Artificial intelligence promises to change customer relationships with banks. As more customers bring devices such as Amazon's Alexa and Google Home into their residences, forward-looking banks can offer automated services to help users perform tasks such as requesting an address change or submitting an application for a credit card or personal loan. In a recent report on the projected impact of AI on the banking and finance industry, the World Economic Forum warns that small and midsize banks struggle to find their footing in this rapidly changing environment. Firms with fewer assets now lag behind larger investment firms when it comes to AI and digital transformation, according to the report, "The New Physics of Financial Services," which cites a survey by Digital Banking Report that found that 48 percent of banks with more than $50 billion in assets have already deployed an AI solution. That's compared to banks with $1 billion to $10 billion in assets, of which only to 7 percent have an AI solution.

Amazon's Echo speakers represent the Next Big Thing in whole-home wireless audio: speakers that let you find and play music using only voice commands. And some of them sound quite good, although all of them are somewhat lacking when it comes to deep bass. To solve that problem, Amazon recently unveiled the Echo Sub, a subwoofer designed specifically to mate with the second-generation Echo and Echo Plus speakers, adding much deeper bass to their sonic palette. Is the Echo Sub worth adding $130 to your budget for smart speakers? The Echo Sub is quite diminutive as subwoofers go. The cylindrical, molded-plastic enclosure measures a mere 8 inches tall and 8.3 inches in diameter.

I am sure I am not alone in being besieged by emails and social media messages about GDPR. Panic has set into the nation. The sheer volume of unsolicited and pressuring communications with GDPR offerings seems slightly ironic. Like everyone, I am fed up with the hype, the veiled threats, the additional pressure on all us mugs who are running businesses or self-employed. Luke Johnson referred to running a business as a vale of tears in his Times articles recently, with bad debts, cyber security issues, client complaints, industrial accidents and so forth.

Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users' embedding, that helps better rating prediction of users. Most existing recommender systems focus on modeling of ratings and implicit feedbacks ignoring users' explicit feedbacks. Explicit feedbacks can be used to validate the reliability of the particular users and can be used to learn about the users' characteristic. Users' characteristic mean what type of reviewers they are. In this paper, we explore three different models for recommendation with more accuracy focusing on users' explicit feedbacks and implicit feedbacks. First one is RHC-PMF that predicts users' rating more accurately based on user's three explicit feedbacks (rating, helpfulness score and centrality) and second one is RV-PMF, where user's implicit feedback (view relationship) is considered. Last one is RHCV-PMF, where both type of feedbacks are considered. In this model users' explicit feedbacks' similarity indicate the similarity of their reliability and characteristic and implicit feedback's similarity indicates their preference similarity. Extensive experiments on real world dataset, i.e. Amazon.com online review dataset shows that our models perform better compare to base-line models in term of users' rating prediction. RHCV-PMF model also performs better rating prediction compare to baseline models for cold start users and cold start items.

After a few weeks of waiting, Roku's promised Google Assistant control is here. If you're using a TV or player running at least Roku OS 8.1, you can link the Google Home app to your Roku account and control core functions using only voice and an "on Roku" suffix. You can launch channels, search for shows and control playback on most devices, while TV owners can turn on the set, adjust volume or switch inputs. The phrasing can occasionally get awkward -- it's not intuitive to say "hey Google, pause on Roku" when you have to answer the door. This won't do anything if you prefer Alexa and other assistants, for that matter.

Many observers speak of the smart-home market as if it were a single entity, but analysis of companies in the sector founded over the past ten years shows that subsectors have peaked at different times in terms of investment value and volume. Investment volume in the components sector, for instance, peaked in 2010, while the peak year for smart-kitchen startups was 2015. Virtual assistants--AI companions in the form of smart speakers--clearly caught on in 2017. This is still an emerging, immature market because use cases are limited and the cost of entry is high. But we anticipate that demand for voice-activated assistants will continue to grow rapidly over the next three to five years as AI capabilities mature. We also expect a virtuous cycle to help drive growth: the more smart-home gadgets connect to these devices, the more popular both the devices and the gadgets they enable will become.

Come join us as we dive into how we're applying deep learning across Twitter and solving some of the challenges our engineers face. In order to attend you must RSVP on the registration link below. AGENDA: 6pm Doors Open 6:30pm Tech Talks Begin 7:15pm Q&A 7:30 - 8pm Networking TOPICS INCLUDE: CHALLENGES IN RECOMMENDER SYSTEMS - ASHISH BANSAL "Twitter has amazing and unique content that is generated at an enormous velocity internationally. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Approaches span collaborative filtering and content based recommendation systems for different use cases. This talk gives insight into unique recommendation system challenges at Twitter's scale and what makes this a fun and challenging task."

As a CIO, regardless of the industry you are in, you share common opportunities and potential for advancements with other CIOs when it comes to technology. These might include theft of materials, physical security of staff and customers, doing more with fewer employees and contributing to the profitability of your company. In this column, we are going to examine some hospitality technology experiments with emphasis in the casino area. The first one is biometrics, the technical term for body measurements and calculations. It has long been used as a form of identification and access control. But with the proliferation of voice recognition devices (think Amazon Alexa, Apple Siri, and Google Assistant), biometric technology has moved into guest services and the Wynn Las Vegas Casino is currently attempting to use this as a big competitive advantage.

AI for short -- is already changing the world in countless ways. Millions of people use devices like Amazon Echo and Google Home to find out more about products and services they need or to control their home environment. Virtually all automakers are exploring self-driving car technology. Retailers are seriously considering plans for delivery drones. AI has transformed the way businesses and health services operate in a short timeframe.

This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Re's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks.