Recommender system is an essential component of web services to engage users. Popular recommender systems model user preferences and item properties using a large amount of crowdsourced user-item interaction data, e.g., rating scores; then top-$N$ items that match the best with a user's preference are recommended to the user. In this work, we show that an attacker can launch a data poisoning attack to a recommender system to make recommendations as the attacker desires via injecting fake users with carefully crafted user-item interaction data. Specifically, an attacker can trick a recommender system to recommend a target item to as many normal users as possible. We focus on matrix factorization based recommender systems because they have been widely deployed in industry. Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem. However, this optimization problem is challenging to solve as it is a non-convex integer programming problem. To address the challenge, we develop several techniques to approximately solve the optimization problem. For instance, we leverage influence function to select a subset of normal users who are influential to the recommendations and solve our formulated optimization problem based on these influential users. Our results show that our attacks are effective and outperform existing methods.

In today's business era, AI chatbots are redefining the way pharma companies interact and engage with their clients. These chatbots mimic human conversation via text or auditory means which is a huge opportunity for the pharma industry to have a one-to-one conversation with their customers, doctors, and patients. Apart from that, by using intelligent virtual assistants, pharmaceutical companies can build a strong relationship with doctors and patients by communicating with them and assisting them directly. The two main areas within this industry that will drastically benefit from developing a pharma chatbot are R&D and marketing. By developing a chatbot, a pharma company can have a virtual digital assistant to provide information to users on various topics, such as how to respond to inquiries on certain health conditions, a complex drug procedure, and the appropriate method of using a certain medical device.

An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand. To this end, we introduce NAM: a Neural Attentive Multiview machine that learns multiview item representations and similarity by employing a novel attention mechanism. NAM harnesses multiple information sources and automatically quantifies their relevancy with respect to a supervised task. Finally, a very practical advantage of NAM is its robustness to the case of dataset with missing views. We demonstrate the effectiveness of NAM for the task of movies and app recommendations. Our evaluations indicate that NAM outperforms single view models as well as alternative multiview methods on item recommendations tasks, including cold-start scenarios.

Smart speakers, like Amazon's Alexa and Apple's Siri, have come under fire over the past few years for'listening' to its owner's conversations. Now, a team of scientists believe they have developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone. Dubbed the'bracelet of silence', the chunky bracelet is fitted with 23 speakers around it that emit ultrasonic signals that drown out any speech of the wearer. While these ultrasonic signals are undetectable to human ears, they leak into the audible spectrum after being captured by the microphones, producing a jamming signal inside the microphone circuit disrupts voice recordings. Scientists developed the ultimate weapon to block the devices' spying abilities - a wearable that jams the microphone.

A former Amazon Executive revealed he switches off his Alexa smart speaker whenever he wants a'private moment' as he doesn't want it listening in. Robert Frederick, a former manager at Amazon Web Services, told BBC Panorama he always turns it off during personal and particularly sensitive conversations. Last year Amazon was forced to admit that some conversations recorded by virtual assistant Alexa were listened to and transcribed by humans. Amazon says human staff listen to less than on per cent of conversations to check for accuracy and the information is made anonymous before they see it. Amazon's Alexa is being placed in an increasing number of devices including televisions, smart speakers and screens The investigative journalism programme is exploring Amazon's rise from online bookstore to tech giant as well as the way it collects data from its customers.

Tata Consultancy Services' Capital Markets Focussed Workflow, Innovative Process Enhancers, and Solutions Backed by the Latest Technologies, Cited as Key Strengths Tata Consultancy Services (TCS), a leading global IT services, consulting and business solutions organization, has been recognized as a Leader in the Everest Group PEAK Matrix for Capital Markets Operations. In an assessment of 24 global service providers offering capital markets operations services, TCS was placed highest for Vision and Capability, as well as Market Impact. Additionally, it was named a Star Performer for having top quartile year-on-year improvement in its scores. TCS' strong position in the overall capital markets segment is attributed to consistent growth in its portfolio with multiple new wins. According to the report, the company has continuously worked on creating solutions backed with the latest technology to help its customers solve operational problems more efficiently.

On Tinder, an opening line can go south pretty quickly. And while there are plenty of Instagram accounts dedicated to exposing these "Tinder nightmares," when the company looked at its numbers, it found that users reported only a fraction of behavior that violated its community standards. Now, Tinder is turning to artificial intelligence to help people dealing with grossness in the DMs. The popular online dating app will use machine learning to automatically screen for potentially offensive messages. If a message gets flagged in the system, Tinder will ask its recipient: "Does this bother you?"

Facing a fridge full of ingredients but still don't know what to cook? Tired of following the same recipes and eager to try something new and creative? Thanks to AI technologies such as image recognition and machine learning, people can now save time, food and money in the kitchen while discovering creative and tasty recipes and even generating their own new and personalized flavours. Facebook has developed an image-to-recipe generation system which enables users to reverse engineer a recipe by simply inputting an image of the dish they want to prepare. First, ingredients and ingredient co-occurrence are generated by exploiting visual features extracted from the food image.

Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies. We consider the problem of low-rank tensor estimation from possibly incomplete, ordinal-valued observations. Two related problems are studied, one on tensor denoising and another on tensor completion. We propose a multi-linear cumulative link model, develop a rank-constrained M-estimator, and obtain theoretical accuracy guarantees. Our mean squared error bound enjoys a faster convergence rate than previous results, and we show that the proposed estimator is minimax optimal under the class of low-rank models. Furthermore, the procedure developed serves as an efficient completion method which guarantees consistent recovery of an order-$K$ $(d,\ldots,d)$-dimensional low-rank tensor using only $\tilde{\mathcal{O}}(Kd)$ noisy, quantized observations. We demonstrate the outperformance of our approach over previous methods on the tasks of clustering and collaborative filtering.

Artificial intelligence (AI), otherwise known as machine learning, is slowly reshaping retail from optimizing back-end supply chain operations to in-store execution. It is also impacting marketing, customer service engagement and anti-fraud activities, according to a report from New York-based information technology industry analyst firm 451 Research. While AI is far from the mainstream, researchers said plenty of retailers are experimenting with how machine learning can be applied in many areas of retail. The report states retailers won't be the only ones needing to adapt to the disruption of machine learning as customers will also face changes in how they view and experience shopping. For AI to work to its full potential, researchers said customers will need to be comfortable with increased data sharing if they want to benefit from personalized shopping experiences via machine learning.