Bollywood superstar Amitabh Bachchan will be the first Indian celebrity to lend his voice to Amazon's Alexa digital assistant starting next year, as the Silicon Valley giant expands its presence in the significant market. The 77-year-old actor has been a household name in India for nearly half a century, and his deep baritone is instantly recognisable to listeners in the country of 1.3 billion. Foreign firms such as Amazon have spent tens of billions of dollars in India in recent years as they fight for a piece of the Asian giant's burgeoning digital economy. In a blog post on Monday, Amazon India said Bachchan's "voice experience" feature will become available for purchase on Alexa next year. "It will include popular offerings like jokes, weather, shayaris (poetry), motivational quotes, advice and more," the firm said.

Qlik, a data integration and analytics cloud platform announced yesterday that it has upgraded its AI assistant, Insight Advisor to provide customers with even more robust and targeted cloud analytics solutions. With Insight Advisor, users can expect search-based visual analysis (NLP-driven), conversational analytics (chat), and associative insights. These augmented analytics aim to expose hidden data relationships, assist with creation and data preparation, and integrate advanced calculations for customers. "Analytics users want to do more with their data, but often struggle with where to look or what next steps to take. Insight Advisor gives these users a complete and powerful AI assistant, built directly into Qlik Sense, to help guide them along every step of their data exploration and analysis journey," said James Fisher, chief product officer at Qlik. "Qlik Sense users are only a click or question away from the assistance needed to derive more insights and value from data. And, with every interaction, Insight Advisor learns alongside them, creating a virtuous cycle where they become smarter together, increasing users' data literacy and data usage."

On a rooftop in San Francisco, Replicant CEO Gadi Shamia signs the Series A term sheet with Scott ... [ ] Beechuk of Norwest Venture Partners, who joins the startup's board as part of the financing. Hundreds of millions of consumers use voice assistants such as Amazon Alexa or Apple's Siri for simple tasks in their daily lives. Now, one company wants to take talking bots to the next level: the highly conversational field of customer service. Replicant, named after Blade Runner's genetically engineering humans, is entering uncharted territory with its software that works as a combination between text-based chatbots and real-time AI assistants for customer service reps. On Thursday, the startup announced that it has raised a $27 million Series A funding round for its autonomous call center.

Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business. These systems are in their infancy in the industry and in need of practical solutions to some fundamental research challenges. At Adobe research, we have been implementing such systems for various use-cases, including points of interest recommendations, tutorial recommendations, next step guidance in multi-media editing software, and ad recommendation for optimizing lifetime value. There are many research challenges when building these systems, such as modeling the sequential behavior of users, deciding when to intervene and offer recommendations without annoying the user, evaluating policies offline with high confidence, safe deployment, non-stationarity, building systems from passive data that do not contain past recommendations, resource constraint optimization in multi-user systems, scaling to large and dynamic actions spaces, and handling and incorporating human cognitive biases. In this paper we cover various use-cases and research challenges we solved to make these systems practical.

One of these textual information is the customer comments or reviews. People usually prefer to read the reviews before buying or using a service to make the right decision. This behavior is also common before the existence of the Internet. From this amount of available data, researches attempt to handle and use these data to have a specific and useful knowledge. Sentiment analysis (SA) is the process of determining the opinion or feeling of a piece of text. Sentiment means feelings, attitudes, emotions and opinions. The applications of sentiment analysis are numerous such as politics or political science, law, e-commerce, sociology and psychology. In e-commerce, the sentiment analysis is super useful for gaining insight into customer opinions; once they understand how the customer feels after analyzing their comments or reviews, they can identify what they like and dislike and build things like recommendation systems, or enhance the product or the service.

The Fintech industry, with its focus on efficiency and consumer-centricity, is a disruptive force in the traditionally staid and frequently complacent financial services market. In certain areas, the pace of Fintech disruption has been so dramatic it has forced incumbent institutions to scramble to adapt their offerings to meet changing consumer demands. Increasingly, artificial intelligence and machine learning are the key technologies that enable Fintechs to compete aggressively with legacy players. Below are some of the key ways AI and machine learning are powering continued innovation in the Fintech sphere. One issue with many financial products and services is the fact that they are often designed to meet the needs of large population groups but fail to address more individualized needs and desires.

When Lenovo's Smart Clock first arrived, we were charmed by the tiny minimalist design, clock-centric features and Google Assistant-powered smart home features. The original $80 price was also a plus, but Best Buy just made it a lot more interesting -- it's selling the Smart Clock at $40, or half off. Aside from the cute design and cloth covered body, we appreciated Lenovo's Smart Clock features like the ambient light sensor and sunrise alarm, which gradually brightens the screen thirty minutes before the alarm sounds. Swiping from the top reveals a "play music" button and a toggle for any smart lights, and you can set a "good night" smart home routine that can automatically adjust the temperature and turn off the TV, for instance. All of these actions also work via Google Assistant voice commands.

This papers studies how competition affects machine learning (ML) predictors. As ML becomes more ubiquitous, it is often deployed by companies to compete over customers. For example, digital platforms like Yelp use ML to predict user preference and make recommendations. A service that is more often queried by users, perhaps because it more accurately anticipates user preferences, is also more likely to obtain additional user data (e.g. in the form of a Yelp review). Thus, competing predictors cause feedback loops whereby a predictor's performance impacts what training data it receives and biases its predictions over time. We introduce a flexible model of competing ML predictors that enables both rapid experimentation and theoretical tractability. We show with empirical and mathematical analysis that competition causes predictors to specialize for specific sub-populations at the cost of worse performance over the general population. We further analyze the impact of predictor specialization on the overall prediction quality experienced by users. We show that having too few or too many competing predictors in a market can hurt the overall prediction quality. Our theory is complemented by experiments on several real datasets using popular learning algorithms, such as neural networks and nearest neighbor methods.

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

Natural language processing (or NLP for short) refers to technology that allows computers to understand human language. NLP is what helps computers read, edit and summarize text – as well as enabling natural language generation (NLG), whereby computers generate their own "speech." In other words, NLP is the technology that enables Siri to understand your requests, while NLG means Siri can respond in natural-sounding language. Smart digital assistants like Alexa and Siri are among the best-known examples of NLP in action. Predictive text and email spam filters are earlier examples.