Tinder, the most popular dating app in the world, has banned teens under the age of 18 but it's not stopping them from signing up. A jilted Australian doctor pleaded guilty Monday to sending 9,000 abusive and threatening messages to her former Tinder date, according to a new report. Radiologist Denise Jane Lee, 40, of Sydney, copped to four of 10 charges against her ahead of a scheduled five-day hearing in the Downing Centre Local Court, the Australian Associated Press reported. Lee, who was arrested in February 2017, copped to three counts of using a carriage service to harass, menace or offend and one count of intimidation, according to the report. Six additional charges were withdrawn.

AI will grow into a $118.6 billion industry by 2025. That isn't something you should take lightly. AI has become an integral part of our everyday lives. When we ask Alexa to play our favorite song, use Google Maps to find our way to a new Chinese restaurant, or even use portrait mode to take pictures of our friends – we're adopting AI without even realizing it. Indeed, the way we carry out generic tasks has shifted far beyond our imagination since the rise of AI.

Digital agencies today are brimming with ideas that can help brands integrate with voice-enabled technology. For Niraj Ruparel, National Head- Mobile, Mindshare India, conversational commerce using voice skill technology in India means serious business. In conversation with exchange4media, he delves into the nuances of voice technology, how brands can ensure that voice interaction for users is a seamless experience and what is working in the favour of Voice as the next big digital trend. The agency's tryst with voice began in January this year, explains Ruparel after global giants Google and Amazon recognised the potential for voice in India. "Voice is pretty big in Tier 2 markets, which is in terms of the penetration or how we reach out to audiences in the rural market. If you talk about the ecosystem per se, we're talking about close to 100 crore active sim cards in India and almost 450 million sim cards are resting on feature phones where the only mode of communication is Voice, so that plays a dominant role there. But now we see those 550 million SIMs which are sitting on 400 million smartphones, 30 per cent of those people have now started querying on Voice Assistants."

Science fiction is quickly becoming everyday reality. Chatbots, robots, digital assistants, automated vehicles, virtual assistants, and much more... are the products of artificial intelligence (AI), which is already transforming entire industries. An infographic by TechJury, provider of one-step tech guides and product reviews, provides a detailed overview of AI. The infographic begins with a timeline of AI, starting in the mid-20th century with the "father of theoretical computer science and artificial intelligence," Alan Turing, who developed the "Turing test" for determining what qualifies as artificial intelligence. The infographic goes on to outline various classifications of AI, provides examples of AI technology, highlights statistics about the AI market, and lists the companies and countries at the forefront of the AI race.

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Y et, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.

Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic regression or naive Bayes, which make strong assumptions and lack guarantees on the accuracy of the estimated probabilities. In this paper, we suggest a simple approach to estimating these probabilities that avoids these shortcomings. Our approach follows from the observation that missingness patterns in real data often exhibit low nuclear norm structure. We can then estimate the missingness probabilities by feeding the (always fully-observed) binary matrix specifying which entries are revealed or missing to an existing nuclear-norm-constrained matrix completion algorithm by Davenport et al. [2014]. Thus, we tackle MNAR matrix completion by solving a different matrix completion problem first that recovers missingness probabilities. We establish finite-sample error bounds for how accurate these probability estimates are and how well these estimates debias standard matrix completion losses for the original matrix to be completed. Our experiments show that the proposed debiasing strategy can improve a variety of existing matrix completion algorithms, and achieves downstream matrix completion accuracy at least as good as logistic regression and naive Bayes debiasing baselines that require additional auxiliary information.

I am not exactly a religious type of person, but I think there's a point where our humanity should be put at stake. It's been a year or so since I wrote my last post about Artificial Intelligence and its branches such as Machine Learning and Deep Learning. As a technology enthusiast I am fascinated and intrigued by the idea of a virtual assistant like Cortana from Halo or Mass Effect's VI (Virtual Intelligence). But as things are getting more and more advanced, the achievements look like we're enslaving ourselves to technological God we are creating ourselves. I am not exactly a religious type of person and I am not going to talk about whether it is good or wrong to have faith in Someone above us (which - for the record - I do), but I think there's a point where our humanity should be put at stake.

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With conversational commerce mushrooming throughout the retail world, it surely must have the feel of déjà vu for the iconic 1-800-Flowers.com Founded in 1976 and branded with its workhorse 1-800-Flowers phone number in 1986, the company can point to its penchant for speaking directly to its customers as a reason for its longevity -- first through the phone and then across the internet. The process of conversational commerce uses remarkably intuitive technology tools such as voice messaging and chatbot applications to facilitate seamless interactions between brands and shoppers to drive transactions or trigger service. Voice protocols are emerging as the dominant technique fueling conversational commerce, notably voice assistants such as Apple Siri, Google Assistant and Amazon Alexa. In addition, text-enabled chatbots are proliferating to help consumers through human-like conversations.

Segmentation, Targeting and Positioning (STP) is a common strategic model in today's marketing approach. It reflects the increasing popularity of customer centric marketing strategies over product differentiation strategies. The audience focused approach in marketing e.g. STP therefore goes hand in hand with marketing personas. The popularity of segmentation in the strategy derives on the one hand from past limitations of CRM and ad-tech systems as well as a dependency on human decision making in the STP process on the other hand.