Chain restaurants of all culinary persuasions have experimented with virtual assistants, which provide another digital channel through which customers may order food. However, few quick-service chains are using artificial intelligence (AI) to help humans take orders more quickly and personalize service. Here, Papa John's thinks it's cracked the code with its AI-assisted call center. The call center initiative, dubbed PapaCall, proved critical during the COVID-19 crisis in enabling consumers leery of human contact to order pizza, beverages, and other food, says Justin Falciola, Papa John's chief insights and technology officer, who oversaw the rollout of the service. Get the latest insights with our CIO Daily newsletter.

The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com search and recommendation system. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional machine learning product can be built from existing models.

Do you live in a Smart Home? Can you control your lighting, heating, and electronic devices with nothing more than a smartphone? It's no secret that demand for smart homes and devices has gone from novelty to necessity. Artificial Intelligence (AI) assistants like Siri and Alexa have exploded in popularity and have been accepted into millions of homes. Many consumers have come to not only accept the help of these devices but have come to rely upon them. Can you even begin to imagine life today without social media, smartphones, or GPS systems?

Get the day's biggest stories sent direct to your inbox so you never miss a thing Artificial Intelligence (AI) has become a staple of our daily lives, from Siri to Google Assistant which can control our phones, computers and even homes. The world of media has explored the advancement and potential dangers of rapidly advancing AI for decades, films such as Blade Runner and 2001: A Space Odyssey have touched on the themes of what happens when AI grows beyond human control. But how much does this affect our perception of AI and its involvement in our daily lives? Using data from Google Search Trends and Linkfluence, new research from Ebuyer has revealed that globally almost 3 million people had searched negative themes around AI online. The research discovered that the biggest search queries included "Can artificial intelligence be dangerous?"

AI is evolving on fast pace. Financial organizations are already using AI technologies to identify fraud and unusual transactions, personalize customer service, help make decisions on creditworthiness, using natural language processing on text documents, and for cybersecurity and general risk management. Over the past decades, banks have been improving their methods of interacting with customers. They have tailored modern technology to the specific character of their work. As an example, in the 1960s, the first ATMs were installed, and ten years later, there were already cards for doing transactions and payment.

Already, financial institutions are using AI technology to detect fraud and other unusual transactions, personalize services, make credit decisions, and use natural language processing on text documents. Banks have improved their customer service over the years. Modern technology has been tailored to their specific work. In 1960, the first ATMs were built. Ten years later, payment and transaction cards were available.

I've always been reticent to publish the performance of the Position trading system: I've even dismissed the record table for the subscribers, without any complaint from them. It should be complex to explain why, it's something intuitive, but it is related to my distrust in backtesting. First, I've always tested forward, not behind. The model, or as I call it now: r.Virgeel has taken shape during four years and is performing well. It is still under development: new ideas are passing as clouds at the moment.

Voice Assistant Technology had a market of its own even before the pandemic hit. Covid-19 and the fear that comes with it have just acted as a catalyst in the adaptation process and radically reshaped consumers' choices. If we go back to 2019, we see that home and voice-connected devices were moving towards becoming consumers' commerce command focus. The trend revolves around millennials who own six devices run on voice assistants except for their phones, and over 31% seem to have made purchases using these devices. With these numbers, we can deduce that the world was already moving towards a more Voice technology and AI-led market before'Global Pandemic' shook our lives.

These days, due to the increasing amount of information generated on the web, most web service providers try to personalize their services. Users also interact with web-based systems in multiple ways and state their interests and preferences by rating the provided items. This paper proposes a framework to predict users' demographic based on ratings registered by users in a system. To the best of our knowledge, this is the first time that the item ratings are employed for users' demographic prediction problems, which have extensively been studied in recommendation systems and service personalization. We apply the framework to the Movielens dataset's ratings and predict users' age and gender. The experimental results show that using all ratings registered by users improves the prediction accuracy by at least 16% compared with previously studied models. Moreover, by classifying the items as popular and unpopular, we eliminate ratings that belong to 95% of items and still reach an acceptable level of accuracy. This significantly reduces update costs in a time-varying environment. Besides this classification, we propose other methods to reduce data volume while keeping the predictions accurate.