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.
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.
Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as "chill" or "jogging" that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or "autotags") furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ''cold-start problem'' common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system."
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling. We show that the weighted trace-norm regularization indeed yields significant gains on the highly non-uniformly sampled Netflix dataset. Papers published at the Neural Information Processing Systems Conference.
User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is often difficult to collect it is important to develop effective models that take advantage of the more widely available implicit feedback. We introduce a probabilistic approach to collaborative filtering with implicit feedback based on modelling the user's item selection process. In the interests of scalability, we restrict our attention to tree-structured distributions over items and develop a principled and efficient algorithm for learning item trees from data.