A few weeks ago, the Google Store listed the Google Home smart speaker as "no longer available." Now, we might know why. According to 9to5Google, the company is planning on a new Nest-branded smart speaker that is more in line with the minimalist look and fabric stylings of the Nest Mini and the Nest Hub. The device is apparently codenamed "Prince" and is said to be somewhat of a rival to the Sonos One, which might mean more robust speakers. It's unclear when the product will actually launch, but as the Pixel 4a is likely delayed, Google's next smart speaker will probably have a later debut as well.

Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as having collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful on-board computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.

The activity-based workspace (ABW) paradigm is becoming more popular in commercial office spaces. In this strategy, occupants are given a choice of spaces to do their work and personal activities on a day-to-day basis. This paper shows the implementation and testing of the Spacematch platform that was designed to improve the allocation and management of ABW. An experiment was implemented to test the ability to characterize the preferences of occupants to match them with suitable environmentally-comfortable and spatially-efficient flexible workspaces. This approach connects occupants with a catalog of available work desks using a web-based mobile application and enables them to provide real-time environmental feedback. In this work, we tested the ability for this feedback data to be merged with indoor environmental values from Internet-of-Things (IoT) sensors to optimize space and energy use by grouping occupants with similar preferences. This paper outlines a case study implementation of this platform on two office buildings. This deployment collected 1,182 responses from 25 field-based research participants over a 30-day study. From this initial data set, the results show that the ABW occupants can be segmented into specific types of users based on their accumulated preference data, and matching preferences can be derived to build a recommendation platform.

Sexually explicit pictures, audio recordings and private conversations shared in dating apps, such as SugarD and Herpes Dating, have been exposed online. Security researchers discovered unprotected Amazon Web Services'buckets' with over 20 million files linked to hundreds of thousands of users. Although no'personally identifiable information' was visible, experts note that a determined hacker could reveal a user through photos and other available information. It is not known if the data was accessed by anyone else, but the team says there is enough to commit fraud, extortion and viral attacks on the apps' members. Sexual explicit pictures, audio recordings and private conversations belonging to users of dating apps, such as SugarD and Herpes Dating, have been exposed online.

Tech giants have struggled for years to convince the public that they are committed to diversifying their own massive workforces, but the demographics have only slowly changed in the past decade. Google's workforce is 54.4 percent white and 3.3 percent black, according to its 2019 diversity report. Apple's U.S. workforce is 50 percent white and 9 percent black. Amazon's report shows a more diverse makeup -- its U.S. workforce is 34.7 percent white and 26.5 percent black -- though its statistics include low-paying warehouse jobs as well as more lucrative white collar positions.

It's painfully common for data to be exposed online. But just because it happens so often that doesn't make it any less dangerous. Especially when that data comes from a slew of dating apps that cater to specific groups and interests. Security researchers Noam Rotem and Ran Locar were scanning the open internet on May 24 when they stumbled upon a collection of publicly accessible Amazon Web Services "buckets." Each contained a trove of data from a different specialized dating app, including 3somes, Cougary, Gay Daddy Bear, Xpal, BBW Dating, Casualx, SugarD, Herpes Dating, and GHunt.

This week we speak to Justin McLeod, founder and chief executive of dating app Hinge. When recovering alcoholic Justin McLeod set up his dating app, it was to help him get over his heartbreak. Five years earlier, his college sweetheart, the woman he thought was the love of his life, had split up with him because of his drink problem. He had subsequently gone to rehab and successfully sobered up, but he had not been able to move on romantically. Not comfortable going into bars because of his addiction issue, he started work on Hinge in 2011 to help him find a new partner.

A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to identify the latent state, after which it can act optimally. This setting is a natural midpoint between online and offline learning---complex models can be learned offline with the agent identifying latent state online---of practical relevance in, say, recommender systems. In this work, we propose general algorithms for this setting, based on both upper confidence bounds (UCBs) and Thompson sampling. Our methods are contextual and aware of model uncertainty and misspecification. We provide a unified theoretical analysis of our algorithms, which have lower regret than classic bandit policies when the number of latent states is smaller than actions. A comprehensive empirical study showcases the advantages of our approach.

With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

This paper investigates the implementation of voice-enabled Google Assistant and Amazon Alexa on Raspberry Pi. Virtual Assistants are being a new trend in how we interact or do computations with physical devices. A voice-enabled system essentially means a system that processes voice as an input, decodes, or understands the meaning of that input and generates an appropriate voice output. In this paper, we are developing a smart speaker prototype that has the functionalities of both in the same Raspberry Pi. Users can invoke a virtual assistant by saying the hot words and can leverage the best services of both eco-systems. This paper also explains the complex architecture of Google Assistant and Amazon Alexa and the working of both assistants as well. Later, this system can be used to control the smart home IoT devices.