At any conference, many attendees likely have similar questions: Where's the nearest restroom? Usually, there's an association staff person or member volunteer at the ready to answer. But last year, at the Citrus Valley Association of Realtors' Real Estate Revive Conference, participants turned to Amazon's Echo device instead. The virtual assistant--which many now know on a first-name basis as Alexa--was there to help. "We were looking to get our attendees excited about the future of voice technologies," says Doug Devitre, a developer hired to program a set of Alexa "skills" for the conference.

A recommender system is the kind of service that every B2C startup needs. It can improve sales and user experience, while at the same time helping you understand your customers better. However, if you are setting up a new business, it is difficult and challenging to understand how to best set up a new recommender system. First of all, you need the right data strategy. Secondly, you need to understand what the key performance metrics for your recommender are.

Apple is utilizing some of Siri's smarts to put an end to spam calls. The tech giant unveiled a new feature in its latest mobile software, iOS 13, called'Silence unknown callers' that should make it more difficult for spammers to reach you. Now, when a spammer calls your phone, Siri will automatically route them to voicemail. The tech giant unveiled a new feature in its latest mobile software, iOS 13, called'Silence unknown callers' that should make it more difficult for spammers to reach you The feature was debuted on Monday at Apple's annual Worldwide Developer Conference, where the firm also rolled out a new Mac Pro and Pro display, software updates for the iPhone, iPad, Mac and Watch, as well as other new features. With'Silence unknown callers,' Apple's digital assistant will scan your incoming calls for spammers and unknown numbers so that you don't have to.

Tinder is giving users more tools to express their sexuality. The dating app announced on Tuesday that users can now select up to three terms that they most identify with from a list of nine options. Tinder is giving users more tools to express their sexuality. Users can choose from nine orientations, including straight, gay, lesbian, bisexual, asexual, demisexual, pansexual, queer and questioning. From there, they can decide whether they want that information to show up on their public-facing profile.

In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.

Intelligent Personal Assistants (IPAs) are software agents that can perform tasks on behalf of individuals and assist them on many of their daily activities. IPAs capabilities are expanding rapidly due to the recent advances on areas such as natural language processing, machine learning, artificial cognition, and ubiquitous computing, which equip the agents with competences to understand what users say, collect information from everyday ubiquitous devices (e.g., smartphones, wearables, tablets, laptops, cars, household appliances, etc.), learn user preferences, deliver data-driven search results, and make decisions based on user's context. Apart from the inherent complexity of building such IPAs, developers and researchers have to address many critical architectural challenges (e.g., low-latency, scalability, concurrency, ubiquity, code mobility, interoperability, support to cognitive services and reasoning, to name a few.), thereby diverting them from their main goal: building IPAs. Thus, our contribution in this paper is twofold: 1) we propose an architecture for a platform-agnostic, high-performance, ubiquitous, and distributed middleware that alleviates the burdensome task of dealing with low-level implementation details when building IPAs by adding multiple abstraction layers that hide the underlying complexity; and 2) we present an implementation of the middleware that concretizes the aforementioned architecture and allows the development of high-level capabilities while scaling the system up to hundreds of thousands of IPAs with no extra effort. We demonstrate the powerfulness of our middleware by analyzing software metrics for complexity, effort, performance, cohesion and coupling when developing a conversational IPA.

Russia is requiring dating app Tinder to hand over data on its users -- including messages -- to national intelligence agencies, part of the country's widening crackdown on internet freedoms. The communications regulator said Monday that Tinder was included on a list of online services operating in Russia that are required to provide user data on demand to Russian authorities, including the FSB security agency. Tinder, an app where people looking for dates swipe left or right on the profiles of other users, will have to cooperate with Russian authorities or face being completely blocked in the country. The rule would apply to any user's data that goes through Russian servers, including messages to other people on the app. Tinder was not immediately available for comment.

Apple is offering iPhone users a way to bypass Facebook's and Google's sign-in services when using new apps. That era actually ended quite some time ago. Remember when iPhones were tied to the desktop for updates? So when Apple confirmed Monday that its next desktop operating system upgrade would split up iTunes into three separate apps, for music, TV shows and movies and podcasts, it seemed like an afterthought. "It's a rounding error," something that should have been done a long time ago, says Gene Munster, an analyst and investor with Loup Ventures, iTunes "had gotten way too big."

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the action set changes remains unaddressed. In this paper, we present an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.

Contextual bandit algorithms have gained increasing popularity in recommender systems, because they can learn to adapt recommendations by making exploration-exploitation trade-off. Recommender systems equipped with traditional contextual bandit algorithms are usually trained with behavioral feedback (e.g., clicks) from users on items. The learning speed can be slow because behavioral feedback by nature does not carry sufficient information. As a result, extensive exploration has to be performed. To address the problem, we propose conversational recommendation in which the system occasionally asks questions to the user about her interest. We first generalize contextual bandit to leverage not only behavioral feedback (arm-level feedback), but also verbal feedback (users' interest on categories, topics, etc.). We then propose a new UCB- based algorithm, and theoretically prove that the new algorithm can indeed reduce the amount of exploration in learning. We also design several strategies for asking questions to further optimize the speed of learning. Experiments on synthetic data, Yelp data, and news recommendation data from Toutiao demonstrate the efficacy of the proposed algorithm.