Understanding text, images, and sounds is not a uniquely human prerogative anymore. Artificial intelligence is transforming virtually every business. AI's ability to derive data-driven insights is paving the road to better digital marketing. From the vast data analysis, marketers gain valuable consumer insights and change how they connect brands with their audiences. Why artificial intelligence cannot be separated from digital marketing anymore?

Artificial intelligence is about to change lead generation and conversion as you know it. In the process, it'll have a transformative impact on companies and careers. AI is a blanket term that covers several different technologies. You might have heard of some of them, like machine learning, computer vision, and natural language processing. Even if you don't know much about it, though, you probably use AI-powered technology dozens or hundreds of times per day.

Virtual assistants turn 16 this year and you don't have to look too hard – or speak too loudly – to find them. In fact, there will be around 8 billion voice-based devices by 2023 – more than the world's population today. From Amazon's Echo and Google's Assistant to Apple's Siri, Samsung's Bixby and Microsoft's Cortana, billions of people around the world are using their voices every day to schedule appointments, get directions, play music or get answers quickly-- all things that once required us to tediously type or write. Even Twitter recently announced that users can now audio tweet their inner musings. And yet, despite widespread adoption of voice-based devices in our personal lives, applications based on voice are nowhere as pervasive in our professional lives as they are in our homes.

Voice search is on the rise. We all have known this for ages but the voice search market may actually be growing faster than we expected. Throughout the last few years, smart speakers have been taking the market by storm. With the emergence of Amazon's Alexa (powered by Bing search), Google's Homepod, and Apples' Homepod (both powered by Google), voice search is naturally seeing unprecedented growth. And the growth is likely to surge in the next few years. Companies have already started to fine-tune their marketing and SEO strategies to accommodate this new technology.

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses. We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations. We also survey mothods that add regularization terms that change the behavior of the gradient, making it harder for attackers to achieve their objective. Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds. In addition, we discuss the challenges faced by most of the recent algorithms presenting future research perspectives.

Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single company. Federated learning provides the possibility to bridge the data silos and build machine learning models without compromising privacy and security. Participants sharing common users or items collaboratively build a model over data from all the participants. There have been some works exploring the application of federated learning to recommender systems and the privacy issues in collaborative filtering systems. However, the privacy threats in federated matrix factorization are not studied. In this paper, we categorize federated matrix factorization into three types based on the partition of feature space and analyze privacy threats against each type of federated matrix factorization model. We also discuss privacy-preserving approaches. As far as we are aware, this is the first study of privacy threats of the matrix factorization method in the federated learning framework.

Leviton is expanding its range of smart home switches and outlets with a new Wi-Fi dimmer switch with Amazon Alexa built in. The Decora Voice Dimmer will allow home owners to control their lights with their voice, and it works with all the other things that Amazon Alexa supports. For example, users will be able to say "Alexa, dim the lights to 60 percent," or "Alexa, turn off the kitchen light." Updated to provide links to our hands-on reviews of the Leviton Decora Voice Dimmer, the Leviton Decora Smart Wi-Fi 4-Button Controller, and the Leviton Decora Smart Fan Speed Controller. Leviton already sells Wi-Fi light switches in the Decora range and the new dimmer can be integrated into routines and schedules through the My Leviton control app.

"AI and ML enhance the adoption of IoT by adding the Return on Investment (RoI) layer on top of the IoT stack for both businesses and consumers."- Utility and convenience have been one of the primary forces behind increased adoption of IoT in the Appliances and Consumer Electronics (ACE) sector in India. With Smart IoT powered appliances, end users can control and monitor appliances, set schedules, and automation rules using mobile apps and voice commands from voice assistants such as Google Home, Alexa, and Siri. Acknowledging this market trend, almost all major brands are adding IoT enabled appliances to their catalogue. From smart lighting to smart air conditioners and smart water purifiers, the Indian market has started witnessing the adoption of IoT products.

Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.

Researchers in Germany have compiled a list of more than 1,000 words that will inadvertently cause virtual assistants like Amazon's Alexa and Apple's Siri to become activated. Once activated, these virtual assistants create sound recordings that are later transmitted to platform holders, where they may be transcribed for quality assurance purposes or other analysis. According to the team, from Ruhr-Universität Bochum and the Max Planck Institute for Cyber Security and Privacy in Germany, this has'alarming' implications for user privacy and likely means short recordings of personal conversations could periodically end up in the hands of Amazon, Apple, Google, or Microsoft workers. Researchers in Germany tested virtual assistants like Amazon's Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana, and found more than 1,000 words or phrases that would inadvertently activate each device The group tested Amazon's Alexa, Apple's Siri, Google Assistant, Microsoft Cortana, as well as three virtual assistants exclusive to the Chinese market, from Xiaomi, Baidu, and Tencent, according to a report from the Ruhr-Universität Bochum news blog. They left each virtual assistant alone in a room with a television that played dozens of hours of episodes from Game of Thrones, Modern Family, and House of Cards, with English, German, and Chinese audio tracks for each.