Microsoft is altering the way you activate its Cortana digital assistant with your voice. The first Cortana-powered Invoke speaker now lets users simply summon the digital assistant using "Cortana" instead of the usual "Hey Cortana" command. The change isn't live on Cortana for iOS, Android, or Windows 10, but it will likely appear there soon. Microsoft says it implemented the change last week for the Invoke speaker, and the old'Hey Cortana' still works too. Moving to just Cortana makes Microsoft's assistant as easy to summon as Amazon's Alexa.

A matrix completion problem, which aims to recover a complete matrix from its partial observations, is one of the important problems in the machine learning field and has been studied actively. However, there is a discrepancy between the mainstream problem setting, which assumes continuous-valued observations, and some practical applications such as recommendation systems and SNS link predictions where observations take discrete or even binary values. To cope with this problem, Davenport et al. (2014) proposed a binary matrix completion (BMC) problem, where observations are quantized into binary values. Hsieh et al. (2015) proposed a PU (Positive and Unlabeled) matrix completion problem, which is an extension of the BMC problem. This problem targets the setting where we cannot observe negative values, such as SNS link predictions. In the construction of their method for this setting, they introduced a methodology of the classification problem, regarding each matrix entry as a sample. Their risk, which defines losses over unobserved entries as well, indicates the possibility of the use of unobserved entries. In this paper, motivated by a semi-supervised classification method recently proposed by Sakai et al. (2017), we develop a method for the BMC problem which can use all of positive, negative, and unobserved entries, by combining the risks of Davenport et al. (2014) and Hsieh et al. (2015). To the best of our knowledge, this is the first BMC method which exploits all kinds of matrix entries. We experimentally show that an appropriate mixture of risks improves the performance.

The smart speaker is a tech innovation that's caught fire with consumers, according to the "Smart Speaker Consumer Adoption Report 2018" released by Voicebot.ai in collaboration with PullString and the RAIN Agency. The findings are based on an in-depth national survey of 1,057 U.S. adults, who were queried on ownership, product preference and use cases. About 19.7 percent of U.S. adults are said to have access to a smart speaker, which would mean an absolute number of 47.3 million out of the total U.S. adult population of 252 million, according to Voicebot.ai. "Access" includes adults who have a device in their homes but are not the primary user. A separate survey by NPR and Edison Research completed in November found 16 percent of adult Americans, or about 39 million, own a smart speaker.

Future Today Institute founder Amy Webb has released her annual tech trends report, and much of it focuses on the continuing impact of artificial intelligence. Other trends highlighted by the report include space travel, human gene editing, and a global shortage of data scientists. Webb, a quantitative futurist and professor of strategic foresight at the NYU Stern School of Business, released the report today in a presentation at SXSW in Austin, Texas. Now in its 11th year, the report identifies 225 trends across 20 industries, with roughly 70 of those trends related directly to AI. In 2018, Webb expects the AI cloud and marketplaces for algorithms will continue to grow and the first personal robots will come to market.

Those with digital home assistants know this phenomenon all too well. You ask Siri, Google or Alexa to hook it up with the facts, they provide an answer, but then you have a follow-up question. In order to ask that follow-up question, you have to say "Hey, Siri," "Hey, Google" or "Alexa" all over again. It's a true annoyance in this first-world we live in. The feature is available on all hands-free Alexa-enabled devices, like the Echo, Echo Dot and Echo Spot.

In the years to come, what will be the biggest improvement in AI-powered digital assistants? It's likely to be the ability to accommodate a fundamental aspect of being human: The fact that we all have different personas, we show different facets of ourselves depending on where we are and who we are with, and our personas change over time. And different personas want different things from their AI assistants. Assistants that can understand your personal circumstances are less likely to remind you to pick up your rash prescription as you drive by the pharmacy if there are other people in the car, bug you about work email at home, or keep suggesting fun nightclubs if you've just had a baby. That was the message from Sunday's panel on "Designing the Next Wave of Natural Language and AI" at the SXSW festival in Austin, Texas.

Orange Bank has entered the banking market with Djingo, a virtual advisor powered by IBM's Watson. "The virtual advisor brings customers a unique experience allowing them to interact with their bank when and where they want," said André Coisne, CEO of Orange Bank. For the French, mobile banking is becoming the preferred mode of interaction with their bank. Today, nearly two out of three (63%) French people have downloaded their bank's app on their mobile, and almost half (47%) consult their app at least once a week*. Orange Bank offers a new quality of customer services through a virtual advisor called'Djingo', which is powered by IBM Watson delivered through IBM Services.

ABSTRACT A method to produce personalized classification models to automatically review online dating profiles on Tinder, based on the user's historical preference, is proposed. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings from a FaceNet model were used as the features to describe an individual's face. A user reviewed 8,545 online dating profiles. For each reviewed online dating profile, a feature set was constructed from the profile images which contained just one face. Two approaches are presented to go from the set of features for each face to a set of profile features. A simple logistic regression trained on the em-beddings from just 20 profiles could obtain a 65% validation accuracy. A point of diminishing marginal returns was identified to occur around 80 profiles, at which the model accuracy of 73% would only improve marginally after reviewing a significant number of additional profiles. Index Terms-- facial classification, facial attractiveness, online dating, classifying dating profiles 1. INTRODUCTION Online dating has become a commonplace in today's society.

This year will finally deliver the benefits of Deep Learning to mobile platforms. We expect significant improvements in privacy, personalization, offline functionality and cost of services across all mobile application segments. Alexa, Cortana and Siri will soon live on our phones, answering questions, translating and being helpful even when we're traveling abroad or hiking off-the-grid. Video games will become more entertaining, challenging and engaging even when we play against the computer. Video streaming will take less of our bandwidth and mobile data, while the image quality will improve.

STD and HIV cases have increased nationwide and gone up and down California. And Long Beach now has the state's second-highest rate of chlamydia and third-highest rates of gonorrhea and syphilis. No one factor explains the increases, but health officials point to the popularity of online dating apps, casual hookups and evidence that young people who are busy and otherwise healthy often don't bother with condoms or routine checkups.