Amazon has announced two new programs for Alexa centered around healthcare and retirement homes. Through Alexa Smart Properties, hospitals and senior living communities can run their own custom version of the voice assistant. Retirement homes might tap into Alexa to help residents keep in contact with family and friends, stay in touch with staff, take part in activities and remain engaged with other members of the community. Staff members can use Alexa to broadcast announcements and, of course, the voice assistant can still be used for things like controlling connected devices and smart TVs. Amazon's aim with the healthcare program is to, among other things, let staff members check in with patients without having to enter their rooms. In turn, patients can ask nurses questions, and they'll be able to respond to brief queries without having to leave their station.

Keeping emerging AI trends in mind, marketers must learn how to navigate a world where big data and Automation are essential. After all, keeping up with innovation is now the key to marketing success. Businesses will need to understand and apply new apps, tools, and approaches to thrive. As we recover from the pandemic, businesses are putting more effort into company blogs, social media, and video. By merging these channels using machine learning and Automation, businesses can identify which emerging AI marketing trends could support their needs.

Recommender systems are used in a variety of domains, from e-commerce to social media to offer personalized recommendations to customers. The benefit of recommendations for customers, such as reduced information overload, has been a hot topic of research. However, it's unclear how and to what extent recommender systems produce commercial value. It's challenging to create a reliable product suggestion system. However, defining what it means to be reliable is also a challenging task.

Morgan basked in the feel-good vibes of seeing people find each other--"I love love!"--and reveled in the real-life connections she was able to mastermind: multiple dates in her hometown of Portland, Oregon; someone who was thinking of flying to meet somebody in New York because of the thread; even a short relationship. Even today, people continue to add their pictures to the thread, seeking love all across the United States. If this feels a bit like old-fashioned matchmaking, it is. These operations are often ad hoc, based on platforms like Twitter and TikTok, and--unlike the dating apps, with their endless menu of eligible suitors--hyperfocused on one person at a time. Randa Sakallah launched Hot Singles in December 2020 to solve her own dating blues.

Recent advent in recommender systems, especially text-aided methods and CDR (Cross-Domain Recommendation) leads to promising results in solving data-sparsity and cold-start problems. Despite such progress, prior algorithms either require user overlapping or ignore domain-aware feature extraction. In addition, text-aided methods exceedingly emphasize aggregated documents and fail to capture the specifics embedded in individual reviews. To overcome such limitations, we propose a novel method, named DaRE (Domainaware Feature Extraction and Review Encoder), a comprehensive solution that consists of three key components; text-based representation learning, domain-aware feature extraction, and a review encoder. DaRE debilitate noises by separating domain-invariant features from domain-specific features through selective adversarial training. DaRE extracts features from aggregated documents, and the review encoder fine-tunes the representations by aligning them with the features extracted from individual reviews. Experiments on four real-world datasets show the superiority of DaRE over state-ofthe-art single-domain and cross-domain methodologies, achieving 9.2 % and 3.6 % improvements, respectively. We upload our implementations (https://anonymous.4open.science/r/DaRE-9CC9/) for a reproducibility

Artificial intelligence is a branch of computer science that deals with making intelligent machines and computer programs. It is a broad branch that includes machine learning and deep learning. John McCarthy, a professor emeritus at Stanford University, coined the term artificial intelligence in 1956. The applications of artificial intelligence include voice assistants like Alexa, Siri, and Google Assistant. It is also applied to deep learning models like Luther AI.

Route recommendation is significant in navigation service. Two major challenges for route recommendation are route representation and user representation. Different from items that can be identified by unique IDs in traditional recommendation, routes are combinations of links (i.e., a road segment and its following action like turning left) and the number of combinations could be close to infinite. Besides, the representation of a route changes under different scenarios. These facts result in severe sparsity of routes, which increases the difficulty of route representation. Moreover, link attribute deficiencies and errors affect preciseness of route representation. Because of the sparsity of routes, the interaction data between users and routes are also sparse. This makes it not easy to acquire user representation from historical user-item interactions as traditional recommendations do. To address these issues, we propose a novel learning framework R4. In R4, we design a sparse & dense network to obtain representations of routes. The sparse unit learns link ID embeddings and aggregates them to represent a route, which captures implicit route characteristics and subsequently alleviates problems caused by link attribute deficiencies and errors. The dense unit extracts implicit local features of routes from link attributes. For user representation, we utilize a series of historical navigation to extract user preference. R4 achieves remarkable performance in both offline and online experiments.

Machine learning has developed a variety of tools for learning and representing high-dimensional distributions with structure. Recent years have also seen big advances in designing multi-item mechanisms. Akin to overfitting, however, these mechanisms can be extremely sensitive to the Bayesian prior that they target, which becomes problematic when that prior is only approximately known. We consider a multi-item mechanism design problem where the bidders' value distributions can be approximated by a topic model. Our solution builds on a recent robustification framework by Brustle et al., which disentangles the statistical challenge of estimating a multi-dimensional prior from the task of designing a good mechanism for it, robustifying the performance of the latter against the estimation error of the former. We provide an extension of the framework that allows us to exploit the expressive power of topic models to reduce the effective dimensionality of the mechanism design problem.

The world, as we see it, is digitizing itself from its peripheral edges to its very core as technology evolves leap by leap. And in this technologically advanced world, concepts such as'artificial intelligence,' 'machine learning,' or'data science' are no longer a figment of the imagination of sci-fi authors like Asimov but a reality that is very much here. The new-age tech is now paving its way to not only establishing itself subtly and not-so-subtly in our everyday lives but also leading the world to its fourth industrial revolution. The amount of data generated by both humans and machines far outpaces humans' ability to absorb, interpret, and make complex decisions based on that data. But a chunk of unstructured data is meaningless until it is converted to generate valuable, meaningful information.

Artificial intelligence (AI) has made important strides in transforming business practices and processes across a wide range of sectors, by helping organisations streamline operations, manage risk and reduce costs. This is especially true when it comes to critical activities such as marketing, customer service and sales, which have been identified by Forbes as the top three areas that AI can enhance business growth. Thanks to AI, we are now seeing a proliferation of digital assistants, also known as predictive chatbots. These are application programs that not only understand natural language voice commands, but can also simulate a conversation with users and complete tasks on their behalf. Using AI and machine learning, combined with a user's history, preferences, and other information, they can respond to difficult questions, make recommendations, and even start conversations.