizona, was "entirely avoidable", a police report said, if Rafaela Vasquez had been paying attention. Instead she repeatedly looked down at her phone, glancing up just a half second before the car hit Herzberg. Police said she could faces charges of vehicle manslaughter, but it would be for prosecutors to decide. The Uber car was in autonomous mode at the time of the crash, but Uber, like other self-driving car developers, requires a back-up driver in the car to intervene when the autonomous system fails or a tricky driving situation occurs.

Ronald Shrewsbery II used to be the Robot Doctor. Now he's known by the more bureaucratic-sounding title "WCM (World Class Manufacturing) Electrical Technical Specialist," but he still doctors the robots. There are a thousand of these machines inside Ohio's Toledo Assembly Complex, a 312-acre manufacturing leviathan dedicated to producing Jeeps. The Toledo Assembly Complex is one of the most heavily automated car factories in the United States. It can extrude 500 cars in a shift, far more than the Cove, the old Jeep plant that was shut down in 2006. And the machines make the work easier. There used to be a lot more lifting, more pushing.