It's no secret that artificial intelligence (AI)-enabled devices are listening in or observing what we're doing, collecting and digitising massive amounts of data. What underpins this entire enterprise is the work under the hood – maintaining data lakes and warehouses that store the data, performing data engineering tasks to establish and enhance the structure, using business intelligence and statistical analysis to make sense of it and, finally, training an AI program to make predictions that yield more "intelligent" decisions. For example, many of us are familiar with virtual assistant technologies that take in information, digitise the data, put it into data pipelines, and analyse it so the AI algorithms become better in near real-time, fine-tuning it specifically to one's world. What makes an AI algorithm potent is when it can start making connections with other data sets and data points. This results in the creation of a profile that encompasses your shopping behaviour, what you're doing at home, what music you like listening to, what you like eating, and even where you live – in suburbia or in the city.

The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal biases. Most metrics attempting to quantify disparities resulting from ML algorithms focus on differences between groups, dividing users based on demographic identities and comparing model performance or overall outcomes between these groups. However, in industry settings, such information is often not available, and inferring these characteristics carries its own risks and biases. Moreover, typical metrics that focus on a single classifier's output ignore the complex network of systems that produce outcomes in real-world settings. In this paper, we evaluate a set of metrics originating from economics, distributional inequality metrics, and their ability to measure disparities in content exposure in a production recommendation system, the Twitter algorithmic timeline. We define desirable criteria for metrics to be used in an operational setting, specifically by ML practitioners. We characterize different types of engagement with content on Twitter using these metrics, and use these results to evaluate the metrics with respect to the desired criteria. We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users. Overall, we conclude that these metrics can be useful tools for understanding disparate outcomes in online social networks.

In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.

Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage. Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operating a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.

NEARLY 40 AND SINGLE, Tracy Morris had been online dating for a while, but she wasn't about to settle. Not for the man who turned out to have a whiny, mewling voice; its weak quality made her feel unsafe. Nor for the one who spoke in short, chopped, angry sentences. "Voice is a pheromone to me," said Ms. Morris, a McLean, Virginia-based interior designer. "I think it's one of the senses."

The answer is multifaceted, but I'll try to make it simple: Training can be demanding. For companies, it takes time to organize courses, curricula and other content; track progress; and demonstrate ROI. Plus, employees don't tend to enjoy standardized training that fails to cater to their needs and takes time from their already busy schedules. These arguably onerous tasks take a fundamental business process and turn it into something many people are reluctant to participate in -- a disadvantage for any company. I strongly believe that great learning keeps organizations from stagnation and irrelevance.

The Conversational AI space has made great strides in the underlying technologies, use cases, and adoption. We are moving past the days of basic question and answer experiences and simple decision trees. Chatbots, voice assistants, and Interactive Voice Response (IVR) solutions have advanced to incorporate sophisticated Natural Language Understanding (NLU) that not only understands a user's Intent, but enables the chatbot to respond appropriately in a way that satisfies the user. Conversational AI solutions are used across a wide variety of use cases and industries -- including customer service, appointment booking, triaging medical symptoms, signing up for an account, and more. The demand for conversational AI continues to grow as users prefer to communicate over digital channels. Businesses are developing chatbots, voice assistant, and IVR experiences to increase customer satisfaction, reduce operational costs, and achieve business goals.

According to reports, the growth of the global OTT marketplace is accelerating much more because of the pandemic. Previously, the compound annual growth rate (CAGR) was 16%. Post-pandemic, this number is expected to reach 19% by 2026, the market value could reach US$438.5 billion. King of the OTT platforms, Netflix is well-known for providing high-quality content with endless options. Netflix is globally popular for its services, and the secret behind is technologies like AI and machine learning that serve more relevant and intuitive recommendations to audiences.

If you are looking for a free Internet dating site that can give you a good chance of meeting new people, then you might want to consider joining eharmony. They have worked hard to make eharmony a great service for both singles and couples to use. The reason that this dating site has become so successful is because they have put in place features that work to bring singles and couples together. When you sign up for eharmony you will find that your personal profile is the center of attention. One of the best parts about eharmony is that you don't have to be a member of the site to get a chance at meeting a perfect mate. You can simply take a look at the singles who have expressed interest in you and send messages to them up until the time that you feel like talking with them.

The growing prominence of technology has greatly changed the landscape of retail over the years. AI-powered solutions are changing the nature of retail and 2022 will be a year for more change owing to the speed at which innovation is occurring and the added acceleration of digital adoption because of the Covid-19 pandemic. Customers' shopping habits have evolved, and retailers will have to evolve to match their needs. Experts believe that the line between digital and physical will blur in 2022 (source). Consumers will expect the efficiency and convenience that technology provides, in the physical world, and the personalized service that a human sales rep delivers, in the digital world.