Inc. defended the privacy features of its Alexa digital assistant -- and introduced some new tools to reassure users – following months of debate about the practices of the technology giant and its largest competitors. The company plans to roll out a feature that lets users of the Alexa voice-based assistant automatically delete their verbal recordings regularly, on a rolling three-month and 18-month basis. Previously, Alexa users had to manually delete their stored voice recordings on a companion website. "We care about this," Dave Limp, the leader of Amazon's devices and services business, said of privacy during a press event at the company's headquarters in Seattle. "Privacy is absolutely foundational to everything that we do in and around Alexa."

The use of multiple digital devices to support people's daily activities has long been discussed.11 Multi-device experiences (MDXs) spanning multiple devices simultaneously are viable for many individuals. Each device has unique strengths in aspects such as display, compute, portability, sensing, communications, and input. Despite the potential to utilize the portfolio of devices at their disposal, people typically use just one device per task; meaning they may need to make compromises in the tasks they attempt or may underperform at the task at hand. It also means the support that digital assistants such as Amazon Alexa, Google Assistant, or Microsoft Cortana can offer is limited to what is possible on the current device.

Is there a wireless earbud that answers to the name Alexa in your future? Amazon is staging an event Wednesday in Seattle to introduce its fall lineup of devices. At last year's event, Amazon introduced 70 products, including updates to the Echo speaker line, a microwave that answered to Alexa and a DVR for cord-cutters. This year, CNBC reported, an Alexa earbud could top the list. Apple dominates the earbuds market with the help of Siri.

Alexa, will you stop recording and storing my queries? You won't get a response from Alexa to that one, but we got the straight answer from the man who runs the devices division at Amazon. The company has no plans to stop that practice. Sure, senior vice-president Dave Limp this week announced new privacy controls that would let users opt in to automatically delete all recorded queries either every three or 18 months, but you can't opt-out of the recording process. Every time you ask Alexa a question and awake it with the "Alexa" command, Amazon records the question and archives it, to help it become a better personal assistant.

Amazon has release a flurry of new hardware and software updates that expand on its popular series of Echo smart speakers and transform Alexa into celebrities like Samuel L. Jackson. For the first time ever, Amazon announced that it is letting Echo users turn Alexa into celebrities for a'special price' of $0.99 for anyone that purchased a speaker in 2019. Samuel L. Jackson will be among the first celebrities voice integrations and will come in both explicit (presumably with lots of f-bombs) and clean versions. More celebrities are set to arrive on the device, though Amazon hasn't yet hinted at who those additions may be. The first-ever high-end smart speaker from Amazon will be called the Echo Studio and is being positioned as a companion to the company's HD streaming service The company also announced new additions to its Echo hardware, including its first-ever high-end smart speaker which it's calling the'Echo Studio.'

A programmer at an internet-search company, Caleb Smith, wins a competition to spend a week at the CEO's estate, tucked away in the mountains. But he learns that he was chosen to be the human half of a Turing Test, a method of determining if a computer is capable of thinking like a real person. Tasked with evaluating Ava, a beautiful cutting-edge robot, they soon find that she is capable of more than they could have conceived. Her - Oct. 14, 7 p.m. Set in a near future in sunny Los Angeles, 2013's Her -- Oscar winner for Best Original Screenplay -- follows Theodore Twombly, a recently-divorced man who writes personal letters for other people for a living. Lonely and mourning his relationship, he begins using an advanced operating system (voiced by Scarlett Johansson) -- think Amazon's Alexa or Google Home -- to which he forms a strong bond.

Yesterday, we talked about how Facebook has decided to monitor political speech. Today, we want to tell you about an area where Facebook is boldly going forward - dating. Facebook recently launched this new feature in the U.S. after testing it overseas. We wanted to know how people should feel about trusting Facebook in this particularly sensitive area since the company has long been under scrutiny for the way it handles users' data, so we've called Lisa Bonos. She writes about dating and relationships for the Washington Post.

This article presents the emerging topic of dynamic search (DS). To position dynamic search in a larger research landscape, the article discusses in detail its relationship to related research topics and disciplines. The article reviews approaches to modeling dynamics during information seeking, with an emphasis on Reinforcement Learning (RL)-enabled methods. Details are given for how different approaches are used to model interactions among the human user, the search system, and the environment. The paper ends with a review of evaluations of dynamic search systems.

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.

In many real-world applications, e.g. recommendation systems, certain items appear much more frequently than other items. However, standard embedding methods---which form the basis of many ML algorithms---allocate the same dimension to all of the items. This leads to statistical and memory inefficiencies. In this work, we propose mixed dimension embedding layers in which the dimension of a particular embedding vector can depend on the frequency of the item. This approach drastically reduces the memory requirement for the embedding, while maintaining and sometimes improving the ML performance. We show that the proposed mixed dimension layers achieve a higher accuracy, while using 8X fewer parameters, for collaborative filtering on the MovieLens dataset. Also, they improve accuracy by 0.1% using half as many parameters or maintain baseline accuracy using 16X fewer parameters for click-through rate prediction task on the Criteo Kaggle dataset.