Google's Nest thermostat is something of a godsend. Not only does it keep your humble abode feeling nice and comfortable, but it's also incredibly easy to install and it monitors your HVAC system, which is useful if anything goes haywire. I've got one in my house and I couldn't be happier with it. This little device saves me a good amount of money in heating and cooling costs. If you're looking to pick one up, good news!

Proponents of artificial intelligence are quick to list the myriad ways their tech will serve as extensions of our busy brains. But as Apple, Google, and other companies race to bring their AI creations onto our phones, we're being presented with an opportunity to use these next-gen digital assistants to correct one of our inherent human flaws: poor memory. Tom Gruber, who cofounded the company that created Apple's Siri voice assistant, says the potential for offloading memory-dependent tasks is the first big leap toward making AI assistants that can truly ape human thinking. "The basic pieces of cognition, the fundamental one is memory," Gruber says. Almost all of our daily cognition or computation is memory-based.

AI-empowered technologies' impact on the world is undeniable, reshaping industries, revolutionizing how humans interact with technology, transforming educational paradigms, and redefining social codes. However, this rapid growth is accompanied by two notable challenges: a lack of diversity within the AI field and a widening AI divide. In this context, This paper examines the intersection of AI and identity as a pathway to understand biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves understanding the associations between the individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper proposes the need for a comprehensive approach to fostering a more inclusive and responsible AI ecosystem through the lens of identity.

The rapid growth of location acquisition technologies makes Point-of-Interest(POI) recommendation possible due to redundant user check-in records. In this paper, we focus on next POI recommendation in which next POI is based on previous POI. We observe that time plays an important role in next POI recommendation but is neglected in the recent proposed translating embedding methods. To tackle this shortage, we propose a time-adaptive translating embedding model (TransTARec) for next POI recommendation that naturally incorporates temporal influence, sequential dynamics, and user preference within a single component. Methodologically, we treat a (previous timestamp, user, next timestamp) triplet as a union translation vector and develop a neural-based fusion operation to fuse user preference and temporal influence. The superiority of TransTARec, which is confirmed by extensive experiments on real-world datasets, comes from not only the introduction of temporal influence but also the direct unification with user preference and sequential dynamics.

A deep-dive exploring over 23,000 profiles in Tinder's dating pool has identified the most common, self-reported interests of those looking for love on the mobile app. Men looking for that first spark of connection with a special lady may want to reconnect with their softer side: leisure interests like reading (6 percent of profiles) and casual activities like walking (9 percent), topped the list for women. The most commonly listed interests for men tended to be on the more high-intensity side, like sports (16 percent), working out (12 percent) and hiking (5 percent). Tinder's'swipe-based' dating app let's users post five of their interests on their profile -- and the researchers pulled this'interest' data from 13,941 women's and 9,229 men's Tinder profiles for the new study. Fortunately, more than a few of these interests were common to both men and women, leaving plenty of room for a more organic connection, based real interests.

Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to recommendations provided to the users at the wrong time. We focus on the domain of e-commerce, namely in identifying Shopping Product Questions (SPQs), where the user asking a product-related question may have an underlying shopping need. Identifying a user's shopping need allows voice assistants to enhance shopping experience by determining when to provide recommendations, such as product or deal recommendations, or proactive shopping actions recommendation. Identifying SPQs is a challenging problem and cannot be done from question text alone, and thus requires to infer latent user behavior patterns inferred from user's past shopping history. We propose features that capture the user's latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. Our evaluation shows that the proposed approach is able to identify SPQs with a high score of F1=0.91. Furthermore, based on an online evaluation with real voice assistant users, we identify SPQs in real-time and recommend shopping actions to users to add the queried product into their shopping list. We demonstrate that we are able to accurately identify SPQs, as indicated by the significantly higher rate of added products to users' shopping lists when being prompted after SPQs vs random PQs.

Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting, and enjoyable for CTA users is not trivial, especially for time-consuming or challenging tasks. Grounded in psychological theories of human interest, we propose to engage users with contextual and interesting statements or facts during interactions with a multi-modal CTA, to reduce fatigue and task abandonment before a task is complete. To operationalize this idea, we train a high-performing classifier (82% F1-score) to automatically identify relevant and interesting facts for users. We use it to create an annotated dataset of task-specific interesting facts for the domain of cooking. Finally, we design and validate a dialogue policy to incorporate the identified relevant and interesting facts into a conversation, to improve user engagement and task completion. Live testing on a leading multi-modal voice assistant shows that 66% of the presented facts were received positively, leading to a 40% gain in the user satisfaction rating, and a 37% increase in conversation length. These findings emphasize that strategically incorporating interesting facts into the CTA experience can promote real-world user participation for guided task interactions.

Substitute relationships are fundamental to people's daily lives across various domains. This study aims to comprehend and predict substitute relationships among products in diverse fields, extensively analyzing the application of machine learning algorithms, natural language processing, and other technologies. By comparing model methodologies across different domains, such as defining substitutes, representing and learning substitute relationships, and substitute reasoning, this study offers a methodological foundation for delving deeper into substitute relationships. Through ongoing research and innovation, we can further refine the personalization and accuracy of substitute recommendation systems, thus advancing the development and application of this field.

Epidemic outbreaks can cause critical health concerns and severe global economic crises. For countries or regions with new infectious disease outbreaks, it is essential to generate preventive strategies by learning lessons from others with similar risk profiles. A Strategy Transfer and Decision Support Approach (STDSA) is proposed based on the profile similarity evaluation. There are four steps in this method: (1) The similarity evaluation indicators are determined from three dimensions, i.e., the Basis of National Epidemic Prevention & Control, Social Resilience, and Infection Situation. (2) The data related to the indicators are collected and preprocessed. (3) The first round of screening on the preprocessed dataset is conducted through an improved collaborative filtering algorithm to calculate the preliminary similarity result from the perspective of the infection situation. (4) Finally, the K-Means model is used for the second round of screening to obtain the final similarity values. The approach will be applied to decision-making support in the context of COVID-19. Our results demonstrate that the recommendations generated by the STDSA model are more accurate and aligned better with the actual situation than those produced by pure K-means models. This study will provide new insights into preventing and controlling epidemics in regions that lack experience.

As a little girl, I often found myself in my family's basement, doing battle with a dragon. I wasn't gaming or playing pretend: My dragon was a piece of enterprise voice-dictation software called Dragon Naturally Speaking, launched in 1997 (and purchased by my dad, an early adopter). As a kid, I was enchanted by the idea of a computer that could type for you. The premise was simple: Wear a headset, pull up the software, and speak. Your words would fill a document on-screen without your hands having to bear the indignity of actually typing.