Earlier this year, my co-author, Malay Upadhyay, and I released our new book, The AI Dilemma, a leadership guide to assess enterprise AI maturity and explore AI's impact in diverse industries. Over the past year, during COVID-19, I have been writing a series of blogs for CEOs and Board Directors and executive leadership teams to advance their knowledge in AI having develop a comprehensive AI set of skills that I called The Brain Trust. Over fifty skills were defined with examples of AI innovations. The response has been overwhelmingly positive. Why I have been writing these blogs is to drive an increased sense of urgency of board directors and leadership teams to internalize that human civilization as we know it is changing at a clip that is unprecedented in human history.

Artificial intelligence (AI) and machine learning (ML) have made the leap from futuristic technology to an integral part of our daily routines, from getting directions from A to B to asking Siri to play our favorite song. At work, AI enriches our lives by automating the time-consuming, mundane tasks we used to do ourselves. As an all-around data analytics evangelist at ServiceNow, I've seen firsthand how connecting data to automation can pay off--literally. And in ways you might never expect. Done right, data analytics can capture and maximize sales leads.

Amazon Alexa is one of today's go-to voice assistants. Available on hundreds of devices, from smart speakers to displays and thermostats, Alexa grows more popular every day, with over 100 million device owners and 900,000 registered developers producing Alexa-powered products. Speaking of the latter, Alexa Live 2021 has finally arrived. This year's free virtual symposium is a great place to learn about all of the new developer tools and services that Alexa will be capable of in the near future. Here's everything announced at this year's event.

In our high-speed, multi-tasking culture, fundamental shifts are happening in the way we interact with search technology. Today, mobile devices are the source of 60% of all online searchers. As voice-to-text technology has improved on smartphones and other devices, so has adoption of voice-based commands. In their research recap "Prepare for the Voice Revolution" PWC reports the majority of survey respondents said searching online with voice assistants--like Apple's Siri and Amazon's Alexa--is easier, more convenient, and faster than speaking to a human or texting on a phone. Although younger mobile-first consumers are driving adoption, PWC states, they aren't using the tech as frequently as their 55 counterparts.

Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions, depending on the variety of users' sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user's state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at https://github.com/stefanosantaris/sar.

Self-service is a hot topic in 2021! From chatbots to AI, machine learning, knowledge management and more, self-service is influencing the way customers interact with businesses. Through multiple communication channels such as virtual assistants, chat, and surveys, you can understand customer and employee questions--and respond quickly and effectively with self-service. And if you don't know by now, self-service solutions are essential to elevating the customer experience and improving operational efficiency. We recently highlighted some amazing real-world self-service topics and results at Engage21!

Google will ban'sugar daddy' apps from its Google Play app store from September 1 this year, the firm has quietly revealed in an update. Sugar daddy, or'compensated sexual relationship' apps, allow older users to pay younger users in return for sexual intimacy. Users are mostly older males, known as'sugar daddies', and younger females ('sugar babies'), although there are'sugar mummies' too. Examples of sugar dating apps include My Sugar Daddy, Spoil, SDM, Elite Millionaire Singles and Seeking Arrangement. According to one young user, sugar daddy apps have earnt them a whopping £18,000 a month – enough to pay for their university degree.

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.

AFTER A LONG and lonely lockdown, Theresa Causa was ready for love. To find it, the 40-year-old nurse practitioner in San Antonio turned to the new dating app "S'More," which helps users pair up by literally shifting the focus from physical appearances to mutual goals and interests. When matches first connect, they see only blurred versions of each other's profile photos, along with bios, hobbies and answers to prompts like "What are your top 3 qualities in a match?" As they exchange messages, their photos gradually un-blur. "I was, like, 'This is for me,'" said Ms. Causa.

Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user's trust in the recommendations, and the analyst's ability to scrutinize a model's outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. The result is a ranking model that aptly captures both debiased and explainable user preferences. Finally, we perform an empirical study on three real-world datasets that demonstrate the advantages of our proposed models.