Even Apple's voice assistant, Siri, is being forced to adapt to an ongoing health crisis. As reported by CNBC, Siri is now capable of responding to users who ask the assistant about whether they have novel coronavirus, COVID-19. Queries like, 'Hey Siri, do I have the coronavirus?' will now elicit a questionnaire asking users if they have a fever or a cough and will recommend those that are experiencing potentially fatal symptoms to call 911. CNBC reports that if the symptoms appear to be more mild, Siri will instruct users to stay home and avoid contact with others instead. It may also redirect some users to the App Store where they can download apps that let them consult with a doctor digitally.

Tech companies are known to listen in on private conversation via its smart speakers in order to'improve voice-recognition features.' Now that millions of people are currently working home due to the coronavirus outbreak, employers are urging their stuff to power down the technology in order to keep it from listening to confidential phone calls. Mishcon de Reya LLP, the UK law firm that advised Princess Diana on her divorce, advised staff to mute or shut off listening devices like Amazon's Alexa or Google's voice assistant when they talk about client matters at home, according to a partner at the firm. Video products such as Ring and baby monitors are also on the list of devices to be away of while working from home, as first reported on by Bloomberg. Mishcon de Reya LLP, the UK law firm that advised Princess Diana on her divorce, advised staff to mute or shut off listening devices like Amazon's Alexa or Google's voice assistant when they talk about client matters at home Mishcon de Reya partner Joe Hancock, who also heads the firm's cybersecurity efforts, told Bloombger: 'Perhaps we're being slightly paranoid but we need to have a lot of trust in these organizations and these devices.' 'We'd rather not take those risks.'

This undated photo distributed on Friday, June 9, 2017, by the North Korean government, shows a test of a new type of cruise missile launch at an undisclosed location in North Korea - file photo. Maneuvering cruise missiles, fast-moving stealthy fighter jets, armed drones, long-range helicopter-fired air-to-ground weapons and hypersonic rounds traveling at five times the speed of sound are all modern methods of air-attack able to destroy Army ground war units -- potentially even rendering them inoperable or, even worse, making them vulnerable to complete destruction. The weapons, sensors and platforms now operated by potential adversaries have created an entirely new tactical environment now defining land combat, a scenario that has inspired the U.S. Army to fast-track new, advanced air and missile defense radar technologies sufficient to thwart this changing sphere of enemy attack possibilities. The service is now surging forward in response to an urgent need with a new 360-degree radar system called Lower Tier Air & Missile Defense Sensor (LTAMDS), slated for initial fielding by 2022. Unlike the more linear directional configuration of the existing Patriot air and missile defense system, the Raytheon-built LTAMDS is engineered with overlapping 120-degree arrays intended to seamlessly track approaching threats using a 360-degree protection envelope.

Graph clustering has been studied extensively on both plain graphs and attributed graphs. However, all these methods need to partition the whole graph to find cluster structures. Sometimes, based on domain knowledge, people may have information about a specific target region in the graph and only want to find a single cluster concentrated on this local region. Such a task is called local clustering. In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs). Currently, very few methods can deal with this kind of task. To this end, we propose two quality measures for a local cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). The former measures the homogeneity of the graph structure while the latter measures the homogeneity of the subspace that is composed of the designated attributes. We call their linear combination as Compactness. Further, we propose LOCLU to optimize the Compactness score. The local cluster detected by LOCLU concentrates on the region of interest, provides efficient information flow in the graph and exhibits a unimodal data distribution in the subspace of the designated attributes.

Consider the grocery clerks at two Safeway stores in the San Francisco Bay Area. A few weeks ago, over 200 workers who are members of the United Food and Commercial Workers Local 5 (UFCW5) union picketed a Safeway store in San Jose, Calif. to voice concerns about a push by parent company Albertsons to add more A.I to its operations. Albertsons recently partnered with the startup Takeoff Technologies to create mini warehouses where computer vision technology automatically sorts items that shoppers order online. Using A.I. reduces the need for Safeway staff to manually locate and grab items for delivery--workers now just retrieve the finalized orders from a conveyor belt and sign off on them for eventual delivery. Several grocery store chains are investing heavily in micro-fulfillment centers after Amazon helped to popularize as-fast-as-you-can deliveries, said Andrew Lipsman, a principal analyst at research firm eMarketer.

An Australian team is using machine learning to tackle the threat of space junk wrecking new satellites. Research to tackle the growing need to find, capture and remove junk from space is advancing at the Australian Institute for Machine Learning in Adelaide, South Australia. Machine Learning for Space director Tat-Jun Chin and his Adelaide-based team have won a $600,000 grant from Australia's SmartSat CRC to continue their work in detecting, tracking and cataloging space junk. SmartSat CRC was established last year to work with the Australian Space Agency based in Adelaide, contributing to the Australian government's goal of tripling the size of the space sector to $12 billion and creating as many as 20,000 jobs by 2030. The space junk project is based on developing a space-based surveillance network and tackling the growing challenge of crowding in space.

Explainable Artificial Intelligence (XAI) seems to be a hot topic nowadays. It is a topic I came across recently in a number of instances: workshops organized by the European Defense Agency (EDA), posts from technology partners such as Expert System (here) or internal discussion with SDL's Research team. The straightforward definition of XAI comes from Wikipedia: "Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision. XAI is an implementation of the social right to explanation."

Bashing Silicon Valley has become one of the few things both political parties agree on this election cycle. And they have good reason--the artificial intelligence (AI) and automation technology developed by the Valley's best and brightest minds is projected to displace the jobs of between one-quarter and one-third of American workers by 2030. The Brookings Institute estimates that 36 million Americans could have 70% of their work tasks replaced by automation. These alarming figures are attracting the attention of policymakers and politicians alike. Some are calling for a tax on robots. Others believe massive open online courses (MOOCs) are the answer to retraining millions of displaced workers.

Like any tool, technology can be used for both good and bad. And sometimes, that bad is inadvertent; tech in the form of airplanes helped expedite the spread of the coronavirus around the world. But fortunately, technology will also aid in stopping this pandemic crisis. A few weeks ago, we wrote about how the San Francisco-based company BlueDot utilized artificial intelligence (AI) to warn the general public about the dangers of COVID-19 well ahead of health officials. In case you missed it, you can read it here. Examples like BlueDot give us hope that emerging tools like AI can help humanity tackle problems like the coronavirus in unprecedented ways.

The United States Navy is developing robot submarine which will be controlled by Artificial Intelligence (AI) systems – with the ability to potentially kill without explicit human input. The project is named as'CLAWS' by the US Navy; however, very little information has been released about it. According to a report by New Scientist, the project is described as an'autonomous undersea weapon system' and carried out by the Office of Naval Research. Details of this killer-submersible were revealed as part of the 2020 budget documents which also disclosed its AI system name. Since this project is a'top secret', only a few details have been released including the fact that it will use sensors and algorithms to perform the complex missions on their own.