Healthcare: AI-powered tools and systems can help improve the accuracy and efficiency of medical diagnoses and treatment plans, which can improve patient outcomes and increase overall well-being. Personalization: AI-powered platforms can provide personalized recommendations for products, media, and services, which can help individuals discover new things that align with their interests and preferences, thus increasing their happiness. Social Connection: AI-powered chatbots and virtual assistants can facilitate communication and connection with others, especially for people who have difficulty interacting with others in person, such as elderly or disabled individuals. Productivity: AI-powered tools can help individuals and businesses increase efficiency and automate repetitive tasks, which can free up time and energy for more enjoyable and fulfilling activities. Entertainment: AI-powered personal assistants like Amazon Echo and Google Home can provide entertaining and informative content, play music, answer questions and control other smart home devices, giving users more control of their environment, and making their experience more pleasant.

Psychotherapist Dr. Jenn Mann discusses why dating apps are harmful to the dating process and how to remedy the situation on'The Ingraham Angle.' Rebel Wilson is one of the co-founders launching a new dating app called "Fluid," which she said is for people in the market for "love without labels" and is inspired by her own experience in relationships and dating. "This is the first dating app where you don't have to actually define yourself or tick a box to say'I'm straight, I'm gay, I'm bisexual,' and you don't have to describe what you are looking for," Wilson told People. The 42-year-old actress is in a relationship with girlfriend Ramona Agruma and is also a new mom to 3-month-old daughter Royce who was born via surrogate. "Fluid" is different from existing apps which "don't take into account this kind of movement of sexual fluidity," she said. WHY WAS 2020 REBEL WILSON'S'YEAR OF HEALTH?' INSIDE WILSON'S WEIGHT LOSS JOURNEY AND'PITCH PERFECT' MOVIES "I've been reading and studying a lot while doing this app," she told People.

Ten years ago, we published a post in the Netflix tech blog explaining our three-tier architectural approach to building recommender systems (see below). A lot has happened in the last 10 years in the recommender systems space for sure. That’s why, when a few months back I designed a Recsys course for Sphere, I thought it would be a great opportunity to revisit the blueprint.

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and model training in DRS. We point out that the existing AutoML-based recommender systems are developing to a multi-component joint search with abstract search space and efficient search algorithm. Finally, we discuss appealing research directions and summarize the survey.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.

Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.

We're in the middle of a global technological revolution, but dating apps have become relics of the status quo. Generative AI programs like ChatGPT offer a way to put the fun back into dating. These programs can provide relevant icebreakers while still maintaining intimacy in conversation. The people who are against using AI to help with dating are ignoring the fact that it can help accelerate the tedious initial process of online dating. For a decade, Americans have described dating apps as exhausting.

Pakyong, 13 Feb: You might not realize it right away, but artificial intelligence (AI) actually powers many of your phone's features. Your phone's technology is always working in the background, handling various duties, even while you are not using it. It examines how your phone is used to maximize battery life, helps you take clear photographs, recognizes music, aids with language translation, and much more. AI was previously only found in pricey devices that incorporated the most cutting-edge technology. However, since AI is now such a crucial component of mobile applications, chipmakers saw the need to create AI processors specifically for machine learning and deep learning activities to speed up processing. The most widely used voice assistants at the moment are Google Assistant, Siri, and Bixby, and you can use at least one of them on any smartphone.

We study the problem of multi-task learning under user-level differential privacy, in which $n$ users contribute data to $m$ tasks, each involving a subset of users. One important aspect of the problem, that can significantly impact quality, is the distribution skew among tasks. Certain tasks may have much fewer data samples than others, making them more susceptible to the noise added for privacy. It is natural to ask whether algorithms can adapt to this skew to improve the overall utility. We give a systematic analysis of the problem, by studying how to optimally allocate a user's privacy budget among tasks. We propose a generic algorithm, based on an adaptive reweighting of the empirical loss, and show that when there is task distribution skew, this gives a quantifiable improvement of excess empirical risk. Experimental studies on recommendation problems that exhibit a long tail of small tasks, demonstrate that our methods significantly improve utility, achieving the state of the art on two standard benchmarks.

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge. In this paper, we study CRS policy learning for cold-start users via meta-reinforcement learning. We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations. To facilitate fast policy adaptation, we design three synergetic components. Firstly, we design a meta-exploration policy dedicated to identifying user preferences via a few exploratory conversations, which accelerates personalized policy adaptation from the meta policy. Secondly, we adapt the item recommendation module for each user to maximize the recommendation quality based on the collected conversation states during conversations. Thirdly, we propose a Transformer-based state encoder as the backbone to connect the previous two components. It provides comprehensive state representations by modeling complicated relations between positive and negative feedback during the conversation. Extensive experiments on three datasets demonstrate the advantage of our solution in serving new users, compared with a rich set of state-of-the-art CRS solutions.