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.

The traditional offline approaches are no longer sufficient for building modern recommender systems in domains such as online news services, mainly due to the high dynamics of environment changes and necessity to operate on a large scale with high data sparsity. The ability to balance exploration with exploitation makes the multi-armed bandits an efficient alternative to the conventional methods, and a robust user segmentation plays a crucial role in providing the context for such online recommendation algorithms. In this work, we present an unsupervised and trend-responsive method for segmenting users according to their semantic interests, which has been integrated with a real-world system for large-scale news recommendations. The results of an online A/B test show significant improvements compared to a global-optimization algorithm on several services with different characteristics. Based on the experimental results as well as the exploration of segments descriptions and trend dynamics, we propose extensions to this approach that address particular real-world challenges for different use-cases. Moreover, we describe a method of generating traceable publishing insights facilitating the creation of content that serves the diversity of all users needs.

For most of human history, people have been good at predicting future technologies. Today however, predicting things even just 5 years ahead seems to end up futile. India is at the cusp of an e-commerce revolution. Although e-commerce has been making the rounds in the country for over a decade, it is in recent years that an appropriate ecosystem has begun to fall in place. Factors such as internet access, staggering penetration of mobile phones and robust investment have driven the growth of this industry and if current projections are anything to go by, India is en route to becoming the world's fastest growing e-commerce market.

Marko Karjnovic unveiled his ideas at The Museum of the Future as part of the World Government Summit in Dubai. The Hybrid Intelligence Biometric Avatar (HIBA), will understand the feelings of people connected to it, take on their personas, exchange information with them and even become part of the fabric of their brains. Mr Karjnovic, who has produced the exhibit, explained: "It is very similar to the work of Elon Musk – it is an open source platform for humanity. "HIBA will have the ability to connect the minds of the most clever of us, combining those minds with everything it can find out practically and put it all together in hybrid intelligence."